{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Build Earthquake Catalog\n", "\n", "In this final notebook, we read the matched-filter database, remove the multiple detections and write a clean earthquake catalog in a csv file." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "n_CPUs = 12\n", "os.environ[\"OMP_NUM_THREADS\"] = str(n_CPUs)\n", "\n", "import BPMF\n", "import glob\n", "import h5py as h5\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import sys\n", "\n", "from tqdm import tqdm\n", "from time import time as give_time\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# PROGRAM PARAMETERS\n", "NETWORK_FILENAME = \"network.csv\"\n", "TEMPLATE_DB = \"template_db\"\n", "MATCHED_FILTER_DB = \"matched_filter_db\"\n", "CHECK_SUMMARY_FILE = False\n", "PATH_MF = os.path.join(BPMF.cfg.OUTPUT_PATH, MATCHED_FILTER_DB)\n", "DATA_FOLDER = \"preprocessed_2_12\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# read network metadata\n", "net = BPMF.dataset.Network(NETWORK_FILENAME)\n", "net.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read the detected events' metadata for each template" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reading catalog: 100%|██████████| 7/7 [00:00<00:00, 13.37it/s]\n" ] } ], "source": [ "# template filenames\n", "template_filenames = glob.glob(os.path.join(BPMF.cfg.OUTPUT_PATH, TEMPLATE_DB, \"template*\"))\n", "template_filenames.sort()\n", "\n", "# initialize the template group\n", "template_group = BPMF.dataset.TemplateGroup.read_from_files(template_filenames, net)\n", "template_group.read_catalog(\n", " extra_attributes=[\"cc\"],\n", " progress=True,\n", " db_path=PATH_MF,\n", " check_summary_file=CHECK_SUMMARY_FILE,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `BPMF.dataset.TemplateGroup` now has a `catalog` attribute, which is a `BPMF.dataset.Catalog` instance." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "template_group.catalog" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " longitude latitude depth origin_time cc \\\n", "event_id \n", "1.0 30.411914 40.793125 11.304688 2012-07-07 06:56:02.200 0.432075 \n", "3.0 30.445117 40.764375 8.054688 2012-07-07 06:56:02.280 0.289950 \n", "5.0 30.397754 40.765937 10.644531 2012-07-07 06:56:02.520 0.331015 \n", "2.0 30.404590 40.762187 10.136719 2012-07-07 06:56:02.560 0.404524 \n", "0.0 30.404590 40.762187 10.136719 2012-07-07 06:56:02.560 0.404524 \n", "... ... ... ... ... ... \n", "2.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 0.167869 \n", "0.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 0.167869 \n", "6.0 30.294482 40.628281 -1.517578 2012-07-07 15:26:15.080 1.000000 \n", "3.14 30.445117 40.764375 8.054688 2012-07-07 16:50:34.840 0.255224 \n", "1.31 30.411914 40.793125 11.304688 2012-07-07 19:23:20.560 0.146581 \n", "\n", " tid \n", "event_id \n", "1.0 1 \n", "3.0 3 \n", "5.0 5 \n", "2.0 2 \n", "0.0 0 \n", "... ... \n", "2.33 2 \n", "0.33 0 \n", "6.0 6 \n", "3.14 3 \n", "1.31 1 \n", "\n", "[148 rows x 6 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "template_group.catalog.catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Remove the multiple detections\n", "\n", "Remove multiple detections with the `TemplateGroup.remove_multiples` method." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# DISTANCE_CRITERION_KM: Distance, in km, between two detected events (within uncertainties) below which \n", "# detected events are investigated for equality.\n", "DISTANCE_CRITERION_KM = 15.0\n", "# DT_CRITERION_SEC: Inter-event time, in seconds, between two detected events below which\n", "# detected events are investigated for redundancy.\n", "DT_CRITERION_SEC = 4.0\n", "# SIMILARITY_CRITERION: Inter-template correlation coefficient below which detected events are investigated for equality.\n", "SIMILARITY_CRITERION = 0.10\n", "# N_CLOSEST_STATIONS: When computing the inter-template correlation coefficient, use the N_CLOSEST_STATIONS closest stations\n", "# of a given pair of templates. This parameter is relevant for studies with large seismic networks.\n", "N_CLOSEST_STATIONS = 10" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# we need to read the waveforms first\n", "template_group.read_waveforms()\n", "template_group.normalize(method=\"rms\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ebeauce/miniconda3/envs/hy7_py310/lib/python3.10/site-packages/BPMF/dataset.py:3452: RuntimeWarning: invalid value encountered in divide\n", " unit_direction /= np.sqrt(np.sum(unit_direction**2, axis=1))[\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Computing the similarity matrix...\n", "Computing the inter-template directional errors...\n", "Searching for events detected by multiple templates\n", "All events occurring within 4.0 sec, with uncertainty ellipsoids closer than 15.0 km will and inter-template CC larger than 0.10 be considered the same\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Removing multiples: 100%|██████████| 148/148 [00:00<00:00, 2651.52it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.06s to flag the multiples\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "template_group.remove_multiples(\n", " n_closest_stations=N_CLOSEST_STATIONS,\n", " dt_criterion=DT_CRITERION_SEC,\n", " distance_criterion=DISTANCE_CRITERION_KM,\n", " similarity_criterion=SIMILARITY_CRITERION,\n", " progress=True,\n", ")\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The catalog now has three new columns: `origin_time_sec` (a timestamp of `origin_time` in seconds), `interevent_time_sec` (template-wise computation), `unique_event`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " longitude latitude depth origin_time cc \\\n", "event_id \n", "1.0 30.411914 40.793125 11.304688 2012-07-07 06:56:02.200 0.432075 \n", "3.0 30.445117 40.764375 8.054688 2012-07-07 06:56:02.280 0.289950 \n", "5.0 30.397754 40.765937 10.644531 2012-07-07 06:56:02.520 0.331015 \n", "2.0 30.404590 40.762187 10.136719 2012-07-07 06:56:02.560 0.404524 \n", "0.0 30.404590 40.762187 10.136719 2012-07-07 06:56:02.560 0.404524 \n", "... ... ... ... ... ... \n", "2.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 0.167869 \n", "0.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 0.167869 \n", "6.0 30.294482 40.628281 -1.517578 2012-07-07 15:26:15.080 1.000000 \n", "3.14 30.445117 40.764375 8.054688 2012-07-07 16:50:34.840 0.255224 \n", "1.31 30.411914 40.793125 11.304688 2012-07-07 19:23:20.560 0.146581 \n", "\n", " tid origin_time_sec interevent_time_sec unique_event \n", "event_id \n", "1.0 1 1.341644e+09 0.00 True \n", "3.0 3 1.341644e+09 0.08 False \n", "5.0 5 1.341644e+09 0.24 False \n", "2.0 2 1.341644e+09 0.04 False \n", "0.0 0 1.341644e+09 0.00 False \n", "... ... ... ... ... \n", "2.33 2 1.341663e+09 245.44 True \n", "0.33 0 1.341663e+09 0.00 False \n", "6.0 6 1.341675e+09 11309.96 True \n", "3.14 3 1.341680e+09 5059.76 True \n", "1.31 1 1.341689e+09 9165.72 True \n", "\n", "[148 rows x 9 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "template_group.catalog.catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final catalog is made of the unique events only." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "template_group.catalog.catalog = template_group.catalog.catalog[template_group.catalog.catalog[\"unique_event\"]]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Let's add the location uncertainties from the template events." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_160176/1634894027.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " template_group.catalog.catalog.loc[selection, \"hmax_unc\"] = tp.hmax_unc\n", "/tmp/ipykernel_160176/1634894027.py:5: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " template_group.catalog.catalog.loc[selection, \"hmin_unc\"] = tp.hmin_unc\n", "/tmp/ipykernel_160176/1634894027.py:6: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " template_group.catalog.catalog.loc[selection, \"az_hmax_unc\"] = tp.az_hmax_unc\n", "/tmp/ipykernel_160176/1634894027.py:7: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " template_group.catalog.catalog.loc[selection, \"vmax_unc\"] = tp.vmax_unc\n" ] } ], "source": [ "for tp in template_group.templates:\n", " tid = tp.tid\n", " selection = template_group.catalog.catalog[\"tid\"] == tid\n", " template_group.catalog.catalog.loc[selection, \"hmax_unc\"] = tp.hmax_unc\n", " template_group.catalog.catalog.loc[selection, \"hmin_unc\"] = tp.hmin_unc\n", " template_group.catalog.catalog.loc[selection, \"az_hmax_unc\"] = tp.az_hmax_unc\n", " template_group.catalog.catalog.loc[selection, \"vmax_unc\"] = tp.vmax_unc" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 52 events in our template matching catalog!\n" ] }, { "data": { "text/html": [ "
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longitudelatitudedepthorigin_timecctidorigin_time_secinterevent_time_secunique_eventhmax_unchmin_uncaz_hmax_uncvmax_unc
event_id
1.030.41191440.79312511.3046882012-07-07 06:56:02.2000.43207511.341644e+090.00True1.8866751.435720-163.0983881.737751
3.130.44511740.7643758.0546882012-07-07 06:56:52.1600.24627231.341644e+090.04True2.9823861.933982-169.5562508.074210
1.230.41191440.79312511.3046882012-07-07 07:07:45.4400.40337611.341645e+09652.96True1.8866751.435720-163.0983881.737751
3.330.44511740.7643758.0546882012-07-07 07:09:59.6800.23929631.341645e+09133.84True2.9823861.933982-169.5562508.074210
0.330.40459040.76218710.1367192012-07-07 07:10:12.4400.25779301.341645e+090.36True1.2040780.954942138.5200751.142778
5.330.39775440.76593710.6445312012-07-07 07:10:20.0000.40218751.341645e+097.56True1.4770161.028668137.0145241.263067
5.430.39775440.76593710.6445312012-07-07 07:10:39.1600.37711251.341645e+0919.12True1.4770161.028668137.0145241.263067
1.430.41191440.79312511.3046882012-07-07 07:10:53.6400.39440011.341645e+0913.60True1.8866751.435720-163.0983881.737751
3.430.44511740.7643758.0546882012-07-07 07:11:07.2800.68114331.341645e+090.08True2.9823861.933982-169.5562508.074210
5.630.39775440.76593710.6445312012-07-07 07:11:36.0800.18118251.341645e+0928.52True1.4770161.028668137.0145241.263067
0.830.40459040.76218710.1367192012-07-07 07:12:06.2001.00000001.341645e+090.04True1.2040780.954942138.5200751.142778
1.730.41191440.79312511.3046882012-07-07 07:14:25.0001.00000011.341645e+09138.80True1.8866751.435720-163.0983881.737751
1.830.41191440.79312511.3046882012-07-07 07:15:44.8800.18390711.341645e+0979.52True1.8866751.435720-163.0983881.737751
5.930.39775440.76593710.6445312012-07-07 07:18:17.0400.39826851.341645e+09152.16True1.4770161.028668137.0145241.263067
0.1130.40459040.76218710.1367192012-07-07 07:22:16.2400.17847101.341646e+09239.16True1.2040780.954942138.5200751.142778
0.1230.40459040.76218710.1367192012-07-07 07:22:43.8800.16965601.341646e+0927.64True1.2040780.954942138.5200751.142778
3.630.44511740.7643758.0546882012-07-07 07:23:08.3200.49105631.341646e+0924.44True2.9823861.933982-169.5562508.074210
1.930.41191440.79312511.3046882012-07-07 07:24:09.5600.18880011.341646e+0960.96True1.8866751.435720-163.0983881.737751
5.1130.39775440.76593710.6445312012-07-07 07:24:34.0001.00000051.341646e+090.24True1.4770161.028668137.0145241.263067
1.1130.41191440.79312511.3046882012-07-07 07:26:09.9200.21158711.341646e+0995.88True1.8866751.435720-163.0983881.737751
1.1230.41191440.79312511.3046882012-07-07 07:27:21.5600.24467011.341646e+0971.64True1.8866751.435720-163.0983881.737751
5.1330.39775440.76593710.6445312012-07-07 07:29:48.9200.21012151.341646e+09147.00True1.4770161.028668137.0145241.263067
3.830.44511740.7643758.0546882012-07-07 07:34:42.6000.42489731.341646e+090.04True2.9823861.933982-169.5562508.074210
3.930.44511740.7643758.0546882012-07-07 07:35:58.0000.26098231.341647e+0975.08True2.9823861.933982-169.5562508.074210
3.1030.44511740.7643758.0546882012-07-07 07:39:17.4000.20231231.341647e+09199.16True2.9823861.933982-169.5562508.074210
3.1130.44511740.7643758.0546882012-07-07 08:00:23.3600.19935031.341648e+091265.96True2.9823861.933982-169.5562508.074210
1.1430.41191440.79312511.3046882012-07-07 08:10:46.5600.15898611.341649e+09623.20True1.8866751.435720-163.0983881.737751
