Build Earthquake Catalog

In this final notebook, we read the matched-filter database, remove the multiple detections and write a clean earthquake catalog in a csv file.

[1]:

import os
n_CPUs = 12
os.environ["OMP_NUM_THREADS"] = str(n_CPUs)

import BPMF
import glob
import h5py as h5
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys

from tqdm import tqdm
from time import time as give_time

[2]:

# PROGRAM PARAMETERS
NETWORK_FILENAME = "network.csv"
TEMPLATE_DB = "template_db"
MATCHED_FILTER_DB = "matched_filter_db"
CHECK_SUMMARY_FILE = False
PATH_MF = os.path.join(BPMF.cfg.OUTPUT_PATH, MATCHED_FILTER_DB)
DATA_FOLDER = "preprocessed_2_12"

[3]:

# read network metadata
net = BPMF.dataset.Network(NETWORK_FILENAME)
net.read()

Read the detected events’ metadata for each template

[4]:

# template filenames
template_filenames = glob.glob(os.path.join(BPMF.cfg.OUTPUT_PATH, TEMPLATE_DB, "template*"))
template_filenames.sort()

# initialize the template group
template_group = BPMF.dataset.TemplateGroup.read_from_files(template_filenames, net)
template_group.read_catalog(
    extra_attributes=["cc"],
    progress=True,
    db_path=PATH_MF,
    check_summary_file=CHECK_SUMMARY_FILE,
)

Reading catalog: 100%|██████████| 7/7 [00:00<00:00, 13.37it/s]

The BPMF.dataset.TemplateGroup now has a catalog attribute, which is a BPMF.dataset.Catalog instance.

[5]:

template_group.catalog

[5]:

<BPMF.dataset.Catalog at 0x7f9f414557b0>

[6]:

template_group.catalog.catalog

[6]:

	longitude	latitude	depth	origin_time	cc	tid
event_id
1.0	30.411914	40.793125	11.304688	2012-07-07 06:56:02.200	0.432075	1
3.0	30.445117	40.764375	8.054688	2012-07-07 06:56:02.280	0.289950	3
5.0	30.397754	40.765937	10.644531	2012-07-07 06:56:02.520	0.331015	5
2.0	30.404590	40.762187	10.136719	2012-07-07 06:56:02.560	0.404524	2
0.0	30.404590	40.762187	10.136719	2012-07-07 06:56:02.560	0.404524	0
...	...	...	...	...	...	...
2.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	2
0.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	0
6.0	30.294482	40.628281	-1.517578	2012-07-07 15:26:15.080	1.000000	6
3.14	30.445117	40.764375	8.054688	2012-07-07 16:50:34.840	0.255224	3
1.31	30.411914	40.793125	11.304688	2012-07-07 19:23:20.560	0.146581	1

148 rows × 6 columns

Remove the multiple detections

Remove multiple detections with the TemplateGroup.remove_multiples method.

[7]:

# DISTANCE_CRITERION_KM: Distance, in km, between two detected events (within uncertainties) below which
#                        detected events are investigated for equality.
DISTANCE_CRITERION_KM = 15.0
# DT_CRITERION_SEC: Inter-event time, in seconds, between two detected events below which
#                   detected events are investigated for redundancy.
DT_CRITERION_SEC = 4.0
# SIMILARITY_CRITERION: Inter-template correlation coefficient below which detected events are investigated for equality.
SIMILARITY_CRITERION = 0.10
# N_CLOSEST_STATIONS: When computing the inter-template correlation coefficient, use the N_CLOSEST_STATIONS closest stations
#                     of a given pair of templates. This parameter is relevant for studies with large seismic networks.
N_CLOSEST_STATIONS = 10

[8]:

# we need to read the waveforms first
template_group.read_waveforms()
template_group.normalize(method="rms")

[9]:

template_group.remove_multiples(
    n_closest_stations=N_CLOSEST_STATIONS,
    dt_criterion=DT_CRITERION_SEC,
    distance_criterion=DISTANCE_CRITERION_KM,
    similarity_criterion=SIMILARITY_CRITERION,
    progress=True,
)

/home/ebeauce/miniconda3/envs/hy7_py310/lib/python3.10/site-packages/BPMF/dataset.py:3452: RuntimeWarning: invalid value encountered in divide
  unit_direction /= np.sqrt(np.sum(unit_direction**2, axis=1))[

Computing the similarity matrix...
Computing the inter-template directional errors...
Searching for events detected by multiple templates
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

Removing multiples: 100%|██████████| 148/148 [00:00<00:00, 2651.52it/s]

0.06s to flag the multiples

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.

[10]:

template_group.catalog.catalog

[10]:

	longitude	latitude	depth	origin_time	cc	tid	origin_time_sec	interevent_time_sec	unique_event
event_id
1.0	30.411914	40.793125	11.304688	2012-07-07 06:56:02.200	0.432075	1	1.341644e+09	0.00	True
3.0	30.445117	40.764375	8.054688	2012-07-07 06:56:02.280	0.289950	3	1.341644e+09	0.08	False
5.0	30.397754	40.765937	10.644531	2012-07-07 06:56:02.520	0.331015	5	1.341644e+09	0.24	False
2.0	30.404590	40.762187	10.136719	2012-07-07 06:56:02.560	0.404524	2	1.341644e+09	0.04	False
0.0	30.404590	40.762187	10.136719	2012-07-07 06:56:02.560	0.404524	0	1.341644e+09	0.00	False
...	...	...	...	...	...	...	...	...	...
2.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	2	1.341663e+09	245.44	True
0.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	0	1.341663e+09	0.00	False
6.0	30.294482	40.628281	-1.517578	2012-07-07 15:26:15.080	1.000000	6	1.341675e+09	11309.96	True
3.14	30.445117	40.764375	8.054688	2012-07-07 16:50:34.840	0.255224	3	1.341680e+09	5059.76	True
1.31	30.411914	40.793125	11.304688	2012-07-07 19:23:20.560	0.146581	1	1.341689e+09	9165.72	True

148 rows × 9 columns

The final catalog is made of the unique events only.

[11]:

template_group.catalog.catalog = template_group.catalog.catalog[template_group.catalog.catalog["unique_event"]]

Let’s add the location uncertainties from the template events.

[12]:

for tp in template_group.templates:
    tid = tp.tid
    selection = template_group.catalog.catalog["tid"] == tid
    template_group.catalog.catalog.loc[selection, "hmax_unc"] = tp.hmax_unc
    template_group.catalog.catalog.loc[selection, "hmin_unc"] = tp.hmin_unc
    template_group.catalog.catalog.loc[selection, "az_hmax_unc"] = tp.az_hmax_unc
    template_group.catalog.catalog.loc[selection, "vmax_unc"] = tp.vmax_unc

/tmp/ipykernel_160176/1634894027.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  template_group.catalog.catalog.loc[selection, "hmax_unc"] = tp.hmax_unc
/tmp/ipykernel_160176/1634894027.py:5: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  template_group.catalog.catalog.loc[selection, "hmin_unc"] = tp.hmin_unc
/tmp/ipykernel_160176/1634894027.py:6: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  template_group.catalog.catalog.loc[selection, "az_hmax_unc"] = tp.az_hmax_unc
/tmp/ipykernel_160176/1634894027.py:7: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  template_group.catalog.catalog.loc[selection, "vmax_unc"] = tp.vmax_unc

[13]:

print(f"There are {len(template_group.catalog.catalog)} events in our template matching catalog!")
template_group.catalog.catalog

There are 52 events in our template matching catalog!

[13]:

	longitude	latitude	depth	origin_time	cc	tid	origin_time_sec	interevent_time_sec	unique_event	hmax_unc	hmin_unc	az_hmax_unc	vmax_unc
event_id
1.0	30.411914	40.793125	11.304688	2012-07-07 06:56:02.200	0.432075	1	1.341644e+09	0.00	True	1.886675	1.435720	-163.098388	1.737751
3.1	30.445117	40.764375	8.054688	2012-07-07 06:56:52.160	0.246272	3	1.341644e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210
1.2	30.411914	40.793125	11.304688	2012-07-07 07:07:45.440	0.403376	1	1.341645e+09	652.96	True	1.886675	1.435720	-163.098388	1.737751
3.3	30.445117	40.764375	8.054688	2012-07-07 07:09:59.680	0.239296	3	1.341645e+09	133.84	True	2.982386	1.933982	-169.556250	8.074210
0.3	30.404590	40.762187	10.136719	2012-07-07 07:10:12.440	0.257793	0	1.341645e+09	0.36	True	1.204078	0.954942	138.520075	1.142778
5.3	30.397754	40.765937	10.644531	2012-07-07 07:10:20.000	0.402187	5	1.341645e+09	7.56	True	1.477016	1.028668	137.014524	1.263067
5.4	30.397754	40.765937	10.644531	2012-07-07 07:10:39.160	0.377112	5	1.341645e+09	19.12	True	1.477016	1.028668	137.014524	1.263067
1.4	30.411914	40.793125	11.304688	2012-07-07 07:10:53.640	0.394400	1	1.341645e+09	13.60	True	1.886675	1.435720	-163.098388	1.737751
3.4	30.445117	40.764375	8.054688	2012-07-07 07:11:07.280	0.681143	3	1.341645e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210
5.6	30.397754	40.765937	10.644531	2012-07-07 07:11:36.080	0.181182	5	1.341645e+09	28.52	True	1.477016	1.028668	137.014524	1.263067
0.8	30.404590	40.762187	10.136719	2012-07-07 07:12:06.200	1.000000	0	1.341645e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
1.7	30.411914	40.793125	11.304688	2012-07-07 07:14:25.000	1.000000	1	1.341645e+09	138.80	True	1.886675	1.435720	-163.098388	1.737751
1.8	30.411914	40.793125	11.304688	2012-07-07 07:15:44.880	0.183907	1	1.341645e+09	79.52	True	1.886675	1.435720	-163.098388	1.737751
5.9	30.397754	40.765937	10.644531	2012-07-07 07:18:17.040	0.398268	5	1.341645e+09	152.16	True	1.477016	1.028668	137.014524	1.263067
0.11	30.404590	40.762187	10.136719	2012-07-07 07:22:16.240	0.178471	0	1.341646e+09	239.16	True	1.204078	0.954942	138.520075	1.142778
0.12	30.404590	40.762187	10.136719	2012-07-07 07:22:43.880	0.169656	0	1.341646e+09	27.64	True	1.204078	0.954942	138.520075	1.142778
3.6	30.445117	40.764375	8.054688	2012-07-07 07:23:08.320	0.491056	3	1.341646e+09	24.44	True	2.982386	1.933982	-169.556250	8.074210
1.9	30.411914	40.793125	11.304688	2012-07-07 07:24:09.560	0.188800	1	1.341646e+09	60.96	True	1.886675	1.435720	-163.098388	1.737751
5.11	30.397754	40.765937	10.644531	2012-07-07 07:24:34.000	1.000000	5	1.341646e+09	0.24	True	1.477016	1.028668	137.014524	1.263067
1.11	30.411914	40.793125	11.304688	2012-07-07 07:26:09.920	0.211587	1	1.341646e+09	95.88	True	1.886675	1.435720	-163.098388	1.737751
1.12	30.411914	40.793125	11.304688	2012-07-07 07:27:21.560	0.244670	1	1.341646e+09	71.64	True	1.886675	1.435720	-163.098388	1.737751
5.13	30.397754	40.765937	10.644531	2012-07-07 07:29:48.920	0.210121	5	1.341646e+09	147.00	True	1.477016	1.028668	137.014524	1.263067
3.8	30.445117	40.764375	8.054688	2012-07-07 07:34:42.600	0.424897	3	1.341646e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210
3.9	30.445117	40.764375	8.054688	2012-07-07 07:35:58.000	0.260982	3	1.341647e+09	75.08	True	2.982386	1.933982	-169.556250	8.074210
3.10	30.445117	40.764375	8.054688	2012-07-07 07:39:17.400	0.202312	3	1.341647e+09	199.16	True	2.982386	1.933982	-169.556250	8.074210
3.11	30.445117	40.764375	8.054688	2012-07-07 08:00:23.360	0.199350	3	1.341648e+09	1265.96	True	2.982386	1.933982	-169.556250	8.074210
1.14	30.411914	40.793125	11.304688	2012-07-07 08:10:46.560	0.158986	1	1.341649e+09	623.20	True	1.886675	1.435720	-163.098388	1.737751
0.17	30.404590	40.762187	10.136719	2012-07-07 08:15:48.880	0.246277	0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
0.18	30.404590	40.762187	10.136719	2012-07-07 08:17:35.000	0.563850	0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
1.17	30.411914	40.793125	11.304688	2012-07-07 08:30:27.240	0.218191	1	1.341650e+09	772.24	True	1.886675	1.435720	-163.098388	1.737751
2.19	30.404590	40.762187	10.136719	2012-07-07 08:41:19.560	0.304813	2	1.341650e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
1.19	30.411914	40.793125	11.304688	2012-07-07 08:45:27.600	0.225189	1	1.341651e+09	248.04	True	1.886675	1.435720	-163.098388	1.737751
2.21	30.404590	40.762187	10.136719	2012-07-07 08:46:34.360	0.321005	2	1.341651e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
1.21	30.411914	40.793125	11.304688	2012-07-07 08:47:00.560	0.407170	1	1.341651e+09	26.20	True	1.886675	1.435720	-163.098388	1.737751
3.12	30.445117	40.764375	8.054688	2012-07-07 08:48:43.800	1.000000	3	1.341651e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210
5.21	30.397754	40.765937	10.644531	2012-07-07 09:10:00.600	0.390236	5	1.341652e+09	0.28	True	1.477016	1.028668	137.014524	1.263067
5.22	30.397754	40.765937	10.644531	2012-07-07 09:10:55.640	0.152766	5	1.341652e+09	54.96	True	1.477016	1.028668	137.014524	1.263067
1.24	30.411914	40.793125	11.304688	2012-07-07 09:20:12.080	0.693933	1	1.341653e+09	556.44	True	1.886675	1.435720	-163.098388	1.737751
5.24	30.397754	40.765937	10.644531	2012-07-07 09:27:10.120	0.332328	5	1.341653e+09	417.68	True	1.477016	1.028668	137.014524	1.263067
1.25	30.411914	40.793125	11.304688	2012-07-07 09:42:13.440	0.206876	1	1.341654e+09	903.28	True	1.886675	1.435720	-163.098388	1.737751
2.27	30.404590	40.762187	10.136719	2012-07-07 09:46:04.920	0.341435	2	1.341654e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
5.26	30.397754	40.765937	10.644531	2012-07-07 09:59:27.240	0.186767	5	1.341655e+09	802.32	True	1.477016	1.028668	137.014524	1.263067
1.27	30.411914	40.793125	11.304688	2012-07-07 10:05:20.680	0.159305	1	1.341656e+09	353.36	True	1.886675	1.435720	-163.098388	1.737751
4.0	30.316699	40.754688	0.285156	2012-07-07 10:16:39.800	1.000000	4	1.341656e+09	679.12	True	3.378762	1.691182	-143.772340	2.733743
5.27	30.397754	40.765937	10.644531	2012-07-07 10:41:34.400	0.335804	5	1.341658e+09	0.28	True	1.477016	1.028668	137.014524	1.263067
0.30	30.404590	40.762187	10.136719	2012-07-07 11:15:32.640	0.298780	0	1.341660e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
0.31	30.404590	40.762187	10.136719	2012-07-07 11:23:41.520	0.385650	0	1.341660e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
1.30	30.411914	40.793125	11.304688	2012-07-07 12:13:39.320	0.266582	1	1.341663e+09	2997.80	True	1.886675	1.435720	-163.098388	1.737751
2.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	2	1.341663e+09	245.44	True	1.204078	0.954942	138.520075	1.142778
6.0	30.294482	40.628281	-1.517578	2012-07-07 15:26:15.080	1.000000	6	1.341675e+09	11309.96	True	4.898208	2.005268	177.708818	3.423410
3.14	30.445117	40.764375	8.054688	2012-07-07 16:50:34.840	0.255224	3	1.341680e+09	5059.76	True	2.982386	1.933982	-169.556250	8.074210
1.31	30.411914	40.793125	11.304688	2012-07-07 19:23:20.560	0.146581	1	1.341689e+09	9165.72	True	1.886675	1.435720	-163.098388	1.737751

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.