0.1730.40459040.76218710.1367192012-07-07 08:15:48.8800.24627701.341649e+090.04True1.2040780.954942138.5200751.142778
0.1830.40459040.76218710.1367192012-07-07 08:17:35.0000.56385001.341649e+090.04True1.2040780.954942138.5200751.142778
1.1730.41191440.79312511.3046882012-07-07 08:30:27.2400.21819111.341650e+09772.24True1.8866751.435720-163.0983881.737751
2.1930.40459040.76218710.1367192012-07-07 08:41:19.5600.30481321.341650e+090.08True1.2040780.954942138.5200751.142778
1.1930.41191440.79312511.3046882012-07-07 08:45:27.6000.22518911.341651e+09248.04True1.8866751.435720-163.0983881.737751
2.2130.40459040.76218710.1367192012-07-07 08:46:34.3600.32100521.341651e+090.08True1.2040780.954942138.5200751.142778
1.2130.41191440.79312511.3046882012-07-07 08:47:00.5600.40717011.341651e+0926.20True1.8866751.435720-163.0983881.737751
3.1230.44511740.7643758.0546882012-07-07 08:48:43.8001.00000031.341651e+090.08True2.9823861.933982-169.5562508.074210
5.2130.39775440.76593710.6445312012-07-07 09:10:00.6000.39023651.341652e+090.28True1.4770161.028668137.0145241.263067
5.2230.39775440.76593710.6445312012-07-07 09:10:55.6400.15276651.341652e+0954.96True1.4770161.028668137.0145241.263067
1.2430.41191440.79312511.3046882012-07-07 09:20:12.0800.69393311.341653e+09556.44True1.8866751.435720-163.0983881.737751
5.2430.39775440.76593710.6445312012-07-07 09:27:10.1200.33232851.341653e+09417.68True1.4770161.028668137.0145241.263067
1.2530.41191440.79312511.3046882012-07-07 09:42:13.4400.20687611.341654e+09903.28True1.8866751.435720-163.0983881.737751
2.2730.40459040.76218710.1367192012-07-07 09:46:04.9200.34143521.341654e+090.08True1.2040780.954942138.5200751.142778
5.2630.39775440.76593710.6445312012-07-07 09:59:27.2400.18676751.341655e+09802.32True1.4770161.028668137.0145241.263067
1.2730.41191440.79312511.3046882012-07-07 10:05:20.6800.15930511.341656e+09353.36True1.8866751.435720-163.0983881.737751
4.030.31669940.7546880.2851562012-07-07 10:16:39.8001.00000041.341656e+09679.12True3.3787621.691182-143.7723402.733743
5.2730.39775440.76593710.6445312012-07-07 10:41:34.4000.33580451.341658e+090.28True1.4770161.028668137.0145241.263067
0.3030.40459040.76218710.1367192012-07-07 11:15:32.6400.29878001.341660e+090.08True1.2040780.954942138.5200751.142778
0.3130.40459040.76218710.1367192012-07-07 11:23:41.5200.38565001.341660e+090.04True1.2040780.954942138.5200751.142778
1.3030.41191440.79312511.3046882012-07-07 12:13:39.3200.26658211.341663e+092997.80True1.8866751.435720-163.0983881.737751
2.3330.40459040.76218710.1367192012-07-07 12:17:45.1200.16786921.341663e+09245.44True1.2040780.954942138.5200751.142778
6.030.29448240.628281-1.5175782012-07-07 15:26:15.0801.00000061.341675e+0911309.96True4.8982082.005268177.7088183.423410
3.1430.44511740.7643758.0546882012-07-07 16:50:34.8400.25522431.341680e+095059.76True2.9823861.933982-169.5562508.074210
1.3130.41191440.79312511.3046882012-07-07 19:23:20.5600.14658111.341689e+099165.72True1.8866751.435720-163.0983881.737751
\n", "
" ], "text/plain": [ " longitude latitude depth origin_time cc \\\n", "event_id \n", "1.0 30.411914 40.793125 11.304688 2012-07-07 06:56:02.200 0.432075 \n", "3.1 30.445117 40.764375 8.054688 2012-07-07 06:56:52.160 0.246272 \n", "1.2 30.411914 40.793125 11.304688 2012-07-07 07:07:45.440 0.403376 \n", "3.3 30.445117 40.764375 8.054688 2012-07-07 07:09:59.680 0.239296 \n", "0.3 30.404590 40.762187 10.136719 2012-07-07 07:10:12.440 0.257793 \n", "5.3 30.397754 40.765937 10.644531 2012-07-07 07:10:20.000 0.402187 \n", "5.4 30.397754 40.765937 10.644531 2012-07-07 07:10:39.160 0.377112 \n", "1.4 30.411914 40.793125 11.304688 2012-07-07 07:10:53.640 0.394400 \n", "3.4 30.445117 40.764375 8.054688 2012-07-07 07:11:07.280 0.681143 \n", "5.6 30.397754 40.765937 10.644531 2012-07-07 07:11:36.080 0.181182 \n", "0.8 30.404590 40.762187 10.136719 2012-07-07 07:12:06.200 1.000000 \n", "1.7 30.411914 40.793125 11.304688 2012-07-07 07:14:25.000 1.000000 \n", "1.8 30.411914 40.793125 11.304688 2012-07-07 07:15:44.880 0.183907 \n", "5.9 30.397754 40.765937 10.644531 2012-07-07 07:18:17.040 0.398268 \n", "0.11 30.404590 40.762187 10.136719 2012-07-07 07:22:16.240 0.178471 \n", "0.12 30.404590 40.762187 10.136719 2012-07-07 07:22:43.880 0.169656 \n", "3.6 30.445117 40.764375 8.054688 2012-07-07 07:23:08.320 0.491056 \n", "1.9 30.411914 40.793125 11.304688 2012-07-07 07:24:09.560 0.188800 \n", "5.11 30.397754 40.765937 10.644531 2012-07-07 07:24:34.000 1.000000 \n", "1.11 30.411914 40.793125 11.304688 2012-07-07 07:26:09.920 0.211587 \n", "1.12 30.411914 40.793125 11.304688 2012-07-07 07:27:21.560 0.244670 \n", "5.13 30.397754 40.765937 10.644531 2012-07-07 07:29:48.920 0.210121 \n", "3.8 30.445117 40.764375 8.054688 2012-07-07 07:34:42.600 0.424897 \n", "3.9 30.445117 40.764375 8.054688 2012-07-07 07:35:58.000 0.260982 \n", "3.10 30.445117 40.764375 8.054688 2012-07-07 07:39:17.400 0.202312 \n", "3.11 30.445117 40.764375 8.054688 2012-07-07 08:00:23.360 0.199350 \n", "1.14 30.411914 40.793125 11.304688 2012-07-07 08:10:46.560 0.158986 \n", "0.17 30.404590 40.762187 10.136719 2012-07-07 08:15:48.880 0.246277 \n", "0.18 30.404590 40.762187 10.136719 2012-07-07 08:17:35.000 0.563850 \n", "1.17 30.411914 40.793125 11.304688 2012-07-07 08:30:27.240 0.218191 \n", "2.19 30.404590 40.762187 10.136719 2012-07-07 08:41:19.560 0.304813 \n", "1.19 30.411914 40.793125 11.304688 2012-07-07 08:45:27.600 0.225189 \n", "2.21 30.404590 40.762187 10.136719 2012-07-07 08:46:34.360 0.321005 \n", "1.21 30.411914 40.793125 11.304688 2012-07-07 08:47:00.560 0.407170 \n", "3.12 30.445117 40.764375 8.054688 2012-07-07 08:48:43.800 1.000000 \n", "5.21 30.397754 40.765937 10.644531 2012-07-07 09:10:00.600 0.390236 \n", "5.22 30.397754 40.765937 10.644531 2012-07-07 09:10:55.640 0.152766 \n", "1.24 30.411914 40.793125 11.304688 2012-07-07 09:20:12.080 0.693933 \n", "5.24 30.397754 40.765937 10.644531 2012-07-07 09:27:10.120 0.332328 \n", "1.25 30.411914 40.793125 11.304688 2012-07-07 09:42:13.440 0.206876 \n", "2.27 30.404590 40.762187 10.136719 2012-07-07 09:46:04.920 0.341435 \n", "5.26 30.397754 40.765937 10.644531 2012-07-07 09:59:27.240 0.186767 \n", "1.27 30.411914 40.793125 11.304688 2012-07-07 10:05:20.680 0.159305 \n", "4.0 30.316699 40.754688 0.285156 2012-07-07 10:16:39.800 1.000000 \n", "5.27 30.397754 40.765937 10.644531 2012-07-07 10:41:34.400 0.335804 \n", "0.30 30.404590 40.762187 10.136719 2012-07-07 11:15:32.640 0.298780 \n", "0.31 30.404590 40.762187 10.136719 2012-07-07 11:23:41.520 0.385650 \n", "1.30 30.411914 40.793125 11.304688 2012-07-07 12:13:39.320 0.266582 \n", "2.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 0.167869 \n", "6.0 30.294482 40.628281 -1.517578 2012-07-07 15:26:15.080 1.000000 \n", "3.14 30.445117 40.764375 8.054688 2012-07-07 16:50:34.840 0.255224 \n", "1.31 30.411914 40.793125 11.304688 2012-07-07 19:23:20.560 0.146581 \n", "\n", " tid origin_time_sec interevent_time_sec unique_event hmax_unc \\\n", "event_id \n", "1.0 1 1.341644e+09 0.00 True 1.886675 \n", "3.1 3 1.341644e+09 0.04 True 2.982386 \n", "1.2 1 1.341645e+09 652.96 True 1.886675 \n", "3.3 3 1.341645e+09 133.84 True 2.982386 \n", "0.3 0 1.341645e+09 0.36 True 1.204078 \n", "5.3 5 1.341645e+09 7.56 True 1.477016 \n", "5.4 5 1.341645e+09 19.12 True 1.477016 \n", "1.4 1 1.341645e+09 13.60 True 1.886675 \n", "3.4 3 1.341645e+09 0.08 True 2.982386 \n", "5.6 5 1.341645e+09 28.52 True 1.477016 \n", "0.8 0 1.341645e+09 0.04 True 1.204078 \n", "1.7 1 1.341645e+09 138.80 True 1.886675 \n", "1.8 1 1.341645e+09 79.52 True 1.886675 \n", "5.9 5 1.341645e+09 152.16 True 1.477016 \n", "0.11 0 1.341646e+09 239.16 True 1.204078 \n", "0.12 0 1.341646e+09 27.64 True 1.204078 \n", "3.6 3 1.341646e+09 24.44 True 2.982386 \n", "1.9 1 1.341646e+09 60.96 True 1.886675 \n", "5.11 5 1.341646e+09 0.24 True 1.477016 \n", "1.11 1 1.341646e+09 95.88 True 1.886675 \n", "1.12 1 1.341646e+09 71.64 True 1.886675 \n", "5.13 5 1.341646e+09 147.00 True 1.477016 \n", "3.8 3 1.341646e+09 0.04 True 2.982386 \n", "3.9 3 1.341647e+09 75.08 True 2.982386 \n", "3.10 3 1.341647e+09 199.16 True 2.982386 \n", "3.11 3 1.341648e+09 1265.96 True 2.982386 \n", "1.14 1 1.341649e+09 623.20 True 1.886675 \n", "0.17 0 1.341649e+09 0.04 True 1.204078 \n", "0.18 0 1.341649e+09 0.04 True 1.204078 \n", "1.17 1 1.341650e+09 772.24 True 1.886675 \n", "2.19 2 1.341650e+09 0.08 True 1.204078 \n", "1.19 1 1.341651e+09 248.04 True 1.886675 \n", "2.21 2 1.341651e+09 0.08 True 1.204078 \n", "1.21 1 1.341651e+09 26.20 True 1.886675 \n", "3.12 3 1.341651e+09 0.08 True 2.982386 \n", "5.21 5 1.341652e+09 0.28 True 1.477016 \n", "5.22 5 1.341652e+09 54.96 True 1.477016 \n", "1.24 1 1.341653e+09 556.44 True 1.886675 \n", "5.24 5 1.341653e+09 417.68 True 1.477016 \n", "1.25 1 1.341654e+09 903.28 True 1.886675 \n", "2.27 2 1.341654e+09 0.08 True 1.204078 \n", "5.26 5 1.341655e+09 802.32 True 1.477016 \n", "1.27 1 1.341656e+09 353.36 True 1.886675 \n", "4.0 4 1.341656e+09 679.12 True 3.378762 \n", "5.27 5 1.341658e+09 0.28 True 1.477016 \n", "0.30 0 1.341660e+09 0.08 True 1.204078 \n", "0.31 0 1.341660e+09 0.04 True 1.204078 \n", "1.30 1 1.341663e+09 2997.80 True 1.886675 \n", "2.33 2 1.341663e+09 245.44 True 1.204078 \n", "6.0 6 1.341675e+09 11309.96 True 4.898208 \n", "3.14 3 1.341680e+09 5059.76 True 2.982386 \n", "1.31 1 1.341689e+09 9165.72 True 1.886675 \n", "\n", " hmin_unc az_hmax_unc vmax_unc \n", "event_id \n", "1.0 1.435720 -163.098388 1.737751 \n", "3.1 1.933982 -169.556250 8.074210 \n", "1.2 1.435720 -163.098388 1.737751 \n", "3.3 1.933982 -169.556250 8.074210 \n", "0.3 0.954942 138.520075 1.142778 \n", "5.3 1.028668 137.014524 1.263067 \n", "5.4 1.028668 137.014524 1.263067 \n", "1.4 1.435720 -163.098388 1.737751 \n", "3.4 1.933982 -169.556250 8.074210 \n", "5.6 1.028668 137.014524 1.263067 \n", "0.8 0.954942 138.520075 1.142778 \n", "1.7 1.435720 -163.098388 1.737751 \n", "1.8 1.435720 -163.098388 1.737751 \n", "5.9 1.028668 137.014524 1.263067 \n", "0.11 0.954942 138.520075 1.142778 \n", "0.12 0.954942 138.520075 1.142778 \n", "3.6 1.933982 -169.556250 8.074210 \n", "1.9 1.435720 -163.098388 1.737751 \n", "5.11 1.028668 137.014524 1.263067 \n", "1.11 1.435720 -163.098388 1.737751 \n", "1.12 1.435720 -163.098388 1.737751 \n", "5.13 1.028668 137.014524 1.263067 \n", "3.8 1.933982 -169.556250 8.074210 \n", "3.9 1.933982 -169.556250 8.074210 \n", "3.10 1.933982 -169.556250 8.074210 \n", "3.11 1.933982 -169.556250 8.074210 \n", "1.14 1.435720 -163.098388 1.737751 \n", "0.17 0.954942 138.520075 1.142778 \n", "0.18 0.954942 138.520075 1.142778 \n", "1.17 1.435720 -163.098388 1.737751 \n", "2.19 0.954942 138.520075 1.142778 \n", "1.19 1.435720 -163.098388 1.737751 \n", "2.21 0.954942 138.520075 1.142778 \n", "1.21 1.435720 -163.098388 1.737751 \n", "3.12 1.933982 -169.556250 8.074210 \n", "5.21 1.028668 137.014524 1.263067 \n", "5.22 1.028668 137.014524 1.263067 \n", "1.24 1.435720 -163.098388 1.737751 \n", "5.24 1.028668 137.014524 1.263067 \n", "1.25 1.435720 -163.098388 1.737751 \n", "2.27 0.954942 138.520075 1.142778 \n", "5.26 1.028668 137.014524 1.263067 \n", "1.27 1.435720 -163.098388 1.737751 \n", "4.0 1.691182 -143.772340 2.733743 \n", "5.27 1.028668 137.014524 1.263067 \n", "0.30 0.954942 138.520075 1.142778 \n", "0.31 0.954942 138.520075 1.142778 \n", "1.30 1.435720 -163.098388 1.737751 \n", "2.33 0.954942 138.520075 1.142778 \n", "6.0 2.005268 177.708818 3.423410 \n", "3.14 1.933982 -169.556250 8.074210 \n", "1.31 1.435720 -163.098388 1.737751 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(f\"There are {len(template_group.catalog.catalog)} events in our template matching catalog!\")\n", "template_group.catalog.catalog" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot these events on a map. You will see that there are far fewer dots on the map than the total number of earthquakes in our catalog... This is because all newly detected events are attributed their template locations. Therefore, most events are plotted at the exact same location." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = template_group.catalog.plot_map(\n", " figsize=(10, 10), network=net, s=50, markersize_station=50, lat_margin=0.02, plot_uncertainties=False\n", " )\n", "ax = fig.get_axes()[0]\n", "ax.set_facecolor(\"dimgrey\")\n", "ax.patch.set_alpha(0.15)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus: Relocate each events\n", "\n", "In your workflow, you might be ok with the approximate locations of the template matching catalog. However, if you decide to keep refining the catalog, here are some suggestions to get started.\n", "\n", "One possibility to start refining the locations of all events is to re-run the same process as in notebook 6, namely `PhaseNet` and `NonLinLoc`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-03-08 16:11:21.120889: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.7/lib64\n", "2023-03-08 16:11:21.120935: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "from BPMF.data_reader_examples import data_reader_mseed\n", "\n", "# this is necessary to limit the number of threads spawn by tf\n", "os.environ[\"TF_NUM_INTRAOP_THREADS\"] = str(n_CPUs)\n", "os.environ[\"TF_NUM_INTEROP_THREADS\"] = str(n_CPUs)\n", "tf.config.threading.set_inter_op_parallelism_threads(n_CPUs)\n", "tf.config.threading.set_intra_op_parallelism_threads(n_CPUs)\n", "tf.config.set_soft_device_placement(True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# PhaseNet picking parameters\n", "\n", "# PhaseNet was trained for 100Hz data. Even if we saw that running PhaseNet on 25Hz data\n", "# was good for backprojection, here, picking benefits from running PhaseNet on 100Hz data.\n", "# Thus, we will upsample the waveforms before running PhaseNet.\n", "PHASENET_SAMPLING_RATE_HZ = 100.\n", "UPSAMPLING_BEFORE_PN_RELOCATION = int(PHASENET_SAMPLING_RATE_HZ/BPMF.cfg.SAMPLING_RATE_HZ)\n", "DOWNSAMPLING_BEFORE_PN_RELOCATION = 1\n", "\n", "# DURATION_SEC: the duration, in seconds, of the data stream starting at the detection time\n", "# defined by Event.origin_time. This data stream is used for picking the P/S waves.\n", "DURATION_SEC = 60.0\n", "# THRESHOLD_P: probability of P-wave arrival above which we declare a pick. If several picks are\n", "# declared during the DURATION_SEC data stream, we only keep the best one. We can\n", "# afford using a low probability threshold since we already know with some confidence\n", "# that an earthquake is in the data stream.\n", "THRESHOLD_P = 0.10\n", "# THRESHOLD_S: probability of S-wave arrival above which we declare a pick.\n", "THRESHOLD_S = 0.10\n", "# DATA_FOLDER: name of the folder where the waveforms we want to use for picking are stored\n", "DATA_FOLDER = \"preprocessed_2_12\"\n", "# COMPONENT_ALIASES: A dictionary that defines the possible channel names to search for\n", "# for example, the seismometer might not be oriented and the horizontal channels\n", "# might be named 1 and 2, in which case we arbitrarily decide to take 1 as the \"N\" channel\n", "# and 2 as the \"E\" channel. This doesn't matter for picking P- and S-wave arrival times.\n", "COMPONENT_ALIASES = {\"N\": [\"N\", \"1\"], \"E\": [\"E\", \"2\"], \"Z\": [\"Z\"]}\n", "# PHASE_ON_COMP: dictionary defining which moveout we use to extract the waveform\n", "PHASE_ON_COMP = {\"N\": \"S\", \"1\": \"S\", \"E\": \"S\", \"2\": \"S\", \"Z\": \"P\"}\n", "# USE_APRIORI_PICKS: boolean. This option is IMPORTANT when running BPMF in HIGH SEISMICITY CONTEXTS, like\n", "# during the aftershock sequence of a large earthquake. If there are many events happening\n", "# close to each other in time, we need to guide PhaseNet to pick the right set of picks.\n", "# For that, we use the predicted P- and S-wave times from backprojection to add extra weight to\n", "# the picks closer to those times and make it more likely to identify them as the \"best\" picks.\n", "# WARNING: If there are truly many events, even this trick might fail. It's because \"phase association\"\n", "# is an intrinsically hard problem in this case, and the picking might be hard to do automatically.\n", "USE_APRIORI_PICKS = True\n", "\n", "# MAX_HORIZONTAL_UNC_KM: Horizontal location uncertainty, in km, above which we keep the template location\n", "MAX_HORIZONTAL_UNC_KM = 10.\n", "\n", "# location parameters\n", "\n", "LOCATION_ROUTINE = \"NLLoc\"\n", "# NLLOC_METHOD: string that defines what loss function is used by NLLoc, see http://alomax.free.fr/nlloc/ for more info.\n", "# Using some flavor of 'EDT' is important to obtain robust locations that are not sensitive to pick outliers.\n", "NLLOC_METHOD = \"EDT\"\n", "# MINIMUM_NUM_STATIONS_W_PICKS: minimum number of stations with picks to even try relocation.\n", "MINIMUM_NUM_STATIONS_W_PICKS = 3\n", "# we set a maximum tolerable difference, in percentage, between the picked time and the predicted travel time\n", "MAX_TIME_DIFFERENT_PICKS_PREDICTED_PERCENT = 10" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2023-03-08 16:11:25.822008: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.7/lib64\n", "2023-03-08 16:11:25.822047: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", "2023-03-08 16:11:25.822072: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (hypo-7): /proc/driver/nvidia/version does not exist\n", "2023-03-08 16:11:25.822308: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2023-03-08 16:11:26.534948: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n", "Pred: 100%|██████████| 1/1 [00:00<00:00, 4.32it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Pred: 100%|██████████| 1/1 [00:00<00:00, 5.37it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Pred: 100%|██████████| 1/1 [00:00<00:00, 4.79it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 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"n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Pred: 100%|██████████| 1/1 [00:00<00:00, 5.44it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Pred: 100%|██████████| 1/1 [00:00<00:00, 5.15it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Pred: 100%|██████████| 1/1 [00:00<00:00, 5.65it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "n events: 1, n stations: 8, batch size (n events x n stations): 8\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Pred: 100%|██████████| 1/1 [00:00<00:00, 5.49it/s]\n" ] } ], "source": [ "events = {}\n", "for idx, row in template_group.catalog.catalog.iterrows():\n", " tid, evidx = row.name.split(\".\")\n", " # get the template instance from template_group\n", " template = template_group.templates[template_group.tindexes.loc[int(tid)]]\n", " # this is the filename of the database where template tid's detected events were stored\n", " detection_db_filename = f\"detections_template{tid}.h5\"\n", " db_path = os.path.join(BPMF.cfg.OUTPUT_PATH, MATCHED_FILTER_DB)\n", " with h5.File(os.path.join(db_path, detection_db_filename), mode=\"r\") as fdet:\n", " keys = list(fdet.keys())\n", " event = BPMF.dataset.Event.read_from_file(\n", " hdf5_file=fdet[keys[int(evidx)]], data_reader=data_reader_mseed\n", " )\n", " # # attach data reader this way (note: conflict with data_reader argument in phasenet's wrapper module)\n", " # event.data_reader = data_reader_mseed\n", " # pick P-/S-wave arrivals\n", " event.pick_PS_phases(\n", " DURATION_SEC,\n", " phase_on_comp=PHASE_ON_COMP,\n", " threshold_P=THRESHOLD_P,\n", " threshold_S=THRESHOLD_S,\n", " component_aliases=COMPONENT_ALIASES,\n", " inter_op_parallelism_threads=n_CPUs,\n", " intra_op_parallelism_threads=n_CPUs,\n", " data_folder=DATA_FOLDER,\n", " upsampling=UPSAMPLING_BEFORE_PN_RELOCATION,\n", " downsampling=DOWNSAMPLING_BEFORE_PN_RELOCATION,\n", " use_apriori_picks=USE_APRIORI_PICKS,\n", " )\n", " \n", " if len(event.picks.dropna(how=\"all\")) >= MINIMUM_NUM_STATIONS_W_PICKS:\n", " # first relocation, insensitive to outliers\n", " event.relocate(\n", " stations=net.stations, routine=LOCATION_ROUTINE, method=NLLOC_METHOD,\n", " ) \n", " if \"NLLoc_reloc\" in event.aux_data:\n", " # this variable was inserted into ev.aux_data if NLLoc successfully located the event\n", " # use predicted times to remove outlier picks\n", " event.remove_outlier_picks(max_diff_percent=MAX_TIME_DIFFERENT_PICKS_PREDICTED_PERCENT)\n", " if len(event.picks.dropna(how=\"all\")) >= MINIMUM_NUM_STATIONS_W_PICKS:\n", " # first relocation, insensitive to outliers\n", " event.relocate(\n", " stations=net.stations, routine=LOCATION_ROUTINE, method=NLLOC_METHOD,\n", " )\n", " else:\n", " del event.aux_data[\"NLLoc_reloc\"]\n", " events[row.name] = event\n", " if (\"NLLoc_reloc\" in event.aux_data) and (event.hmax_unc) < MAX_HORIZONTAL_UNC_KM:\n", " template_group.catalog.catalog.loc[row.name, \"longitude\"] = event.longitude\n", " template_group.catalog.catalog.loc[row.name, \"latitude\"] = event.latitude\n", " template_group.catalog.catalog.loc[row.name, \"depth\"] = event.depth" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = template_group.catalog.plot_map(\n", " figsize=(10, 10), network=net, s=50, markersize_station=50, lat_margin=0.02, plot_uncertainties=False\n", " )\n", "ax = fig.get_axes()[0]\n", "ax.set_facecolor(\"dimgrey\")\n", "ax.patch.set_alpha(0.15)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudedepthorigin_timecctidorigin_time_secinterevent_time_secunique_eventhmax_unchmin_uncaz_hmax_uncvmax_unc
event_id
1.030.39970740.76406210.1367192012-07-07 06:56:02.2000.43207511.341644e+090.00True1.8866751.435720-163.0983881.737751
3.130.44511740.7643758.0546882012-07-07 06:56:52.1600.24627231.341644e+090.04True2.9823861.933982-169.5562508.074210
1.230.40459040.76718710.2382812012-07-07 07:07:45.4400.40337611.341645e+09652.96True1.8866751.435720-163.0983881.737751
3.330.44511740.7643758.0546882012-07-07 07:09:59.6800.23929631.341645e+09133.84True2.9823861.933982-169.5562508.074210
0.330.40459040.76218710.1367192012-07-07 07:10:12.4400.25779301.341645e+090.36True1.2040780.954942138.5200751.142778
5.330.39775440.76593710.6445312012-07-07 07:10:20.0000.40218751.341645e+097.56True1.4770161.028668137.0145241.263067
5.430.39824240.76312510.2890622012-07-07 07:10:39.1600.37711251.341645e+0919.12True1.4770161.028668137.0145241.263067
1.430.41386740.76187510.8984382012-07-07 07:10:53.6400.39440011.341645e+0913.60True1.8866751.435720-163.0983881.737751
3.430.41386740.76062510.8984382012-07-07 07:11:07.2800.68114331.341645e+090.08True2.9823861.933982-169.5562508.074210
5.630.40752040.76281210.1367192012-07-07 07:11:36.0800.18118251.341645e+0928.52True1.4770161.028668137.0145241.263067
0.830.40654340.76281210.2382812012-07-07 07:12:06.2001.00000001.341645e+090.04True1.2040780.954942138.5200751.142778
1.730.40068440.76343710.1367192012-07-07 07:14:25.0001.00000011.341645e+09138.80True1.8866751.435720-163.0983881.737751
1.830.41191440.79312511.3046882012-07-07 07:15:44.8800.18390711.341645e+0979.52True1.8866751.435720-163.0983881.737751
5.930.39775440.76593710.6445312012-07-07 07:18:17.0400.39826851.341645e+09152.16True1.4770161.028668137.0145241.263067
0.1130.40459040.76218710.1367192012-07-07 07:22:16.2400.17847101.341646e+09239.16True1.2040780.954942138.5200751.142778
0.1230.40214840.7443756.6328122012-07-07 07:22:43.8800.16965601.341646e+0927.64True1.2040780.954942138.5200751.142778
3.630.39335940.7537502.6718752012-07-07 07:23:08.3200.49105631.341646e+0924.44True2.9823861.933982-169.5562508.074210
1.930.39677740.76906210.7460942012-07-07 07:24:09.5600.18880011.341646e+0960.96True1.8866751.435720-163.0983881.737751
5.1130.40068440.76531210.7460942012-07-07 07:24:34.0001.00000051.341646e+090.24True1.4770161.028668137.0145241.263067
1.1130.41191440.79312511.3046882012-07-07 07:26:09.9200.21158711.341646e+0995.88True1.8866751.435720-163.0983881.737751
1.1230.31474640.7159380.9960942012-07-07 07:27:21.5600.24467011.341646e+0971.64True1.8866751.435720-163.0983881.737751
5.1330.39775440.76593710.6445312012-07-07 07:29:48.9200.21012151.341646e+09147.00True1.4770161.028668137.0145241.263067
3.830.44560540.7653128.0039062012-07-07 07:34:42.6000.42489731.341646e+090.04True2.9823861.933982-169.5562508.074210
3.930.44511740.7643758.0546882012-07-07 07:35:58.0000.26098231.341647e+0975.08True2.9823861.933982-169.5562508.074210
3.1030.44511740.7643758.0546882012-07-07 07:39:17.4000.20231231.341647e+09199.16True2.9823861.933982-169.5562508.074210