[14]:

fig = template_group.catalog.plot_map(
    figsize=(10, 10), network=net, s=50, markersize_station=50, lat_margin=0.02, plot_uncertainties=False
    )
ax = fig.get_axes()[0]
ax.set_facecolor("dimgrey")
ax.patch.set_alpha(0.15)

../../_images/tutorial_notebooks_10_build_earthquake_catalog_21_0.png

Bonus: Relocate each events

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.

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.

[15]:

import tensorflow as tf

from BPMF.data_reader_examples import data_reader_mseed

# this is necessary to limit the number of threads spawn by tf
os.environ["TF_NUM_INTRAOP_THREADS"] = str(n_CPUs)
os.environ["TF_NUM_INTEROP_THREADS"] = str(n_CPUs)
tf.config.threading.set_inter_op_parallelism_threads(n_CPUs)
tf.config.threading.set_intra_op_parallelism_threads(n_CPUs)
tf.config.set_soft_device_placement(True)

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
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.

[16]:

# PhaseNet picking parameters

# PhaseNet was trained for 100Hz data. Even if we saw that running PhaseNet on 25Hz data
# was good for backprojection, here, picking benefits from running PhaseNet on 100Hz data.
# Thus, we will upsample the waveforms before running PhaseNet.
PHASENET_SAMPLING_RATE_HZ = 100.
UPSAMPLING_BEFORE_PN_RELOCATION = int(PHASENET_SAMPLING_RATE_HZ/BPMF.cfg.SAMPLING_RATE_HZ)
DOWNSAMPLING_BEFORE_PN_RELOCATION = 1

# DURATION_SEC: the duration, in seconds, of the data stream starting at the detection time
#               defined by Event.origin_time. This data stream is used for picking the P/S waves.
DURATION_SEC = 60.0
# THRESHOLD_P: probability of P-wave arrival above which we declare a pick. If several picks are
#              declared during the DURATION_SEC data stream, we only keep the best one. We can
#              afford using a low probability threshold since we already know with some confidence
#              that an earthquake is in the data stream.
THRESHOLD_P = 0.10
# THRESHOLD_S: probability of S-wave arrival above which we declare a pick.
THRESHOLD_S = 0.10
# DATA_FOLDER: name of the folder where the waveforms we want to use for picking are stored
DATA_FOLDER = "preprocessed_2_12"
# COMPONENT_ALIASES: A dictionary that defines the possible channel names to search for
#                    for example, the seismometer might not be oriented and the horizontal channels
#                    might be named 1 and 2, in which case we arbitrarily decide to take 1 as the "N" channel
#                    and 2 as the "E" channel. This doesn't matter for picking P- and S-wave arrival times.
COMPONENT_ALIASES = {"N": ["N", "1"], "E": ["E", "2"], "Z": ["Z"]}
# PHASE_ON_COMP: dictionary defining which moveout we use to extract the waveform
PHASE_ON_COMP = {"N": "S", "1": "S", "E": "S", "2": "S", "Z": "P"}
# USE_APRIORI_PICKS: boolean. This option is IMPORTANT when running BPMF in HIGH SEISMICITY CONTEXTS, like
#                   during the aftershock sequence of a large earthquake. If there are many events happening
#                   close to each other in time, we need to guide PhaseNet to pick the right set of picks.
#                   For that, we use the predicted P- and S-wave times from backprojection to add extra weight to
#                   the picks closer to those times and make it more likely to identify them as the "best" picks.
#                   WARNING: If there are truly many events, even this trick might fail. It's because "phase association"
#                   is an intrinsically hard problem in this case, and the picking might be hard to do automatically.
USE_APRIORI_PICKS = True

# MAX_HORIZONTAL_UNC_KM: Horizontal location uncertainty, in km, above which we keep the template location
MAX_HORIZONTAL_UNC_KM = 10.

# location parameters

LOCATION_ROUTINE = "NLLoc"
# NLLOC_METHOD: string that defines what loss function is used by NLLoc, see http://alomax.free.fr/nlloc/ for more info.
#               Using some flavor of 'EDT' is important to obtain robust locations that are not sensitive to pick outliers.
NLLOC_METHOD = "EDT"
# MINIMUM_NUM_STATIONS_W_PICKS: minimum number of stations with picks to even try relocation.
MINIMUM_NUM_STATIONS_W_PICKS = 3
# we set a maximum tolerable difference, in percentage, between the picked time and the predicted travel time
MAX_TIME_DIFFERENT_PICKS_PREDICTED_PERCENT = 10

[17]:

events = {}
for idx, row in template_group.catalog.catalog.iterrows():
    tid, evidx = row.name.split(".")
    # get the template instance from template_group
    template = template_group.templates[template_group.tindexes.loc[int(tid)]]
    # this is the filename of the database where template tid's detected events were stored
    detection_db_filename = f"detections_template{tid}.h5"
    db_path = os.path.join(BPMF.cfg.OUTPUT_PATH, MATCHED_FILTER_DB)
    with h5.File(os.path.join(db_path, detection_db_filename), mode="r") as fdet:
        keys = list(fdet.keys())
        event = BPMF.dataset.Event.read_from_file(
            hdf5_file=fdet[keys[int(evidx)]], data_reader=data_reader_mseed
            )
    # # attach data reader this way (note: conflict with data_reader argument in phasenet's wrapper module)
    # event.data_reader = data_reader_mseed
    # pick P-/S-wave arrivals
    event.pick_PS_phases(
        DURATION_SEC,
        phase_on_comp=PHASE_ON_COMP,
        threshold_P=THRESHOLD_P,
        threshold_S=THRESHOLD_S,
        component_aliases=COMPONENT_ALIASES,
        inter_op_parallelism_threads=n_CPUs,
        intra_op_parallelism_threads=n_CPUs,
        data_folder=DATA_FOLDER,
        upsampling=UPSAMPLING_BEFORE_PN_RELOCATION,
        downsampling=DOWNSAMPLING_BEFORE_PN_RELOCATION,
        use_apriori_picks=USE_APRIORI_PICKS,
    )