3.1130.44511740.7643758.0546882012-07-07 08:00:23.3600.19935031.341648e+091265.96True2.9823861.933982-169.5562508.074210
1.1430.41191440.79312511.3046882012-07-07 08:10:46.5600.15898611.341649e+09623.20True1.8866751.435720-163.0983881.737751
0.1730.33842840.7179691.0214842012-07-07 08:15:48.8800.24627701.341649e+090.04True1.2040780.954942138.5200751.142778
0.1830.42949240.7631258.4609382012-07-07 08:17:35.0000.56385001.341649e+090.04True1.2040780.954942138.5200751.142778
1.1730.41191440.79312511.3046882012-07-07 08:30:27.2400.21819111.341650e+09772.24True1.8866751.435720-163.0983881.737751
2.1930.40459040.76218710.1367192012-07-07 08:41:19.5600.30481321.341650e+090.08True1.2040780.954942138.5200751.142778
1.1930.41191440.79312511.3046882012-07-07 08:45:27.6000.22518911.341651e+09248.04True1.8866751.435720-163.0983881.737751
2.2130.40459040.76218710.1367192012-07-07 08:46:34.3600.32100521.341651e+090.08True1.2040780.954942138.5200751.142778
1.2130.41191440.79312511.3046882012-07-07 08:47:00.5600.40717011.341651e+0926.20True1.8866751.435720-163.0983881.737751
3.1230.44121140.7643758.2578122012-07-07 08:48:43.8001.00000031.341651e+090.08True2.9823861.933982-169.5562508.074210
5.2130.39775440.76593710.6445312012-07-07 09:10:00.6000.39023651.341652e+090.28True1.4770161.028668137.0145241.263067
5.2230.39775440.76593710.6445312012-07-07 09:10:55.6400.15276651.341652e+0954.96True1.4770161.028668137.0145241.263067
1.2430.40263740.7634379.9335942012-07-07 09:20:12.0800.69393311.341653e+09556.44True1.8866751.435720-163.0983881.737751
5.2430.35332040.6706251.9609382012-07-07 09:27:10.1200.33232851.341653e+09417.68True1.4770161.028668137.0145241.263067
1.2530.41191440.79312511.3046882012-07-07 09:42:13.4400.20687611.341654e+09903.28True1.8866751.435720-163.0983881.737751
2.2730.40459040.76218710.1367192012-07-07 09:46:04.9200.34143521.341654e+090.08True1.2040780.954942138.5200751.142778
5.2630.39775440.76593710.6445312012-07-07 09:59:27.2400.18676751.341655e+09802.32True1.4770161.028668137.0145241.263067
1.2730.41191440.79312511.3046882012-07-07 10:05:20.6800.15930511.341656e+09353.36True1.8866751.435720-163.0983881.737751
4.030.32207040.7593752.1640622012-07-07 10:16:39.8001.00000041.341656e+09679.12True3.3787621.691182-143.7723402.733743
5.2730.42753940.7643759.2734382012-07-07 10:41:34.4000.33580451.341658e+090.28True1.4770161.028668137.0145241.263067
0.3030.40459040.76218710.1367192012-07-07 11:15:32.6400.29878001.341660e+090.08True1.2040780.954942138.5200751.142778
0.3130.40459040.76218710.1367192012-07-07 11:23:41.5200.38565001.341660e+090.04True1.2040780.954942138.5200751.142778
1.3030.33134840.7190620.9960942012-07-07 12:13:39.3200.26658211.341663e+092997.80True1.8866751.435720-163.0983881.737751
2.3330.40459040.76218710.1367192012-07-07 12:17:45.1200.16786921.341663e+09245.44True1.2040780.954942138.5200751.142778
6.030.31889640.6767192.6972662012-07-07 15:26:15.0801.00000061.341675e+0911309.96True4.8982082.005268177.7088183.423410
3.1430.44511740.7643758.0546882012-07-07 16:50:34.8400.25522431.341680e+095059.76True2.9823861.933982-169.5562508.074210
1.3130.41191440.79312511.3046882012-07-07 19:23:20.5600.14658111.341689e+099165.72True1.8866751.435720-163.0983881.737751
\n", "
" ], "text/plain": [ " longitude latitude depth origin_time cc \\\n", "event_id \n", "1.0 30.399707 40.764062 10.136719 2012-07-07 06:56:02.200 0.432075 \n", "3.1 30.445117 40.764375 8.054688 2012-07-07 06:56:52.160 0.246272 \n", "1.2 30.404590 40.767187 10.238281 2012-07-07 07:07:45.440 0.403376 \n", "3.3 30.445117 40.764375 8.054688 2012-07-07 07:09:59.680 0.239296 \n", "0.3 30.404590 40.762187 10.136719 2012-07-07 07:10:12.440 0.257793 \n", "5.3 30.397754 40.765937 10.644531 2012-07-07 07:10:20.000 0.402187 \n", "5.4 30.398242 40.763125 10.289062 2012-07-07 07:10:39.160 0.377112 \n", "1.4 30.413867 40.761875 10.898438 2012-07-07 07:10:53.640 0.394400 \n", "3.4 30.413867 40.760625 10.898438 2012-07-07 07:11:07.280 0.681143 \n", "5.6 30.407520 40.762812 10.136719 2012-07-07 07:11:36.080 0.181182 \n", "0.8 30.406543 40.762812 10.238281 2012-07-07 07:12:06.200 1.000000 \n", "1.7 30.400684 40.763437 10.136719 2012-07-07 07:14:25.000 1.000000 \n", "1.8 30.411914 40.793125 11.304688 2012-07-07 07:15:44.880 0.183907 \n", "5.9 30.397754 40.765937 10.644531 2012-07-07 07:18:17.040 0.398268 \n", "0.11 30.404590 40.762187 10.136719 2012-07-07 07:22:16.240 0.178471 \n", "0.12 30.402148 40.744375 6.632812 2012-07-07 07:22:43.880 0.169656 \n", "3.6 30.393359 40.753750 2.671875 2012-07-07 07:23:08.320 0.491056 \n", "1.9 30.396777 40.769062 10.746094 2012-07-07 07:24:09.560 0.188800 \n", "5.11 30.400684 40.765312 10.746094 2012-07-07 07:24:34.000 1.000000 \n", "1.11 30.411914 40.793125 11.304688 2012-07-07 07:26:09.920 0.211587 \n", "1.12 30.314746 40.715938 0.996094 2012-07-07 07:27:21.560 0.244670 \n", "5.13 30.397754 40.765937 10.644531 2012-07-07 07:29:48.920 0.210121 \n", "3.8 30.445605 40.765312 8.003906 2012-07-07 07:34:42.600 0.424897 \n", "3.9 30.445117 40.764375 8.054688 2012-07-07 07:35:58.000 0.260982 \n", "3.10 30.445117 40.764375 8.054688 2012-07-07 07:39:17.400 0.202312 \n", "3.11 30.445117 40.764375 8.054688 2012-07-07 08:00:23.360 0.199350 \n", "1.14 30.411914 40.793125 11.304688 2012-07-07 08:10:46.560 0.158986 \n", "0.17 30.338428 40.717969 1.021484 2012-07-07 08:15:48.880 0.246277 \n", "0.18 30.429492 40.763125 8.460938 2012-07-07 08:17:35.000 0.563850 \n", "1.17 30.411914 40.793125 11.304688 2012-07-07 08:30:27.240 0.218191 \n", "2.19 30.404590 40.762187 10.136719 2012-07-07 08:41:19.560 0.304813 \n", "1.19 30.411914 40.793125 11.304688 2012-07-07 08:45:27.600 0.225189 \n", "2.21 30.404590 40.762187 10.136719 2012-07-07 08:46:34.360 0.321005 \n", "1.21 30.411914 40.793125 11.304688 2012-07-07 08:47:00.560 0.407170 \n", "3.12 30.441211 40.764375 8.257812 2012-07-07 08:48:43.800 1.000000 \n", "5.21 30.397754 40.765937 10.644531 2012-07-07 09:10:00.600 0.390236 \n", "5.22 30.397754 40.765937 10.644531 2012-07-07 09:10:55.640 0.152766 \n", "1.24 30.402637 40.763437 9.933594 2012-07-07 09:20:12.080 0.693933 \n", "5.24 30.353320 40.670625 1.960938 2012-07-07 09:27:10.120 0.332328 \n", "1.25 30.411914 40.793125 11.304688 2012-07-07 09:42:13.440 0.206876 \n", "2.27 30.404590 40.762187 10.136719 2012-07-07 09:46:04.920 0.341435 \n", "5.26 30.397754 40.765937 10.644531 2012-07-07 09:59:27.240 0.186767 \n", "1.27 30.411914 40.793125 11.304688 2012-07-07 10:05:20.680 0.159305 \n", "4.0 30.322070 40.759375 2.164062 2012-07-07 10:16:39.800 1.000000 \n", "5.27 30.427539 40.764375 9.273438 2012-07-07 10:41:34.400 0.335804 \n", "0.30 30.404590 40.762187 10.136719 2012-07-07 11:15:32.640 0.298780 \n", "0.31 30.404590 40.762187 10.136719 2012-07-07 11:23:41.520 0.385650 \n", "1.30 30.331348 40.719062 0.996094 2012-07-07 12:13:39.320 0.266582 \n", "2.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 0.167869 \n", "6.0 30.318896 40.676719 2.697266 2012-07-07 15:26:15.080 1.000000 \n", "3.14 30.445117 40.764375 8.054688 2012-07-07 16:50:34.840 0.255224 \n", "1.31 30.411914 40.793125 11.304688 2012-07-07 19:23:20.560 0.146581 \n", "\n", " tid origin_time_sec interevent_time_sec unique_event hmax_unc \\\n", "event_id \n", "1.0 1 1.341644e+09 0.00 True 1.886675 \n", "3.1 3 1.341644e+09 0.04 True 2.982386 \n", "1.2 1 1.341645e+09 652.96 True 1.886675 \n", "3.3 3 1.341645e+09 133.84 True 2.982386 \n", "0.3 0 1.341645e+09 0.36 True 1.204078 \n", "5.3 5 1.341645e+09 7.56 True 1.477016 \n", "5.4 5 1.341645e+09 19.12 True 1.477016 \n", "1.4 1 1.341645e+09 13.60 True 1.886675 \n", "3.4 3 1.341645e+09 0.08 True 2.982386 \n", "5.6 5 1.341645e+09 28.52 True 1.477016 \n", "0.8 0 1.341645e+09 0.04 True 1.204078 \n", "1.7 1 1.341645e+09 138.80 True 1.886675 \n", "1.8 1 1.341645e+09 79.52 True 1.886675 \n", "5.9 5 1.341645e+09 152.16 True 1.477016 \n", "0.11 0 1.341646e+09 239.16 True 1.204078 \n", "0.12 0 1.341646e+09 27.64 True 1.204078 \n", "3.6 3 1.341646e+09 24.44 True 2.982386 \n", "1.9 1 1.341646e+09 60.96 True 1.886675 \n", "5.11 5 1.341646e+09 0.24 True 1.477016 \n", "1.11 1 1.341646e+09 95.88 True 1.886675 \n", "1.12 1 1.341646e+09 71.64 True 1.886675 \n", "5.13 5 1.341646e+09 147.00 True 1.477016 \n", "3.8 3 1.341646e+09 0.04 True 2.982386 \n", "3.9 3 1.341647e+09 75.08 True 2.982386 \n", "3.10 3 1.341647e+09 199.16 True 2.982386 \n", "3.11 3 1.341648e+09 1265.96 True 2.982386 \n", "1.14 1 1.341649e+09 623.20 True 1.886675 \n", "0.17 0 1.341649e+09 0.04 True 1.204078 \n", "0.18 0 1.341649e+09 0.04 True 1.204078 \n", "1.17 1 1.341650e+09 772.24 True 1.886675 \n", "2.19 2 1.341650e+09 0.08 True 1.204078 \n", "1.19 1 1.341651e+09 248.04 True 1.886675 \n", "2.21 2 1.341651e+09 0.08 True 1.204078 \n", "1.21 1 1.341651e+09 26.20 True 1.886675 \n", "3.12 3 1.341651e+09 0.08 True 2.982386 \n", "5.21 5 1.341652e+09 0.28 True 1.477016 \n", "5.22 5 1.341652e+09 54.96 True 1.477016 \n", "1.24 1 1.341653e+09 556.44 True 1.886675 \n", "5.24 5 1.341653e+09 417.68 True 1.477016 \n", "1.25 1 1.341654e+09 903.28 True 1.886675 \n", "2.27 2 1.341654e+09 0.08 True 1.204078 \n", "5.26 5 1.341655e+09 802.32 True 1.477016 \n", "1.27 1 1.341656e+09 353.36 True 1.886675 \n", "4.0 4 1.341656e+09 679.12 True 3.378762 \n", "5.27 5 1.341658e+09 0.28 True 1.477016 \n", "0.30 0 1.341660e+09 0.08 True 1.204078 \n", "0.31 0 1.341660e+09 0.04 True 1.204078 \n", "1.30 1 1.341663e+09 2997.80 True 1.886675 \n", "2.33 2 1.341663e+09 245.44 True 1.204078 \n", "6.0 6 1.341675e+09 11309.96 True 4.898208 \n", "3.14 3 1.341680e+09 5059.76 True 2.982386 \n", "1.31 1 1.341689e+09 9165.72 True 1.886675 \n", "\n", " hmin_unc az_hmax_unc vmax_unc \n", "event_id \n", "1.0 1.435720 -163.098388 1.737751 \n", "3.1 1.933982 -169.556250 8.074210 \n", "1.2 1.435720 -163.098388 1.737751 \n", "3.3 1.933982 -169.556250 8.074210 \n", "0.3 0.954942 138.520075 1.142778 \n", "5.3 1.028668 137.014524 1.263067 \n", "5.4 1.028668 137.014524 1.263067 \n", "1.4 1.435720 -163.098388 1.737751 \n", "3.4 1.933982 -169.556250 8.074210 \n", "5.6 1.028668 137.014524 1.263067 \n", "0.8 0.954942 138.520075 1.142778 \n", "1.7 1.435720 -163.098388 1.737751 \n", "1.8 1.435720 -163.098388 1.737751 \n", "5.9 1.028668 137.014524 1.263067 \n", "0.11 0.954942 138.520075 1.142778 \n", "0.12 0.954942 138.520075 1.142778 \n", "3.6 1.933982 -169.556250 8.074210 \n", "1.9 1.435720 -163.098388 1.737751 \n", "5.11 1.028668 137.014524 1.263067 \n", "1.11 1.435720 -163.098388 1.737751 \n", "1.12 1.435720 -163.098388 1.737751 \n", "5.13 1.028668 137.014524 1.263067 \n", "3.8 1.933982 -169.556250 8.074210 \n", "3.9 1.933982 -169.556250 8.074210 \n", "3.10 1.933982 -169.556250 8.074210 \n", "3.11 1.933982 -169.556250 8.074210 \n", "1.14 1.435720 -163.098388 1.737751 \n", "0.17 0.954942 138.520075 1.142778 \n", "0.18 0.954942 138.520075 1.142778 \n", "1.17 1.435720 -163.098388 1.737751 \n", "2.19 0.954942 138.520075 1.142778 \n", "1.19 1.435720 -163.098388 1.737751 \n", "2.21 0.954942 138.520075 1.142778 \n", "1.21 1.435720 -163.098388 1.737751 \n", "3.12 1.933982 -169.556250 8.074210 \n", "5.21 1.028668 137.014524 1.263067 \n", "5.22 1.028668 137.014524 1.263067 \n", "1.24 1.435720 -163.098388 1.737751 \n", "5.24 1.028668 137.014524 1.263067 \n", "1.25 1.435720 -163.098388 1.737751 \n", "2.27 0.954942 138.520075 1.142778 \n", "5.26 1.028668 137.014524 1.263067 \n", "1.27 1.435720 -163.098388 1.737751 \n", "4.0 1.691182 -143.772340 2.733743 \n", "5.27 1.028668 137.014524 1.263067 \n", "0.30 0.954942 138.520075 1.142778 \n", "0.31 0.954942 138.520075 1.142778 \n", "1.30 1.435720 -163.098388 1.737751 \n", "2.33 0.954942 138.520075 1.142778 \n", "6.0 2.005268 177.708818 3.423410 \n", "3.14 1.933982 -169.556250 8.074210 \n", "1.31 1.435720 -163.098388 1.737751 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "template_group.catalog.catalog" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Assemble the backprojection and template matching catalog \n", "\n", "When selecting the template events from the backprojection catalog, we imposed some quality criteria that might have thrown out some events that we still want in our final catalog. Here, we make a simple comparison of the backprojection and template matching catalogs to find these missing events and add them to the final catalog." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "BACKPROJECTION_CATALOG_FILENAME = \"backprojection_catalog.csv\"" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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event_idlongitudelatitudedepthorigin_timecctidorigin_time_secinterevent_time_secunique_eventhmax_unchmin_uncaz_hmax_uncvmax_unc
0tm_1.030.39970740.76406210.1367192012-07-07 06:56:02.2000.43207511.341644e+090.00True1.8866751.435720-163.0983881.737751
1tm_3.130.44511740.7643758.0546882012-07-07 06:56:52.1600.24627231.341644e+090.04True2.9823861.933982-169.5562508.074210
2tm_1.230.40459040.76718710.2382812012-07-07 07:07:45.4400.40337611.341645e+09652.96True1.8866751.435720-163.0983881.737751
3tm_3.330.44511740.7643758.0546882012-07-07 07:09:59.6800.23929631.341645e+09133.84True2.9823861.933982-169.5562508.074210
4tm_0.330.40459040.76218710.1367192012-07-07 07:10:12.4400.25779301.341645e+090.36True1.2040780.954942138.5200751.142778
5tm_5.330.39775440.76593710.6445312012-07-07 07:10:20.0000.40218751.341645e+097.56True1.4770161.028668137.0145241.263067
6tm_5.430.39824240.76312510.2890622012-07-07 07:10:39.1600.37711251.341645e+0919.12True1.4770161.028668137.0145241.263067
7tm_1.430.41386740.76187510.8984382012-07-07 07:10:53.6400.39440011.341645e+0913.60True1.8866751.435720-163.0983881.737751
8tm_3.430.41386740.76062510.8984382012-07-07 07:11:07.2800.68114331.341645e+090.08True2.9823861.933982-169.5562508.074210
9tm_5.630.40752040.76281210.1367192012-07-07 07:11:36.0800.18118251.341645e+0928.52True1.4770161.028668137.0145241.263067
10tm_0.830.40654340.76281210.2382812012-07-07 07:12:06.2001.00000001.341645e+090.04True1.2040780.954942138.5200751.142778
11tm_1.730.40068440.76343710.1367192012-07-07 07:14:25.0001.00000011.341645e+09138.80True1.8866751.435720-163.0983881.737751
12tm_1.830.41191440.79312511.3046882012-07-07 07:15:44.8800.18390711.341645e+0979.52True1.8866751.435720-163.0983881.737751
13tm_5.930.39775440.76593710.6445312012-07-07 07:18:17.0400.39826851.341645e+09152.16True1.4770161.028668137.0145241.263067
14tm_0.1130.40459040.76218710.1367192012-07-07 07:22:16.2400.17847101.341646e+09239.16True1.2040780.954942138.5200751.142778
15tm_0.1230.40214840.7443756.6328122012-07-07 07:22:43.8800.16965601.341646e+0927.64True1.2040780.954942138.5200751.142778
16tm_3.630.39335940.7537502.6718752012-07-07 07:23:08.3200.49105631.341646e+0924.44True2.9823861.933982-169.5562508.074210
17tm_1.930.39677740.76906210.7460942012-07-07 07:24:09.5600.18880011.341646e+0960.96True1.8866751.435720-163.0983881.737751
18tm_5.1130.40068440.76531210.7460942012-07-07 07:24:34.0001.00000051.341646e+090.24True1.4770161.028668137.0145241.263067
19tm_1.1130.41191440.79312511.3046882012-07-07 07:26:09.9200.21158711.341646e+0995.88True1.8866751.435720-163.0983881.737751
20tm_1.1230.31474640.7159380.9960942012-07-07 07:27:21.5600.24467011.341646e+0971.64True1.8866751.435720-163.0983881.737751
21tm_5.1330.39775440.76593710.6445312012-07-07 07:29:48.9200.21012151.341646e+09147.00True1.4770161.028668137.0145241.263067
22tm_3.830.44560540.7653128.0039062012-07-07 07:34:42.6000.42489731.341646e+090.04True2.9823861.933982-169.5562508.074210
23tm_3.930.44511740.7643758.0546882012-07-07 07:35:58.0000.26098231.341647e+0975.08True2.9823861.933982-169.5562508.074210
24tm_3.1030.44511740.7643758.0546882012-07-07 07:39:17.4000.20231231.341647e+09199.16True2.9823861.933982-169.5562508.074210
25tm_3.1130.44511740.7643758.0546882012-07-07 08:00:23.3600.19935031.341648e+091265.96True2.9823861.933982-169.5562508.074210
26tm_1.1430.41191440.79312511.3046882012-07-07 08:10:46.5600.15898611.341649e+09623.20True1.8866751.435720-163.0983881.737751
27tm_0.1730.33842840.7179691.0214842012-07-07 08:15:48.8800.24627701.341649e+090.04True1.2040780.954942138.5200751.142778
28tm_0.1830.42949240.7631258.4609382012-07-07 08:17:35.0000.56385001.341649e+090.04True1.2040780.954942138.5200751.142778
29tm_1.1730.41191440.79312511.3046882012-07-07 08:30:27.2400.21819111.341650e+09772.24True1.8866751.435720-163.0983881.737751
30tm_2.1930.40459040.76218710.1367192012-07-07 08:41:19.5600.30481321.341650e+090.08True1.2040780.954942138.5200751.142778
31tm_1.1930.41191440.79312511.3046882012-07-07 08:45:27.6000.22518911.341651e+09248.04True1.8866751.435720-163.0983881.737751
32tm_2.2130.40459040.76218710.1367192012-07-07 08:46:34.3600.32100521.341651e+090.08True1.2040780.954942138.5200751.142778
33tm_1.2130.41191440.79312511.3046882012-07-07 08:47:00.5600.40717011.341651e+0926.20True1.8866751.435720-163.0983881.737751
34tm_3.1230.44121140.7643758.2578122012-07-07 08:48:43.8001.00000031.341651e+090.08True2.9823861.933982-169.5562508.074210
35tm_5.2130.39775440.76593710.6445312012-07-07 09:10:00.6000.39023651.341652e+090.28True1.4770161.028668137.0145241.263067
36tm_5.2230.39775440.76593710.6445312012-07-07 09:10:55.6400.15276651.341652e+0954.96True1.4770161.028668137.0145241.263067
37tm_1.2430.40263740.7634379.9335942012-07-07 09:20:12.0800.69393311.341653e+09556.44True1.8866751.435720-163.0983881.737751
38tm_5.2430.35332040.6706251.9609382012-07-07 09:27:10.1200.33232851.341653e+09417.68True1.4770161.028668137.0145241.263067
39tm_1.2530.41191440.79312511.3046882012-07-07 09:42:13.4400.20687611.341654e+09903.28True1.8866751.435720-163.0983881.737751
40tm_2.2730.40459040.76218710.1367192012-07-07 09:46:04.9200.34143521.341654e+090.08True1.2040780.954942138.5200751.142778
41tm_5.2630.39775440.76593710.6445312012-07-07 09:59:27.2400.18676751.341655e+09802.32True1.4770161.028668137.0145241.263067
42tm_1.2730.41191440.79312511.3046882012-07-07 10:05:20.6800.15930511.341656e+09353.36True1.8866751.435720-163.0983881.737751
43tm_4.030.32207040.7593752.1640622012-07-07 10:16:39.8001.00000041.341656e+09679.12True3.3787621.691182-143.7723402.733743
44tm_5.2730.42753940.7643759.2734382012-07-07 10:41:34.4000.33580451.341658e+090.28True1.4770161.028668137.0145241.263067
45tm_0.3030.40459040.76218710.1367192012-07-07 11:15:32.6400.29878001.341660e+090.08True1.2040780.954942138.5200751.142778
46tm_0.3130.40459040.76218710.1367192012-07-07 11:23:41.5200.38565001.341660e+090.04True1.2040780.954942138.5200751.142778
47tm_1.3030.33134840.7190620.9960942012-07-07 12:13:39.3200.26658211.341663e+092997.80True1.8866751.435720-163.0983881.737751
48tm_2.3330.40459040.76218710.1367192012-07-07 12:17:45.1200.16786921.341663e+09245.44True1.2040780.954942138.5200751.142778
49tm_6.030.31889640.6767192.6972662012-07-07 15:26:15.0801.00000061.341675e+0911309.96True4.8982082.005268177.7088183.423410
50tm_3.1430.44511740.7643758.0546882012-07-07 16:50:34.8400.25522431.341680e+095059.76True2.9823861.933982-169.5562508.074210
51tm_1.3130.41191440.79312511.3046882012-07-07 19:23:20.5600.14658111.341689e+099165.72True1.8866751.435720-163.0983881.737751
\n", "
" ], "text/plain": [ " event_id longitude latitude depth origin_time \\\n", "0 tm_1.0 30.399707 40.764062 10.136719 2012-07-07 06:56:02.200 \n", "1 tm_3.1 30.445117 40.764375 8.054688 2012-07-07 06:56:52.160 \n", "2 tm_1.2 30.404590 40.767187 10.238281 2012-07-07 07:07:45.440 \n", "3 tm_3.3 30.445117 40.764375 8.054688 2012-07-07 07:09:59.680 \n", "4 tm_0.3 30.404590 40.762187 10.136719 2012-07-07 07:10:12.440 \n", "5 tm_5.3 30.397754 40.765937 10.644531 2012-07-07 07:10:20.000 \n", "6 tm_5.4 30.398242 40.763125 10.289062 2012-07-07 07:10:39.160 \n", "7 tm_1.4 30.413867 40.761875 10.898438 2012-07-07 07:10:53.640 \n", "8 tm_3.4 30.413867 40.760625 10.898438 2012-07-07 07:11:07.280 \n", "9 tm_5.6 30.407520 40.762812 10.136719 2012-07-07 07:11:36.080 \n", "10 tm_0.8 30.406543 40.762812 10.238281 2012-07-07 07:12:06.200 \n", "11 tm_1.7 30.400684 40.763437 10.136719 2012-07-07 07:14:25.000 \n", "12 tm_1.8 30.411914 40.793125 11.304688 2012-07-07 07:15:44.880 \n", "13 tm_5.9 30.397754 40.765937 10.644531 2012-07-07 07:18:17.040 \n", "14 tm_0.11 30.404590 40.762187 10.136719 2012-07-07 07:22:16.240 \n", "15 tm_0.12 30.402148 40.744375 6.632812 2012-07-07 07:22:43.880 \n", "16 tm_3.6 30.393359 40.753750 2.671875 2012-07-07 07:23:08.320 \n", "17 tm_1.9 30.396777 40.769062 10.746094 2012-07-07 07:24:09.560 \n", "18 tm_5.11 30.400684 40.765312 10.746094 2012-07-07 07:24:34.000 \n", "19 tm_1.11 30.411914 40.793125 11.304688 2012-07-07 07:26:09.920 \n", "20 tm_1.12 30.314746 40.715938 0.996094 2012-07-07 07:27:21.560 \n", "21 tm_5.13 30.397754 40.765937 10.644531 2012-07-07 07:29:48.920 \n", "22 tm_3.8 30.445605 40.765312 8.003906 2012-07-07 07:34:42.600 \n", "23 tm_3.9 30.445117 40.764375 8.054688 2012-07-07 07:35:58.000 \n", "24 tm_3.10 30.445117 40.764375 8.054688 2012-07-07 07:39:17.400 \n", "25 tm_3.11 30.445117 40.764375 8.054688 2012-07-07 08:00:23.360 \n", "26 tm_1.14 30.411914 40.793125 11.304688 2012-07-07 08:10:46.560 \n", "27 tm_0.17 30.338428 40.717969 1.021484 2012-07-07 08:15:48.880 \n", "28 tm_0.18 30.429492 40.763125 8.460938 2012-07-07 08:17:35.000 \n", "29 tm_1.17 30.411914 40.793125 11.304688 2012-07-07 08:30:27.240 \n", "30 tm_2.19 30.404590 40.762187 10.136719 2012-07-07 08:41:19.560 \n", "31 tm_1.19 30.411914 40.793125 11.304688 2012-07-07 08:45:27.600 \n", "32 tm_2.21 30.404590 40.762187 10.136719 2012-07-07 08:46:34.360 \n", "33 tm_1.21 30.411914 40.793125 11.304688 2012-07-07 08:47:00.560 \n", "34 tm_3.12 30.441211 40.764375 8.257812 2012-07-07 08:48:43.800 \n", "35 tm_5.21 30.397754 40.765937 10.644531 2012-07-07 09:10:00.600 \n", "36 tm_5.22 30.397754 40.765937 10.644531 2012-07-07 09:10:55.640 \n", "37 tm_1.24 30.402637 40.763437 9.933594 2012-07-07 09:20:12.080 \n", "38 tm_5.24 30.353320 40.670625 1.960938 2012-07-07 09:27:10.120 \n", "39 tm_1.25 30.411914 40.793125 11.304688 2012-07-07 09:42:13.440 \n", "40 tm_2.27 30.404590 40.762187 10.136719 2012-07-07 09:46:04.920 \n", "41 tm_5.26 30.397754 40.765937 10.644531 2012-07-07 09:59:27.240 \n", "42 tm_1.27 30.411914 40.793125 11.304688 2012-07-07 10:05:20.680 \n", "43 tm_4.0 30.322070 40.759375 2.164062 2012-07-07 10:16:39.800 \n", "44 tm_5.27 30.427539 40.764375 9.273438 2012-07-07 10:41:34.400 \n", "45 tm_0.30 30.404590 40.762187 10.136719 2012-07-07 11:15:32.640 \n", "46 tm_0.31 30.404590 40.762187 10.136719 2012-07-07 11:23:41.520 \n", "47 tm_1.30 30.331348 40.719062 0.996094 2012-07-07 12:13:39.320 \n", "48 tm_2.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 \n", "49 tm_6.0 30.318896 40.676719 2.697266 2012-07-07 15:26:15.080 \n", "50 tm_3.14 30.445117 40.764375 8.054688 2012-07-07 16:50:34.840 \n", "51 tm_1.31 30.411914 40.793125 11.304688 2012-07-07 19:23:20.560 \n", "\n", " cc tid origin_time_sec interevent_time_sec unique_event \\\n", "0 0.432075 1 1.341644e+09 0.00 True \n", "1 0.246272 3 1.341644e+09 0.04 True \n", "2 0.403376 1 1.341645e+09 652.96 True \n", "3 0.239296 3 1.341645e+09 133.84 True \n", "4 0.257793 0 1.341645e+09 0.36 True \n", "5 0.402187 5 1.341645e+09 7.56 True \n", "6 0.377112 5 1.341645e+09 19.12 True \n", "7 0.394400 1 1.341645e+09 13.60 True \n", "8 0.681143 3 1.341645e+09 0.08 True \n", "9 0.181182 5 1.341645e+09 28.52 True \n", "10 1.000000 0 1.341645e+09 0.04 True \n", "11 1.000000 1 1.341645e+09 138.80 True \n", "12 0.183907 1 1.341645e+09 79.52 True \n", "13 0.398268 5 1.341645e+09 152.16 True \n", "14 0.178471 0 1.341646e+09 239.16 True \n", "15 0.169656 0 1.341646e+09 27.64 True \n", "16 0.491056 3 1.341646e+09 24.44 True \n", "17 0.188800 1 1.341646e+09 60.96 True \n", "18 1.000000 5 1.341646e+09 0.24 True \n", "19 0.211587 1 1.341646e+09 95.88 True \n", "20 0.244670 1 1.341646e+09 71.64 True \n", "21 0.210121 5 1.341646e+09 147.00 True \n", "22 0.424897 3 1.341646e+09 0.04 True \n", "23 0.260982 3 1.341647e+09 75.08 True \n", "24 0.202312 3 1.341647e+09 199.16 True \n", "25 0.199350 3 1.341648e+09 1265.96 True \n", "26 0.158986 1 1.341649e+09 623.20 True \n", "27 0.246277 0 1.341649e+09 0.04 True \n", "28 0.563850 0 1.341649e+09 0.04 True \n", "29 0.218191 1 1.341650e+09 772.24 True \n", "30 0.304813 2 1.341650e+09 0.08 True \n", "31 0.225189 1 1.341651e+09 248.04 True \n", "32 0.321005 2 1.341651e+09 0.08 True \n", "33 0.407170 1 1.341651e+09 26.20 True \n", "34 