    if len(event.picks.dropna(how="all")) >= MINIMUM_NUM_STATIONS_W_PICKS:
        # first relocation, insensitive to outliers
        event.relocate(
            stations=net.stations, routine=LOCATION_ROUTINE, method=NLLOC_METHOD,
        )
    if "NLLoc_reloc" in event.aux_data:
        # this variable was inserted into ev.aux_data if NLLoc successfully located the event
        # use predicted times to remove outlier picks
        event.remove_outlier_picks(max_diff_percent=MAX_TIME_DIFFERENT_PICKS_PREDICTED_PERCENT)
        if len(event.picks.dropna(how="all")) >= MINIMUM_NUM_STATIONS_W_PICKS:
            # first relocation, insensitive to outliers
            event.relocate(
                stations=net.stations, routine=LOCATION_ROUTINE, method=NLLOC_METHOD,
            )
        else:
            del event.aux_data["NLLoc_reloc"]
    events[row.name] = event
    if ("NLLoc_reloc" in event.aux_data) and (event.hmax_unc) < MAX_HORIZONTAL_UNC_KM:
        template_group.catalog.catalog.loc[row.name, "longitude"] = event.longitude
        template_group.catalog.catalog.loc[row.name, "latitude"] = event.latitude
        template_group.catalog.catalog.loc[row.name, "depth"] = event.depth

n events: 1, n stations: 8, batch size (n events x n stations): 8

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
2023-03-08 16:11:25.822047: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
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
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
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-08 16:11:26.534948: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
Pred: 100%|██████████| 1/1 [00:00<00:00,  4.32it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.37it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  4.79it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.25it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.29it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.56it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.36it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.44it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.89it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  3.87it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.72it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.49it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.48it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.35it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.24it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.31it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.30it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.47it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.53it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.51it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.18it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.46it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.31it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.03it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.19it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.18it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.65it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  4.21it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.29it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.33it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.33it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.34it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.27it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.57it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.45it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.48it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.38it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.21it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  3.96it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.46it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.37it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.31it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.54it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.54it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.74it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.55it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.11it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.36it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.44it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.15it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.65it/s]

n events: 1, n stations: 8, batch size (n events x n stations): 8

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.49it/s]

[18]:

fig = template_group.catalog.plot_map(
    figsize=(10, 10), network=net, s=50, markersize_station=50, lat_margin=0.02, plot_uncertainties=False
    )
ax = fig.get_axes()[0]
ax.set_facecolor("dimgrey")
ax.patch.set_alpha(0.15)

../../_images/tutorial_notebooks_10_build_earthquake_catalog_26_0.png

[19]:

template_group.catalog.catalog

[19]:

	longitude	latitude	depth	origin_time	cc	tid	origin_time_sec	interevent_time_sec	unique_event	hmax_unc	hmin_unc	az_hmax_unc	vmax_unc
event_id
1.0	30.399707	40.764062	10.136719	2012-07-07 06:56:02.200	0.432075	1	1.341644e+09	0.00	True	1.886675	1.435720	-163.098388	1.737751
3.1	30.445117	40.764375	8.054688	2012-07-07 06:56:52.160	0.246272	3	1.341644e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210
1.2	30.404590	40.767187	10.238281	2012-07-07 07:07:45.440	0.403376	1	1.341645e+09	652.96	True	1.886675	1.435720	-163.098388	1.737751
3.3	30.445117	40.764375	8.054688	2012-07-07 07:09:59.680	0.239296	3	1.341645e+09	133.84	True	2.982386	1.933982	-169.556250	8.074210
0.3	30.404590	40.762187	10.136719	2012-07-07 07:10:12.440	0.257793	0	1.341645e+09	0.36	True	1.204078	0.954942	138.520075	1.142778
5.3	30.397754	40.765937	10.644531	2012-07-07 07:10:20.000	0.402187	5	1.341645e+09	7.56	True	1.477016	1.028668	137.014524	1.263067
5.4	30.398242	40.763125	10.289062	2012-07-07 07:10:39.160	0.377112	5	1.341645e+09	19.12	True	1.477016	1.028668	137.014524	1.263067
1.4	30.413867	40.761875	10.898438	2012-07-07 07:10:53.640	0.394400	1	1.341645e+09	13.60	True	1.886675	1.435720	-163.098388	1.737751
3.4	30.413867	40.760625	10.898438	2012-07-07 07:11:07.280	0.681143	3	1.341645e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210
5.6	30.407520	40.762812	10.136719	2012-07-07 07:11:36.080	0.181182	5	1.341645e+09	28.52	True	1.477016	1.028668	137.014524	1.263067
0.8	30.406543	40.762812	10.238281	2012-07-07 07:12:06.200	1.000000	0	1.341645e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
1.7	30.400684	40.763437	10.136719	2012-07-07 07:14:25.000	1.000000	1	1.341645e+09	138.80	True	1.886675	1.435720	-163.098388	1.737751
1.8	30.411914	40.793125	11.304688	2012-07-07 07:15:44.880	0.183907	1	1.341645e+09	79.52	True	1.886675	1.435720	-163.098388	1.737751
5.9	30.397754	40.765937	10.644531	2012-07-07 07:18:17.040	0.398268	5	1.341645e+09	152.16	True	1.477016	1.028668	137.014524	1.263067
0.11	30.404590	40.762187	10.136719	2012-07-07 07:22:16.240	0.178471	0	1.341646e+09	239.16	True	1.204078	0.954942	138.520075	1.142778
0.12	30.402148	40.744375	6.632812	2012-07-07 07:22:43.880	0.169656	0	1.341646e+09	27.64	True	1.204078	0.954942	138.520075	1.142778
3.6	30.393359	40.753750	2.671875	2012-07-07 07:23:08.320	0.491056	3	1.341646e+09	24.44	True	2.982386	1.933982	-169.556250	8.074210
1.9	30.396777	40.769062	10.746094	2012-07-07 07:24:09.560	0.188800	1	1.341646e+09	60.96	True	1.886675	1.435720	-163.098388	1.737751
5.11	30.400684	40.765312	10.746094	2012-07-07 07:24:34.000	1.000000	5	1.341646e+09	0.24	True	1.477016	1.028668	137.014524	1.263067
1.11	30.411914	40.793125	11.304688	2012-07-07 07:26:09.920	0.211587	1	1.341646e+09	95.88	True	1.886675	1.435720	-163.098388	1.737751
1.12	30.314746	40.715938	0.996094	2012-07-07 07:27:21.560	0.244670	1	1.341646e+09	71.64	True	1.886675	1.435720	-163.098388	1.737751
5.13	30.397754	40.765937	10.644531	2012-07-07 07:29:48.920	0.210121	5	1.341646e+09	147.00	True	1.477016	1.028668	137.014524	1.263067
3.8	30.445605	40.765312	8.003906	2012-07-07 07:34:42.600	0.424897	3	1.341646e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210
3.9	30.445117	40.764375	8.054688	2012-07-07 07:35:58.000	0.260982	3	1.341647e+09	75.08	True	2.982386	1.933982	-169.556250	8.074210
3.10	30.445117	40.764375	8.054688	2012-07-07 07:39:17.400	0.202312	3	1.341647e+09	199.16	True	2.982386	1.933982	-169.556250	8.074210
3.11	30.445117	40.764375	8.054688	2012-07-07 08:00:23.360	0.199350	3	1.341648e+09	1265.96	True	2.982386	1.933982	-169.556250	8.074210
1.14	30.411914	40.793125	11.304688	2012-07-07 08:10:46.560	0.158986	1	1.341649e+09	623.20	True	1.886675	1.435720	-163.098388	1.737751
0.17	30.338428	40.717969	1.021484	2012-07-07 08:15:48.880	0.246277	0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
0.18	30.429492	40.763125	8.460938	2012-07-07 08:17:35.000	0.563850	0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
1.17	30.411914	40.793125	11.304688	2012-07-07 08:30:27.240	0.218191	1	1.341650e+09	772.24	True	1.886675	1.435720	-163.098388	1.737751
2.19	30.404590	40.762187	10.136719	2012-07-07 08:41:19.560	0.304813	2	1.341650e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
1.19	30.411914	40.793125	11.304688	2012-07-07 08:45:27.600	0.225189	1	1.341651e+09	248.04	True	1.886675	1.435720	-163.098388	1.737751
2.21	30.404590	40.762187	10.136719	2012-07-07 08:46:34.360	0.321005	2	1.341651e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
1.21	30.411914	40.793125	11.304688	2012-07-07 08:47:00.560	0.407170	1	1.341651e+09	26.20	True	1.886675	1.435720	-163.098388	1.737751
3.12	30.441211	40.764375	8.257812	2012-07-07 08:48:43.800	1.000000	3	1.341651e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210
5.21	30.397754	40.765937	10.644531	2012-07-07 09:10:00.600	0.390236	5	1.341652e+09	0.28	True	1.477016	1.028668	137.014524	1.263067
5.22	30.397754	40.765937	10.644531	2012-07-07 09:10:55.640	0.152766	5	1.341652e+09	54.96	True	1.477016	1.028668	137.014524	1.263067
1.24	30.402637	40.763437	9.933594	2012-07-07 09:20:12.080	0.693933	1	1.341653e+09	556.44	True	1.886675	1.435720	-163.098388	1.737751
5.24	30.353320	40.670625	1.960938	2012-07-07 09:27:10.120	0.332328	5	1.341653e+09	417.68	True	1.477016	1.028668	137.014524	1.263067
1.25	30.411914	40.793125	11.304688	2012-07-07 09:42:13.440	0.206876	1	1.341654e+09	903.28	True	1.886675	1.435720	-163.098388	1.737751
2.27	30.404590	40.762187	10.136719	2012-07-07 09:46:04.920	0.341435	2	1.341654e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
5.26	30.397754	40.765937	10.644531	2012-07-07 09:59:27.240	0.186767	5	1.341655e+09	802.32	True	1.477016	1.028668	137.014524	1.263067
1.27	30.411914	40.793125	11.304688	2012-07-07 10:05:20.680	0.159305	1	1.341656e+09	353.36	True	1.886675	1.435720	-163.098388	1.737751
4.0	30.322070	40.759375	2.164062	2012-07-07 10:16:39.800	1.000000	4	1.341656e+09	679.12	True	3.378762	1.691182	-143.772340	2.733743
5.27	30.427539	40.764375	9.273438	2012-07-07 10:41:34.400	0.335804	5	1.341658e+09	0.28	True	1.477016	1.028668	137.014524	1.263067
0.30	30.404590	40.762187	10.136719	2012-07-07 11:15:32.640	0.298780	0	1.341660e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
0.31	30.404590	40.762187	10.136719	2012-07-07 11:23:41.520	0.385650	0	1.341660e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
1.30	30.331348	40.719062	0.996094	2012-07-07 12:13:39.320	0.266582	1	1.341663e+09	2997.80	True	1.886675	1.435720	-163.098388	1.737751
2.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	2	1.341663e+09	245.44	True	1.204078	0.954942	138.520075	1.142778
6.0	30.318896	40.676719	2.697266	2012-07-07 15:26:15.080	1.000000	6	1.341675e+09	11309.96	True	4.898208	2.005268	177.708818	3.423410
3.14	30.445117	40.764375	8.054688	2012-07-07 16:50:34.840	0.255224	3	1.341680e+09	5059.76	True	2.982386	1.933982	-169.556250	8.074210
1.31	30.411914	40.793125	11.304688	2012-07-07 19:23:20.560	0.146581	1	1.341689e+09	9165.72	True	1.886675	1.435720	-163.098388	1.737751