1.000000 3 1.341651e+09 0.08 True \n", "35 0.390236 5 1.341652e+09 0.28 True \n", "36 0.152766 5 1.341652e+09 54.96 True \n", "37 0.693933 1 1.341653e+09 556.44 True \n", "38 0.332328 5 1.341653e+09 417.68 True \n", "39 0.206876 1 1.341654e+09 903.28 True \n", "40 0.341435 2 1.341654e+09 0.08 True \n", "41 0.186767 5 1.341655e+09 802.32 True \n", "42 0.159305 1 1.341656e+09 353.36 True \n", "43 1.000000 4 1.341656e+09 679.12 True \n", "44 0.335804 5 1.341658e+09 0.28 True \n", "45 0.298780 0 1.341660e+09 0.08 True \n", "46 0.385650 0 1.341660e+09 0.04 True \n", "47 0.266582 1 1.341663e+09 2997.80 True \n", "48 0.167869 2 1.341663e+09 245.44 True \n", "49 1.000000 6 1.341675e+09 11309.96 True \n", "50 0.255224 3 1.341680e+09 5059.76 True \n", "51 0.146581 1 1.341689e+09 9165.72 True \n", "\n", " hmax_unc hmin_unc az_hmax_unc vmax_unc \n", "0 1.886675 1.435720 -163.098388 1.737751 \n", "1 2.982386 1.933982 -169.556250 8.074210 \n", "2 1.886675 1.435720 -163.098388 1.737751 \n", "3 2.982386 1.933982 -169.556250 8.074210 \n", "4 1.204078 0.954942 138.520075 1.142778 \n", "5 1.477016 1.028668 137.014524 1.263067 \n", "6 1.477016 1.028668 137.014524 1.263067 \n", "7 1.886675 1.435720 -163.098388 1.737751 \n", "8 2.982386 1.933982 -169.556250 8.074210 \n", "9 1.477016 1.028668 137.014524 1.263067 \n", "10 1.204078 0.954942 138.520075 1.142778 \n", "11 1.886675 1.435720 -163.098388 1.737751 \n", "12 1.886675 1.435720 -163.098388 1.737751 \n", "13 1.477016 1.028668 137.014524 1.263067 \n", "14 1.204078 0.954942 138.520075 1.142778 \n", "15 1.204078 0.954942 138.520075 1.142778 \n", "16 2.982386 1.933982 -169.556250 8.074210 \n", "17 1.886675 1.435720 -163.098388 1.737751 \n", "18 1.477016 1.028668 137.014524 1.263067 \n", "19 1.886675 1.435720 -163.098388 1.737751 \n", "20 1.886675 1.435720 -163.098388 1.737751 \n", "21 1.477016 1.028668 137.014524 1.263067 \n", "22 2.982386 1.933982 -169.556250 8.074210 \n", "23 2.982386 1.933982 -169.556250 8.074210 \n", "24 2.982386 1.933982 -169.556250 8.074210 \n", "25 2.982386 1.933982 -169.556250 8.074210 \n", "26 1.886675 1.435720 -163.098388 1.737751 \n", "27 1.204078 0.954942 138.520075 1.142778 \n", "28 1.204078 0.954942 138.520075 1.142778 \n", "29 1.886675 1.435720 -163.098388 1.737751 \n", "30 1.204078 0.954942 138.520075 1.142778 \n", "31 1.886675 1.435720 -163.098388 1.737751 \n", "32 1.204078 0.954942 138.520075 1.142778 \n", "33 1.886675 1.435720 -163.098388 1.737751 \n", "34 2.982386 1.933982 -169.556250 8.074210 \n", "35 1.477016 1.028668 137.014524 1.263067 \n", "36 1.477016 1.028668 137.014524 1.263067 \n", "37 1.886675 1.435720 -163.098388 1.737751 \n", "38 1.477016 1.028668 137.014524 1.263067 \n", "39 1.886675 1.435720 -163.098388 1.737751 \n", "40 1.204078 0.954942 138.520075 1.142778 \n", "41 1.477016 1.028668 137.014524 1.263067 \n", "42 1.886675 1.435720 -163.098388 1.737751 \n", "43 3.378762 1.691182 -143.772340 2.733743 \n", "44 1.477016 1.028668 137.014524 1.263067 \n", "45 1.204078 0.954942 138.520075 1.142778 \n", "46 1.204078 0.954942 138.520075 1.142778 \n", "47 1.886675 1.435720 -163.098388 1.737751 \n", "48 1.204078 0.954942 138.520075 1.142778 \n", "49 4.898208 2.005268 177.708818 3.423410 \n", "50 2.982386 1.933982 -169.556250 8.074210 \n", "51 1.886675 1.435720 -163.098388 1.737751 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tm_catalog = template_group.catalog.catalog.copy()\n", "tm_catalog.reset_index(inplace=True)\n", "for i in range(len(tm_catalog)):\n", " tm_catalog.loc[i, \"event_id\"] = f\"tm_{tm_catalog.loc[i, 'event_id']}\"\n", "tm_catalog" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudedepthorigin_timehmax_unchmin_uncaz_hmax_uncMwMw_errevent_id
030.40263740.76593710.2382812012-07-07 06:56:02.5601.5544061.320863-167.3898372.6886092.603238e-02bp_0
130.40849640.76593710.2382812012-07-07 07:07:45.7201.3867111.049999148.1217503.9126201.835205e-31bp_1
230.40459040.76218710.1367192012-07-07 07:12:06.2001.2040780.954942138.520075NaNNaNbp_2
330.41191440.79312511.3046882012-07-07 07:14:25.0001.8866751.435720-163.0983882.8094261.748471e-02bp_3
430.39775440.76593710.6445312012-07-07 07:24:34.0001.4770161.028668137.0145242.6046102.720755e-02bp_4
530.40996140.76812510.2890622012-07-07 07:34:42.8002.1579811.659802125.916805NaNNaNbp_5
630.41386740.7593759.2734382012-07-07 08:17:35.0801.8078231.10972170.558859NaNNaNbp_6
730.44511740.7643758.0546882012-07-07 08:48:43.8002.9823861.933982-169.556250NaNNaNbp_7
830.40263740.76468710.0351562012-07-07 09:20:12.4801.1169280.884104159.472964NaNNaNbp_8
930.31669940.7546880.2851562012-07-07 10:16:39.8003.3787621.691182-143.772340NaNNaNbp_9
1030.42168040.7643759.2734382012-07-07 10:41:34.3201.9210961.39624181.876681NaNNaNbp_10
1130.29448240.628281-1.5175782012-07-07 15:26:15.0804.8982082.005268177.708818NaNNaNbp_11
\n", "
" ], "text/plain": [ " longitude latitude depth origin_time hmax_unc \\\n", "0 30.402637 40.765937 10.238281 2012-07-07 06:56:02.560 1.554406 \n", "1 30.408496 40.765937 10.238281 2012-07-07 07:07:45.720 1.386711 \n", "2 30.404590 40.762187 10.136719 2012-07-07 07:12:06.200 1.204078 \n", "3 30.411914 40.793125 11.304688 2012-07-07 07:14:25.000 1.886675 \n", "4 30.397754 40.765937 10.644531 2012-07-07 07:24:34.000 1.477016 \n", "5 30.409961 40.768125 10.289062 2012-07-07 07:34:42.800 2.157981 \n", "6 30.413867 40.759375 9.273438 2012-07-07 08:17:35.080 1.807823 \n", "7 30.445117 40.764375 8.054688 2012-07-07 08:48:43.800 2.982386 \n", "8 30.402637 40.764687 10.035156 2012-07-07 09:20:12.480 1.116928 \n", "9 30.316699 40.754688 0.285156 2012-07-07 10:16:39.800 3.378762 \n", "10 30.421680 40.764375 9.273438 2012-07-07 10:41:34.320 1.921096 \n", "11 30.294482 40.628281 -1.517578 2012-07-07 15:26:15.080 4.898208 \n", "\n", " hmin_unc az_hmax_unc Mw Mw_err event_id \n", "0 1.320863 -167.389837 2.688609 2.603238e-02 bp_0 \n", "1 1.049999 148.121750 3.912620 1.835205e-31 bp_1 \n", "2 0.954942 138.520075 NaN NaN bp_2 \n", "3 1.435720 -163.098388 2.809426 1.748471e-02 bp_3 \n", "4 1.028668 137.014524 2.604610 2.720755e-02 bp_4 \n", "5 1.659802 125.916805 NaN NaN bp_5 \n", "6 1.109721 70.558859 NaN NaN bp_6 \n", "7 1.933982 -169.556250 NaN NaN bp_7 \n", "8 0.884104 159.472964 NaN NaN bp_8 \n", "9 1.691182 -143.772340 NaN NaN bp_9 \n", "10 1.396241 81.876681 NaN NaN bp_10 \n", "11 2.005268 177.708818 NaN NaN bp_11 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bp_catalog = pd.read_csv(\n", " os.path.join(BPMF.cfg.OUTPUT_PATH, BACKPROJECTION_CATALOG_FILENAME),\n", " index_col=0\n", ")\n", "# add event ids\n", "for i in range(len(bp_catalog)):\n", " bp_catalog.loc[i, \"event_id\"] = f\"bp_{i}\"\n", "# convert origin times from string to pandas.Timestamp\n", "bp_catalog[\"origin_time\"] = pd.to_datetime(bp_catalog[\"origin_time\"])\n", "bp_catalog" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# catalog merging parameters\n", "dt_criterion_pd = pd.Timedelta(DT_CRITERION_SEC, \"s\")\n", "HMAX_UNC_CRITERION_KM = 10." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loop through BP cat: 100%|██████████| 12/12 [00:00<00:00, 1574.19it/s]\n" ] } ], "source": [ "final_catalog = tm_catalog.copy()\n", "missing_event = np.zeros(len(bp_catalog), dtype=bool)\n", "\n", "for i in tqdm(range(len(bp_catalog)), desc=\"Loop through BP cat\"):\n", " if bp_catalog.iloc[i][\"hmax_unc\"] > HMAX_UNC_CRITERION_KM:\n", " continue\n", " t_min = bp_catalog.iloc[i][\"origin_time\"] - dt_criterion_pd\n", " t_max = bp_catalog.iloc[i][\"origin_time\"] + dt_criterion_pd\n", " subset_tm = (tm_catalog[\"origin_time\"] > t_min) & (tm_catalog[\"origin_time\"] < t_max)\n", " if np.sum(subset_tm) == 0:\n", " missing_event[i] = True\n", "\n", "final_catalog = pd.concat(\n", " (tm_catalog, bp_catalog[missing_event]),\n", " ignore_index=True\n", " )\n", "final_catalog.sort_values(\n", " \"origin_time\", ascending=True, inplace=True\n", " )" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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event_idlongitudelatitudedepthorigin_timecctidorigin_time_secinterevent_time_secunique_eventhmax_unchmin_uncaz_hmax_uncvmax_uncMwMw_err
event_id
tm_1.0tm_1.030.39970740.76406210.1367192012-07-07 06:56:02.2000.4320751.01.341644e+090.00True1.8866751.435720-163.0983881.737751NaNNaN
tm_3.1tm_3.130.44511740.7643758.0546882012-07-07 06:56:52.1600.2462723.01.341644e+090.04True2.9823861.933982-169.5562508.074210NaNNaN
tm_1.2tm_1.230.40459040.76718710.2382812012-07-07 07:07:45.4400.4033761.01.341645e+09652.96True1.8866751.435720-163.0983881.737751NaNNaN
tm_3.3tm_3.330.44511740.7643758.0546882012-07-07 07:09:59.6800.2392963.01.341645e+09133.84True2.9823861.933982-169.5562508.074210NaNNaN
tm_0.3tm_0.330.40459040.76218710.1367192012-07-07 07:10:12.4400.2577930.01.341645e+090.36True1.2040780.954942138.5200751.142778NaNNaN
tm_5.3tm_5.330.39775440.76593710.6445312012-07-07 07:10:20.0000.4021875.01.341645e+097.56True1.4770161.028668137.0145241.263067NaNNaN
tm_5.4tm_5.430.39824240.76312510.2890622012-07-07 07:10:39.1600.3771125.01.341645e+0919.12True1.4770161.028668137.0145241.263067NaNNaN
tm_1.4tm_1.430.41386740.76187510.8984382012-07-07 07:10:53.6400.3944001.01.341645e+0913.60True1.8866751.435720-163.0983881.737751NaNNaN
tm_3.4tm_3.430.41386740.76062510.8984382012-07-07 07:11:07.2800.6811433.01.341645e+090.08True2.9823861.933982-169.5562508.074210NaNNaN
tm_5.6tm_5.630.40752040.76281210.1367192012-07-07 07:11:36.0800.1811825.01.341645e+0928.52True1.4770161.028668137.0145241.263067NaNNaN
tm_0.8tm_0.830.40654340.76281210.2382812012-07-07 07:12:06.2001.0000000.01.341645e+090.04True1.2040780.954942138.5200751.142778NaNNaN
tm_1.7tm_1.730.40068440.76343710.1367192012-07-07 07:14:25.0001.0000001.01.341645e+09138.80True1.8866751.435720-163.0983881.737751NaNNaN
tm_1.8tm_1.830.41191440.79312511.3046882012-07-07 07:15:44.8800.1839071.01.341645e+0979.52True1.8866751.435720-163.0983881.737751NaNNaN
tm_5.9tm_5.930.39775440.76593710.6445312012-07-07 07:18:17.0400.3982685.01.341645e+09152.16True1.4770161.028668137.0145241.263067NaNNaN
tm_0.11tm_0.1130.40459040.76218710.1367192012-07-07 07:22:16.2400.1784710.01.341646e+09239.16True1.2040780.954942138.5200751.142778NaNNaN
tm_0.12tm_0.1230.40214840.7443756.6328122012-07-07 07:22:43.8800.1696560.01.341646e+0927.64True1.2040780.954942138.5200751.142778NaNNaN
tm_3.6tm_3.630.39335940.7537502.6718752012-07-07 07:23:08.3200.4910563.01.341646e+0924.44True2.9823861.933982-169.5562508.074210NaNNaN
tm_1.9tm_1.930.39677740.76906210.7460942012-07-07 07:24:09.5600.1888001.01.341646e+0960.96True1.8866751.435720-163.0983881.737751NaNNaN
tm_5.11tm_5.1130.40068440.76531210.7460942012-07-07 07:24:34.0001.0000005.01.341646e+090.24True1.4770161.028668137.0145241.263067NaNNaN
tm_1.11tm_1.1130.41191440.79312511.3046882012-07-07 07:26:09.9200.2115871.01.341646e+0995.88True1.8866751.435720-163.0983881.737751NaNNaN
tm_1.12tm_1.1230.31474640.7159380.9960942012-07-07 07:27:21.5600.2446701.01.341646e+0971.64True1.8866751.435720-163.0983881.737751NaNNaN
tm_5.13tm_5.1330.39775440.76593710.6445312012-07-07 07:29:48.9200.2101215.01.341646e+09147.00True1.4770161.028668137.0145241.263067NaNNaN
tm_3.8tm_3.830.44560540.7653128.0039062012-07-07 07:34:42.6000.4248973.01.341646e+090.04True2.9823861.933982-169.5562508.074210NaNNaN
tm_3.9tm_3.930.44511740.7643758.0546882012-07-07 07:35:58.0000.2609823.01.341647e+0975.08True2.9823861.933982-169.5562508.074210NaNNaN
tm_3.10tm_3.1030.44511740.7643758.0546882012-07-07 07:39:17.4000.2023123.01.341647e+09199.16True2.9823861.933982-169.5562508.074210NaNNaN
tm_3.11tm_3.1130.44511740.7643758.0546882012-07-07 08:00:23.3600.1993503.01.341648e+091265.96True2.9823861.933982-169.5562508.074210NaNNaN
tm_1.14tm_1.1430.41191440.79312511.3046882012-07-07 08:10:46.5600.1589861.01.341649e+09623.20True1.8866751.435720-163.0983881.737751NaNNaN
tm_0.17tm_0.1730.33842840.7179691.0214842012-07-07 08:15:48.8800.2462770.01.341649e+090.04True1.2040780.954942138.5200751.142778NaNNaN
tm_0.18tm_0.1830.42949240.7631258.4609382012-07-07 08:17:35.0000.5638500.01.341649e+090.04True1.2040780.954942138.5200751.142778NaNNaN
tm_1.17tm_1.1730.41191440.79312511.3046882012-07-07 08:30:27.2400.2181911.01.341650e+09772.24True1.8866751.435720-163.0983881.737751NaNNaN
tm_2.19tm_2.1930.40459040.76218710.1367192012-07-07 08:41:19.5600.3048132.01.341650e+090.08True1.2040780.954942138.5200751.142778NaNNaN
tm_1.19tm_1.1930.41191440.79312511.3046882012-07-07 08:45:27.6000.2251891.01.341651e+09248.04True1.8866751.435720-163.0983881.737751NaNNaN
tm_2.21tm_2.2130.40459040.76218710.1367192012-07-07 08:46:34.3600.3210052.01.341651e+090.08True1.2040780.954942138.5200751.142778NaNNaN
tm_1.21tm_1.2130.41191440.79312511.3046882012-07-07 08:47:00.5600.4071701.01.341651e+0926.20True1.8866751.435720-163.0983881.737751NaNNaN
tm_3.12tm_3.1230.44121140.7643758.2578122012-07-07 08:48:43.8001.0000003.01.341651e+090.08True2.9823861.933982-169.5562508.074210NaNNaN
tm_5.21tm_5.2130.39775440.76593710.6445312012-07-07 09:10:00.6000.3902365.01.341652e+090.28True1.4770161.028668137.0145241.263067NaNNaN
tm_5.22tm_5.2230.39775440.76593710.6445312012-07-07 09:10:55.6400.1527665.01.341652e+0954.96True1.4770161.028668137.0145241.263067NaNNaN
tm_1.24tm_1.2430.40263740.7634379.9335942012-07-07 09:20:12.0800.6939331.01.341653e+09556.44True1.8866751.435720-163.0983881.737751NaNNaN
tm_5.24tm_5.2430.35332040.6706251.9609382012-07-07 09:27:10.1200.3323285.01.341653e+09417.68True1.4770161.028668137.0145241.263067NaNNaN
tm_1.25tm_1.2530.41191440.79312511.3046882012-07-07 09:42:13.4400.2068761.01.341654e+09903.28True1.8866751.435720-163.0983881.737751NaNNaN
tm_2.27tm_2.2730.40459040.76218710.1367192012-07-07 09:46:04.9200.3414352.01.341654e+090.08True1.2040780.954942138.5200751.142778NaNNaN
tm_5.26tm_5.2630.39775440.76593710.6445312012-07-07 09:59:27.2400.1867675.01.341655e+09802.32True1.4770161.028668137.0145241.263067NaNNaN
tm_1.27tm_1.2730.41191440.79312511.3046882012-07-07 10:05:20.6800.1593051.01.341656e+09353.36True1.8866751.435720-163.0983881.737751NaNNaN
tm_4.0tm_4.030.32207040.7593752.1640622012-07-07 10:16:39.8001.0000004.01.341656e+09679.12True3.3787621.691182-143.7723402.733743NaNNaN
tm_5.27tm_5.2730.42753940.7643759.2734382012-07-07 10:41:34.4000.3358045.01.341658e+090.28True1.4770161.028668137.0145241.263067NaNNaN
tm_0.30tm_0.3030.40459040.76218710.1367192012-07-07 11:15:32.6400.2987800.01.341660e+090.08True1.2040780.954942138.5200751.142778NaNNaN
tm_0.31tm_0.3130.40459040.76218710.1367192012-07-07 11:23:41.5200.3856500.01.341660e+090.04True1.2040780.954942138.5200751.142778NaNNaN
tm_1.30tm_1.3030.33134840.7190620.9960942012-07-07 12:13:39.3200.2665821.01.341663e+092997.80True1.8866751.435720-163.0983881.737751NaNNaN
tm_2.33tm_2.3330.40459040.76218710.1367192012-07-07 12:17:45.1200.1678692.01.341663e+09245.44True1.2040780.954942138.5200751.142778NaNNaN
tm_6.0tm_6.030.31889640.6767192.6972662012-07-07 15:26:15.0801.0000006.01.341675e+0911309.96True4.8982082.005268177.7088183.423410NaNNaN
tm_3.14tm_3.1430.44511740.7643758.0546882012-07-07 16:50:34.8400.2552243.01.341680e+095059.76True2.9823861.933982-169.5562508.074210NaNNaN
tm_1.31tm_1.3130.41191440.79312511.3046882012-07-07 19:23:20.5600.1465811.01.341689e+099165.72True1.8866751.435720-163.0983881.737751NaNNaN
\n", "
" ], "text/plain": [ " event_id longitude latitude depth origin_time \\\n", "event_id \n", "tm_1.0 tm_1.0 30.399707 40.764062 10.136719 2012-07-07 06:56:02.200 \n", "tm_3.1 tm_3.1 30.445117 40.764375 8.054688 2012-07-07 06:56:52.160 \n", "tm_1.2 tm_1.2 30.404590 40.767187 10.238281 2012-07-07 07:07:45.440 \n", "tm_3.3 tm_3.3 30.445117 40.764375 8.054688 2012-07-07 07:09:59.680 \n", "tm_0.3 tm_0.3 30.404590 40.762187 10.136719 2012-07-07 07:10:12.440 \n", "tm_5.3 tm_5.3 30.397754 40.765937 10.644531 2012-07-07 07:10:20.000 \n", "tm_5.4 tm_5.4 30.398242 40.763125 10.289062 2012-07-07 07:10:39.160 \n", "tm_1.4 tm_1.4 30.413867 40.761875 10.898438 2012-07-07 07:10:53.640 \n", "tm_3.4 tm_3.4 30.413867 40.760625 10.898438 2012-07-07 07:11:07.280 \n", "tm_5.6 tm_5.6 30.407520 40.762812 10.136719 2012-07-07 07:11:36.080 \n", "tm_0.8 tm_0.8 30.406543 40.762812 10.238281 2012-07-07 07:12:06.200 \n", "tm_1.7 tm_1.7 30.400684 40.763437 10.136719 2012-07-07 07:14:25.000 \n", "tm_1.8 tm_1.8 30.411914 40.793125 11.304688 2012-07-07 07:15:44.880 \n", "tm_5.9 tm_5.9 30.397754 40.765937 10.644531 2012-07-07 07:18:17.040 \n", "tm_0.11 tm_0.11 30.404590 40.762187 10.136719 2012-07-07 07:22:16.240 \n", "tm_0.12 tm_0.12 30.402148 40.744375 6.632812 2012-07-07 07:22:43.880 \n", "tm_3.6 tm_3.6 30.393359 40.753750 2.671875 2012-07-07 07:23:08.320 \n", "tm_1.9 tm_1.9 30.396777 40.769062 10.746094 2012-07-07 07:24:09.560 \n", "tm_5.11 tm_5.11 30.400684 40.765312 10.746094 2012-07-07 07:24:34.000 \n", "tm_1.11 tm_1.11 30.411914 40.793125 11.304688 2012-07-07 07:26:09.920 \n", "tm_1.12 tm_1.12 30.314746 40.715938 0.996094 2012-07-07 07:27:21.560 \n", "tm_5.13 tm_5.13 30.397754 40.765937 10.644531 2012-07-07 07:29:48.920 \n", "tm_3.8 tm_3.8 30.445605 40.765312 8.003906 2012-07-07 07:34:42.600 \n", "tm_3.9 tm_3.9 30.445117 40.764375 8.054688 2012-07-07 07:35:58.000 \n", "tm_3.10 tm_3.10 30.445117 40.764375 8.054688 2012-07-07 07:39:17.400 \n", "tm_3.11 tm_3.11 30.445117 40.764375 8.054688 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09:27:10.120 \n", "tm_1.25 tm_1.25 30.411914 40.793125 11.304688 2012-07-07 09:42:13.440 \n", "tm_2.27 tm_2.27 30.404590 40.762187 10.136719 2012-07-07 09:46:04.920 \n", "tm_5.26 tm_5.26 30.397754 40.765937 10.644531 2012-07-07 09:59:27.240 \n", "tm_1.27 tm_1.27 30.411914 40.793125 11.304688 2012-07-07 10:05:20.680 \n", "tm_4.0 tm_4.0 30.322070 40.759375 2.164062 2012-07-07 10:16:39.800 \n", "tm_5.27 tm_5.27 30.427539 40.764375 9.273438 2012-07-07 10:41:34.400 \n", "tm_0.30 tm_0.30 30.404590 40.762187 10.136719 2012-07-07 11:15:32.640 \n", "tm_0.31 tm_0.31 30.404590 40.762187 10.136719 2012-07-07 11:23:41.520 \n", "tm_1.30 tm_1.30 30.331348 40.719062 0.996094 2012-07-07 12:13:39.320 \n", "tm_2.33 tm_2.33 30.404590 40.762187 10.136719 2012-07-07 12:17:45.120 \n", "tm_6.0 tm_6.0 30.318896 40.676719 2.697266 2012-07-07 15:26:15.080 \n", "tm_3.14 tm_3.14 30.445117 40.764375 8.054688 2012-07-07 16:50:34.840 \n", "tm_1.31 tm_1.31 30.411914 40.793125 11.304688 2012-07-07 19:23:20.560 \n", "\n", " cc tid origin_time_sec interevent_time_sec unique_event \\\n", "event_id \n", "tm_1.0 0.432075 1.0 1.341644e+09 0.00 True \n", "tm_3.1 0.246272 3.0 1.341644e+09 0.04 True \n", "tm_1.2 0.403376 1.0 1.341645e+09 652.96 True \n", "tm_3.3 0.239296 3.0 1.341645e+09 133.84 True \n", "tm_0.3 0.257793 0.0 1.341645e+09 0.36 True \n", "tm_5.3 0.402187 5.0 1.341645e+09 7.56 True \n", "tm_5.4 0.377112 5.0 1.341645e+09 19.12 True \n", "tm_1.4 0.394400 1.0 1.341645e+09 13.60 True \n", "tm_3.4 0.681143 3.0 1.341645e+09 0.08 True \n", "tm_5.6 0.181182 5.0 1.341645e+09 28.52 True \n", "tm_0.8 1.000000 0.0 1.341645e+09 0.04 True \n", "tm_1.7 1.000000 1.0 1.341645e+09 138.80 True \n", "tm_1.8 0.183907 1.0 1.341645e+09 79.52 True \n", "tm_5.9 0.398268 5.0 1.341645e+09 152.16 True \n", "tm_0.11 0.178471 0.0 1.341646e+09 239.16 True \n", "tm_0.12 0.169656 0.0 1.341646e+09 27.64 True \n", "tm_3.6 0.491056 3.0 1.341646e+09 24.44 True \n", "tm_1.9 0.188800 1.0 1.341646e+09 60.96 True \n", "tm_5.11 1.000000 5.0 1.341646e+09 0.24 True \n", "tm_1.11 0.211587 1.0 1.341646e+09 95.88 True \n", "tm_1.12 0.244670 1.0 1.341646e+09 71.64 True \n", "tm_5.13 0.210121 5.0 1.341646e+09 147.00 True \n", "tm_3.8 0.424897 3.0 1.341646e+09 0.04 True \n", "tm_3.9 0.260982 3.0 1.341647e+09 75.08 True \n", "tm_3.10 0.202312 3.0 1.341647e+09 199.16 True \n", "tm_3.11 0.199350 3.0 1.341648e+09 1265.96 True \n", "tm_1.14 0.158986 1.0 1.341649e+09 623.20 True \n", "tm_0.17 0.246277 0.0 1.341649e+09 0.04 True \n", "tm_0.18 0.563850 0.0 1.341649e+09 0.04 True \n", "tm_1.17 0.218191 1.0 1.341650e+09 772.24 True \n", "tm_2.19 0.304813 2.0 1.341650e+09 0.08 True \n", "tm_1.19 0.225189 1.0 1.341651e+09 248.04 True \n", "tm_2.21 0.321005 2.0 1.341651e+09 0.08 True \n", "tm_1.21 0.407170 1.0 1.341651e+09 26.20 True \n", "tm_3.12 1.000000 3.0 1.341651e+09 0.08 True \n", "tm_5.21 0.390236 5.0 1.341652e+09 0.28 True \n", "tm_5.22 0.152766 5.0 1.341652e+09 54.96 True \n", "tm_1.24 0.693933 1.0 1.341653e+09 556.44 True \n", "tm_5.24 0.332328 5.0 1.341653e+09 417.68 True \n", "tm_1.25 0.206876 1.0 1.341654e+09 903.28 True \n", "tm_2.27 0.341435 2.0 1.341654e+09 0.08 True \n", "tm_5.26 0.186767 5.0 1.341655e+09 802.32 True \n", "tm_1.27 0.159305 1.0 1.341656e+09 353.36 True \n", "tm_4.0 1.000000 4.0 1.341656e+09 679.12 True \n", "tm_5.27 0.335804 5.0 1.341658e+09 0.28 True \n", "tm_0.30 0.298780 0.0 1.341660e+09 0.08 True \n", "tm_0.31 0.385650 0.0 1.341660e+09 0.04 True \n", "tm_1.30 0.266582 1.0 1.341663e+09 2997.80 True \n", "tm_2.33 0.167869 2.0 1.341663e+09 245.44 True \n", "tm_6.0 1.000000 6.0 1.341675e+09 11309.96 True \n", "tm_3.14 0.255224 3.0 1.341680e+09 5059.76 True \n", "tm_1.31 0.146581 1.0 1.341689e+09 9165.72 True \n", "\n", " hmax_unc hmin_unc az_hmax_unc vmax_unc Mw Mw_err \n", "event_id \n", "tm_1.0 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_3.1 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_1.2 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_3.3 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_0.3 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_5.3 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_5.4 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_1.4 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_3.4 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_5.6 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_0.8 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_1.7 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_1.8 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_5.9 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_0.11 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_0.12 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_3.6 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_1.9 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_5.11 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_1.11 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_1.12 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_5.13 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_3.8 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_3.9 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_3.10 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_3.11 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_1.14 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_0.17 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_0.18 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_1.17 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_2.19 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_1.19 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_2.21 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_1.21 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_3.12 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_5.21 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_5.22 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_1.24 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_5.24 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_1.25 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_2.27 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_5.26 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_1.27 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_4.0 3.378762 1.691182 -143.772340 2.733743 NaN NaN \n", "tm_5.27 1.477016 1.028668 137.014524 1.263067 NaN NaN \n", "tm_0.30 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_0.31 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_1.30 1.886675 1.435720 -163.098388 1.737751 NaN NaN \n", "tm_2.33 1.204078 0.954942 138.520075 1.142778 NaN NaN \n", "tm_6.0 4.898208 2.005268 177.708818 3.423410 NaN NaN \n", "tm_3.14 2.982386 1.933982 -169.556250 8.074210 NaN NaN \n", "tm_1.31 1.886675 1.435720 -163.098388 1.737751 NaN NaN " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_catalog.set_index(\"event_id\", drop=False, inplace=True)\n", "final_catalog" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Compute earthquake magnitudes\n", "\n", "This section is still work-in-progress! The goal is to use the moment magnitudes $M_w$ that we computed in notebook 7 and the waveform peak amplitudes that we extracted during template matching in notebook 9 to compute local magnitudes for more earthquakes." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "for tp in template_group.templates:\n", " if \"Mw\" in tp.aux_data:\n", " self_detection = (final_catalog[\"tid\"] == float(tp.tid)) & (final_catalog[\"cc\"] > 0.999)\n", " final_catalog.loc[self_detection, \"Mw\"] = tp.aux_data[\"Mw\"]\n", " final_catalog.loc[self_detection, \"Mw_err\"] = tp.aux_data[\"Mw_err\"]\n", "# final_catalog" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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origin_timelongitudelatitudedepthhmax_uncvmax_uncMwMl