Assemble the backprojection and template matching catalog

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.

[20]:

BACKPROJECTION_CATALOG_FILENAME = "backprojection_catalog.csv"

[21]:

tm_catalog = template_group.catalog.catalog.copy()
tm_catalog.reset_index(inplace=True)
for i in range(len(tm_catalog)):
    tm_catalog.loc[i, "event_id"] = f"tm_{tm_catalog.loc[i, 'event_id']}"
tm_catalog

[21]:

	event_id	longitude	latitude	depth	origin_time	cc	tid	origin_time_sec	interevent_time_sec	unique_event	hmax_unc	hmin_unc	az_hmax_unc	vmax_unc
0	tm_1.0	30.399707	40.764062	10.136719	2012-07-07 06:56:02.200	0.432075	1	1.341644e+09	0.00	True	1.886675	1.435720	-163.098388	1.737751
1	tm_3.1	30.445117	40.764375	8.054688	2012-07-07 06:56:52.160	0.246272	3	1.341644e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210
2	tm_1.2	30.404590	40.767187	10.238281	2012-07-07 07:07:45.440	0.403376	1	1.341645e+09	652.96	True	1.886675	1.435720	-163.098388	1.737751
3	tm_3.3	30.445117	40.764375	8.054688	2012-07-07 07:09:59.680	0.239296	3	1.341645e+09	133.84	True	2.982386	1.933982	-169.556250	8.074210
4	tm_0.3	30.404590	40.762187	10.136719	2012-07-07 07:10:12.440	0.257793	0	1.341645e+09	0.36	True	1.204078	0.954942	138.520075	1.142778
5	tm_5.3	30.397754	40.765937	10.644531	2012-07-07 07:10:20.000	0.402187	5	1.341645e+09	7.56	True	1.477016	1.028668	137.014524	1.263067
6	tm_5.4	30.398242	40.763125	10.289062	2012-07-07 07:10:39.160	0.377112	5	1.341645e+09	19.12	True	1.477016	1.028668	137.014524	1.263067
7	tm_1.4	30.413867	40.761875	10.898438	2012-07-07 07:10:53.640	0.394400	1	1.341645e+09	13.60	True	1.886675	1.435720	-163.098388	1.737751
8	tm_3.4	30.413867	40.760625	10.898438	2012-07-07 07:11:07.280	0.681143	3	1.341645e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210
9	tm_5.6	30.407520	40.762812	10.136719	2012-07-07 07:11:36.080	0.181182	5	1.341645e+09	28.52	True	1.477016	1.028668	137.014524	1.263067
10	tm_0.8	30.406543	40.762812	10.238281	2012-07-07 07:12:06.200	1.000000	0	1.341645e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
11	tm_1.7	30.400684	40.763437	10.136719	2012-07-07 07:14:25.000	1.000000	1	1.341645e+09	138.80	True	1.886675	1.435720	-163.098388	1.737751
12	tm_1.8	30.411914	40.793125	11.304688	2012-07-07 07:15:44.880	0.183907	1	1.341645e+09	79.52	True	1.886675	1.435720	-163.098388	1.737751
13	tm_5.9	30.397754	40.765937	10.644531	2012-07-07 07:18:17.040	0.398268	5	1.341645e+09	152.16	True	1.477016	1.028668	137.014524	1.263067
14	tm_0.11	30.404590	40.762187	10.136719	2012-07-07 07:22:16.240	0.178471	0	1.341646e+09	239.16	True	1.204078	0.954942	138.520075	1.142778
15	tm_0.12	30.402148	40.744375	6.632812	2012-07-07 07:22:43.880	0.169656	0	1.341646e+09	27.64	True	1.204078	0.954942	138.520075	1.142778
16	tm_3.6	30.393359	40.753750	2.671875	2012-07-07 07:23:08.320	0.491056	3	1.341646e+09	24.44	True	2.982386	1.933982	-169.556250	8.074210
17	tm_1.9	30.396777	40.769062	10.746094	2012-07-07 07:24:09.560	0.188800	1	1.341646e+09	60.96	True	1.886675	1.435720	-163.098388	1.737751
18	tm_5.11	30.400684	40.765312	10.746094	2012-07-07 07:24:34.000	1.000000	5	1.341646e+09	0.24	True	1.477016	1.028668	137.014524	1.263067
19	tm_1.11	30.411914	40.793125	11.304688	2012-07-07 07:26:09.920	0.211587	1	1.341646e+09	95.88	True	1.886675	1.435720	-163.098388	1.737751
20	tm_1.12	30.314746	40.715938	0.996094	2012-07-07 07:27:21.560	0.244670	1	1.341646e+09	71.64	True	1.886675	1.435720	-163.098388	1.737751
21	tm_5.13	30.397754	40.765937	10.644531	2012-07-07 07:29:48.920	0.210121	5	1.341646e+09	147.00	True	1.477016	1.028668	137.014524	1.263067
22	tm_3.8	30.445605	40.765312	8.003906	2012-07-07 07:34:42.600	0.424897	3	1.341646e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210