event_id
tm_1.02012-07-07 06:56:02.20030.39970740.76406210.1367191.8866751.737751NaN2.586553
tm_3.12012-07-07 06:56:52.16030.44511740.7643758.0546882.9823868.074210NaNNaN
tm_1.22012-07-07 07:07:45.44030.40459040.76718710.2382811.8866751.737751NaN4.005050
tm_3.32012-07-07 07:09:59.68030.44511740.7643758.0546882.9823868.074210NaNNaN
tm_0.32012-07-07 07:10:12.44030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_5.32012-07-07 07:10:20.00030.39775440.76593710.6445311.4770161.263067NaN1.087575
tm_5.42012-07-07 07:10:39.16030.39824240.76312510.2890621.4770161.263067NaN1.145118
tm_1.42012-07-07 07:10:53.64030.41386740.76187510.8984381.8866751.737751NaN1.165784
tm_3.42012-07-07 07:11:07.28030.41386740.76062510.8984382.9823868.074210NaNNaN
tm_5.62012-07-07 07:11:36.08030.40752040.76281210.1367191.4770161.263067NaN1.124363
tm_0.82012-07-07 07:12:06.20030.40654340.76281210.2382811.2040781.142778NaNNaN
tm_1.72012-07-07 07:14:25.00030.40068440.76343710.1367191.8866751.7377512.8094262.809426
tm_1.82012-07-07 07:15:44.88030.41191440.79312511.3046881.8866751.737751NaN1.251298
tm_5.92012-07-07 07:18:17.04030.39775440.76593710.6445311.4770161.263067NaN0.990920
tm_0.112012-07-07 07:22:16.24030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_0.122012-07-07 07:22:43.88030.40214840.7443756.6328121.2040781.142778NaNNaN
tm_3.62012-07-07 07:23:08.32030.39335940.7537502.6718752.9823868.074210NaNNaN
tm_1.92012-07-07 07:24:09.56030.39677740.76906210.7460941.8866751.737751NaN1.027156
tm_5.112012-07-07 07:24:34.00030.40068440.76531210.7460941.4770161.2630672.6046102.604610
tm_1.112012-07-07 07:26:09.92030.41191440.79312511.3046881.8866751.737751NaN1.171287
tm_1.122012-07-07 07:27:21.56030.31474640.7159380.9960941.8866751.737751NaN0.869616
tm_5.132012-07-07 07:29:48.92030.39775440.76593710.6445311.4770161.263067NaN1.004545
tm_3.82012-07-07 07:34:42.60030.44560540.7653128.0039062.9823868.074210NaNNaN
tm_3.92012-07-07 07:35:58.00030.44511740.7643758.0546882.9823868.074210NaNNaN
tm_3.102012-07-07 07:39:17.40030.44511740.7643758.0546882.9823868.074210NaNNaN
tm_3.112012-07-07 08:00:23.36030.44511740.7643758.0546882.9823868.074210NaNNaN
tm_1.142012-07-07 08:10:46.56030.41191440.79312511.3046881.8866751.737751NaN1.096352
tm_0.172012-07-07 08:15:48.88030.33842840.7179691.0214841.2040781.142778NaNNaN
tm_0.182012-07-07 08:17:35.00030.42949240.7631258.4609381.2040781.142778NaNNaN
tm_1.172012-07-07 08:30:27.24030.41191440.79312511.3046881.8866751.737751NaN1.085124
tm_2.192012-07-07 08:41:19.56030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_1.192012-07-07 08:45:27.60030.41191440.79312511.3046881.8866751.737751NaN1.000992
tm_2.212012-07-07 08:46:34.36030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_1.212012-07-07 08:47:00.56030.41191440.79312511.3046881.8866751.737751NaN1.066457
tm_3.122012-07-07 08:48:43.80030.44121140.7643758.2578122.9823868.074210NaNNaN
tm_5.212012-07-07 09:10:00.60030.39775440.76593710.6445311.4770161.263067NaN1.246354
tm_5.222012-07-07 09:10:55.64030.39775440.76593710.6445311.4770161.263067NaN0.934968
tm_1.242012-07-07 09:20:12.08030.40263740.7634379.9335941.8866751.737751NaN2.351699
tm_5.242012-07-07 09:27:10.12030.35332040.6706251.9609381.4770161.263067NaN1.231549
tm_1.252012-07-07 09:42:13.44030.41191440.79312511.3046881.8866751.737751NaN0.972295
tm_2.272012-07-07 09:46:04.92030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_5.262012-07-07 09:59:27.24030.39775440.76593710.6445311.4770161.263067NaN0.991324
tm_1.272012-07-07 10:05:20.68030.41191440.79312511.3046881.8866751.737751NaN0.886426
tm_4.02012-07-07 10:16:39.80030.32207040.7593752.1640623.3787622.733743NaNNaN
tm_5.272012-07-07 10:41:34.40030.42753940.7643759.2734381.4770161.263067NaN1.485605
tm_0.302012-07-07 11:15:32.64030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_0.312012-07-07 11:23:41.52030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_1.302012-07-07 12:13:39.32030.33134840.7190620.9960941.8866751.737751NaN1.012254
tm_2.332012-07-07 12:17:45.12030.40459040.76218710.1367191.2040781.142778NaNNaN
tm_6.02012-07-07 15:26:15.08030.31889640.6767192.6972664.8982083.423410NaNNaN
tm_3.142012-07-07 16:50:34.84030.44511740.7643758.0546882.9823868.074210NaNNaN
tm_1.312012-07-07 19:23:20.56030.41191440.79312511.3046881.8866751.737751NaN0.854499
\n", "
" ], "text/plain": [ " origin_time longitude latitude depth hmax_unc \\\n", "event_id \n", "tm_1.0 2012-07-07 06:56:02.200 30.399707 40.764062 10.136719 1.886675 \n", "tm_3.1 2012-07-07 06:56:52.160 30.445117 40.764375 8.054688 2.982386 \n", "tm_1.2 2012-07-07 07:07:45.440 30.404590 40.767187 10.238281 1.886675 \n", "tm_3.3 2012-07-07 07:09:59.680 30.445117 40.764375 8.054688 2.982386 \n", "tm_0.3 2012-07-07 07:10:12.440 30.404590 40.762187 10.136719 1.204078 \n", "tm_5.3 2012-07-07 07:10:20.000 30.397754 40.765937 10.644531 1.477016 \n", "tm_5.4 2012-07-07 07:10:39.160 30.398242 40.763125 10.289062 1.477016 \n", "tm_1.4 2012-07-07 07:10:53.640 30.413867 40.761875 10.898438 1.886675 \n", "tm_3.4 2012-07-07 07:11:07.280 30.413867 40.760625 10.898438 2.982386 \n", "tm_5.6 2012-07-07 07:11:36.080 30.407520 40.762812 10.136719 1.477016 \n", "tm_0.8 2012-07-07 07:12:06.200 30.406543 40.762812 10.238281 1.204078 \n", "tm_1.7 2012-07-07 07:14:25.000 30.400684 40.763437 10.136719 1.886675 \n", "tm_1.8 2012-07-07 07:15:44.880 30.411914 40.793125 11.304688 1.886675 \n", "tm_5.9 2012-07-07 07:18:17.040 30.397754 40.765937 10.644531 1.477016 \n", "tm_0.11 2012-07-07 07:22:16.240 30.404590 40.762187 10.136719 1.204078 \n", "tm_0.12 2012-07-07 07:22:43.880 30.402148 40.744375 6.632812 1.204078 \n", "tm_3.6 2012-07-07 07:23:08.320 30.393359 40.753750 2.671875 2.982386 \n", "tm_1.9 2012-07-07 07:24:09.560 30.396777 40.769062 10.746094 1.886675 \n", "tm_5.11 2012-07-07 07:24:34.000 30.400684 40.765312 10.746094 1.477016 \n", "tm_1.11 2012-07-07 07:26:09.920 30.411914 40.793125 11.304688 1.886675 \n", "tm_1.12 2012-07-07 07:27:21.560 30.314746 40.715938 0.996094 1.886675 \n", "tm_5.13 2012-07-07 07:29:48.920 30.397754 40.765937 10.644531 1.477016 \n", "tm_3.8 2012-07-07 07:34:42.600 30.445605 40.765312 8.003906 2.982386 \n", "tm_3.9 2012-07-07 07:35:58.000 30.445117 40.764375 8.054688 2.982386 \n", "tm_3.10 2012-07-07 07:39:17.400 30.445117 40.764375 8.054688 2.982386 \n", "tm_3.11 2012-07-07 08:00:23.360 30.445117 40.764375 8.054688 2.982386 \n", "tm_1.14 2012-07-07 08:10:46.560 30.411914 40.793125 11.304688 1.886675 \n", "tm_0.17 2012-07-07 08:15:48.880 30.338428 40.717969 1.021484 1.204078 \n", "tm_0.18 2012-07-07 08:17:35.000 30.429492 40.763125 8.460938 1.204078 \n", "tm_1.17 2012-07-07 08:30:27.240 30.411914 40.793125 11.304688 1.886675 \n", "tm_2.19 2012-07-07 08:41:19.560 30.404590 40.762187 10.136719 1.204078 \n", "tm_1.19 2012-07-07 08:45:27.600 30.411914 40.793125 11.304688 1.886675 \n", "tm_2.21 2012-07-07 08:46:34.360 30.404590 40.762187 10.136719 1.204078 \n", "tm_1.21 2012-07-07 08:47:00.560 30.411914 40.793125 11.304688 1.886675 \n", "tm_3.12 2012-07-07 08:48:43.800 30.441211 40.764375 8.257812 2.982386 \n", "tm_5.21 2012-07-07 09:10:00.600 30.397754 40.765937 10.644531 1.477016 \n", "tm_5.22 2012-07-07 09:10:55.640 30.397754 40.765937 10.644531 1.477016 \n", "tm_1.24 2012-07-07 09:20:12.080 30.402637 40.763437 9.933594 1.886675 \n", "tm_5.24 2012-07-07 09:27:10.120 30.353320 40.670625 1.960938 1.477016 \n", "tm_1.25 2012-07-07 09:42:13.440 30.411914 40.793125 11.304688 1.886675 \n", "tm_2.27 2012-07-07 09:46:04.920 30.404590 40.762187 10.136719 1.204078 \n", "tm_5.26 2012-07-07 09:59:27.240 30.397754 40.765937 10.644531 1.477016 \n", "tm_1.27 2012-07-07 10:05:20.680 30.411914 40.793125 11.304688 1.886675 \n", "tm_4.0 2012-07-07 10:16:39.800 30.322070 40.759375 2.164062 3.378762 \n", "tm_5.27 2012-07-07 10:41:34.400 30.427539 40.764375 9.273438 1.477016 \n", "tm_0.30 2012-07-07 11:15:32.640 30.404590 40.762187 10.136719 1.204078 \n", "tm_0.31 2012-07-07 11:23:41.520 30.404590 40.762187 10.136719 1.204078 \n", "tm_1.30 2012-07-07 12:13:39.320 30.331348 40.719062 0.996094 1.886675 \n", "tm_2.33 2012-07-07 12:17:45.120 30.404590 40.762187 10.136719 1.204078 \n", "tm_6.0 2012-07-07 15:26:15.080 30.318896 40.676719 2.697266 4.898208 \n", "tm_3.14 2012-07-07 16:50:34.840 30.445117 40.764375 8.054688 2.982386 \n", "tm_1.31 2012-07-07 19:23:20.560 30.411914 40.793125 11.304688 1.886675 \n", "\n", " vmax_unc Mw Ml \n", "event_id \n", "tm_1.0 1.737751 NaN 2.586553 \n", "tm_3.1 8.074210 NaN NaN \n", "tm_1.2 1.737751 NaN 4.005050 \n", "tm_3.3 8.074210 NaN NaN \n", "tm_0.3 1.142778 NaN NaN \n", "tm_5.3 1.263067 NaN 1.087575 \n", "tm_5.4 1.263067 NaN 1.145118 \n", "tm_1.4 1.737751 NaN 1.165784 \n", "tm_3.4 8.074210 NaN NaN \n", "tm_5.6 1.263067 NaN 1.124363 \n", "tm_0.8 1.142778 NaN NaN \n", "tm_1.7 1.737751 2.809426 2.809426 \n", "tm_1.8 1.737751 NaN 1.251298 \n", "tm_5.9 1.263067 NaN 0.990920 \n", "tm_0.11 1.142778 NaN NaN \n", "tm_0.12 1.142778 NaN NaN \n", "tm_3.6 8.074210 NaN NaN \n", "tm_1.9 1.737751 NaN 1.027156 \n", "tm_5.11 1.263067 2.604610 2.604610 \n", "tm_1.11 1.737751 NaN 1.171287 \n", "tm_1.12 1.737751 NaN 0.869616 \n", "tm_5.13 1.263067 NaN 1.004545 \n", "tm_3.8 8.074210 NaN NaN \n", "tm_3.9 8.074210 NaN NaN \n", "tm_3.10 8.074210 NaN NaN \n", "tm_3.11 8.074210 NaN NaN \n", "tm_1.14 1.737751 NaN 1.096352 \n", "tm_0.17 1.142778 NaN NaN \n", "tm_0.18 1.142778 NaN NaN \n", "tm_1.17 1.737751 NaN 1.085124 \n", "tm_2.19 1.142778 NaN NaN \n", "tm_1.19 1.737751 NaN 1.000992 \n", "tm_2.21 1.142778 NaN NaN \n", "tm_1.21 1.737751 NaN 1.066457 \n", "tm_3.12 8.074210 NaN NaN \n", "tm_5.21 1.263067 NaN 1.246354 \n", "tm_5.22 1.263067 NaN 0.934968 \n", "tm_1.24 1.737751 NaN 2.351699 \n", "tm_5.24 1.263067 NaN 1.231549 \n", "tm_1.25 1.737751 NaN 0.972295 \n", "tm_2.27 1.142778 NaN NaN \n", "tm_5.26 1.263067 NaN 0.991324 \n", "tm_1.27 1.737751 NaN 0.886426 \n", "tm_4.0 2.733743 NaN NaN \n", "tm_5.27 1.263067 NaN 1.485605 \n", "tm_0.30 1.142778 NaN NaN \n", "tm_0.31 1.142778 NaN NaN \n", "tm_1.30 1.737751 NaN 1.012254 \n", "tm_2.33 1.142778 NaN NaN \n", "tm_6.0 3.423410 NaN NaN \n", "tm_3.14 8.074210 NaN NaN \n", "tm_1.31 1.737751 NaN 0.854499 " ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ref_events = final_catalog.dropna(subset=[\"Mw\"])\n", "ref_events\n", "for event_id in events:\n", " event = events[event_id]\n", " event_id = f\"tm_{event_id}\"\n", " tid = float(event.aux_data[\"tid\"])\n", " # print(tid)\n", " subset = ref_events.tid == tid\n", " if np.sum(subset) > 0:\n", " peak_amp = event.aux_data[\"peak_amplitudes\"]\n", " Mls = []\n", " for i, row in ref_events[subset].iterrows():\n", " peak_amp_ref = events[row.name[3:]].aux_data[\"peak_amplitudes\"]\n", " ratios = (peak_amp/peak_amp_ref).flatten()\n", " ratios = ratios[~np.isinf(ratios) & (ratios > 0.)]\n", " Ml_diff = np.median(np.log10(ratios))\n", " # Ml_i = row.Mw + 2./3. * Ml_diff\n", " Ml_i = row.Mw + Ml_diff\n", " Mls.append(Ml_i)\n", " final_catalog.loc[event_id, \"Ml\"] = np.median(Mls)\n", "final_catalog[[\"origin_time\", \"longitude\", \"latitude\", \"depth\", \"hmax_unc\", \"vmax_unc\", \"Mw\", \"Ml\"]]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 ('hy7_py310')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "221f0e5b1b98151b07a79bf3b6d0c1d306576197d2c4531763770570a29e708e" } } }, "nbformat": 4, "nbformat_minor": 2 }