23	tm_3.9	30.445117	40.764375	8.054688	2012-07-07 07:35:58.000	0.260982	3	1.341647e+09	75.08	True	2.982386	1.933982	-169.556250	8.074210
24	tm_3.10	30.445117	40.764375	8.054688	2012-07-07 07:39:17.400	0.202312	3	1.341647e+09	199.16	True	2.982386	1.933982	-169.556250	8.074210
25	tm_3.11	30.445117	40.764375	8.054688	2012-07-07 08:00:23.360	0.199350	3	1.341648e+09	1265.96	True	2.982386	1.933982	-169.556250	8.074210
26	tm_1.14	30.411914	40.793125	11.304688	2012-07-07 08:10:46.560	0.158986	1	1.341649e+09	623.20	True	1.886675	1.435720	-163.098388	1.737751
27	tm_0.17	30.338428	40.717969	1.021484	2012-07-07 08:15:48.880	0.246277	0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
28	tm_0.18	30.429492	40.763125	8.460938	2012-07-07 08:17:35.000	0.563850	0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
29	tm_1.17	30.411914	40.793125	11.304688	2012-07-07 08:30:27.240	0.218191	1	1.341650e+09	772.24	True	1.886675	1.435720	-163.098388	1.737751
30	tm_2.19	30.404590	40.762187	10.136719	2012-07-07 08:41:19.560	0.304813	2	1.341650e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
31	tm_1.19	30.411914	40.793125	11.304688	2012-07-07 08:45:27.600	0.225189	1	1.341651e+09	248.04	True	1.886675	1.435720	-163.098388	1.737751
32	tm_2.21	30.404590	40.762187	10.136719	2012-07-07 08:46:34.360	0.321005	2	1.341651e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
33	tm_1.21	30.411914	40.793125	11.304688	2012-07-07 08:47:00.560	0.407170	1	1.341651e+09	26.20	True	1.886675	1.435720	-163.098388	1.737751
34	tm_3.12	30.441211	40.764375	8.257812	2012-07-07 08:48:43.800	1.000000	3	1.341651e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210
35	tm_5.21	30.397754	40.765937	10.644531	2012-07-07 09:10:00.600	0.390236	5	1.341652e+09	0.28	True	1.477016	1.028668	137.014524	1.263067
36	tm_5.22	30.397754	40.765937	10.644531	2012-07-07 09:10:55.640	0.152766	5	1.341652e+09	54.96	True	1.477016	1.028668	137.014524	1.263067
37	tm_1.24	30.402637	40.763437	9.933594	2012-07-07 09:20:12.080	0.693933	1	1.341653e+09	556.44	True	1.886675	1.435720	-163.098388	1.737751
38	tm_5.24	30.353320	40.670625	1.960938	2012-07-07 09:27:10.120	0.332328	5	1.341653e+09	417.68	True	1.477016	1.028668	137.014524	1.263067
39	tm_1.25	30.411914	40.793125	11.304688	2012-07-07 09:42:13.440	0.206876	1	1.341654e+09	903.28	True	1.886675	1.435720	-163.098388	1.737751
40	tm_2.27	30.404590	40.762187	10.136719	2012-07-07 09:46:04.920	0.341435	2	1.341654e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
41	tm_5.26	30.397754	40.765937	10.644531	2012-07-07 09:59:27.240	0.186767	5	1.341655e+09	802.32	True	1.477016	1.028668	137.014524	1.263067
42	tm_1.27	30.411914	40.793125	11.304688	2012-07-07 10:05:20.680	0.159305	1	1.341656e+09	353.36	True	1.886675	1.435720	-163.098388	1.737751
43	tm_4.0	30.322070	40.759375	2.164062	2012-07-07 10:16:39.800	1.000000	4	1.341656e+09	679.12	True	3.378762	1.691182	-143.772340	2.733743
44	tm_5.27	30.427539	40.764375	9.273438	2012-07-07 10:41:34.400	0.335804	5	1.341658e+09	0.28	True	1.477016	1.028668	137.014524	1.263067
45	tm_0.30	30.404590	40.762187	10.136719	2012-07-07 11:15:32.640	0.298780	0	1.341660e+09	0.08	True	1.204078	0.954942	138.520075	1.142778
46	tm_0.31	30.404590	40.762187	10.136719	2012-07-07 11:23:41.520	0.385650	0	1.341660e+09	0.04	True	1.204078	0.954942	138.520075	1.142778
47	tm_1.30	30.331348	40.719062	0.996094	2012-07-07 12:13:39.320	0.266582	1	1.341663e+09	2997.80	True	1.886675	1.435720	-163.098388	1.737751
48	tm_2.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	2	1.341663e+09	245.44	True	1.204078	0.954942	138.520075	1.142778
49	tm_6.0	30.318896	40.676719	2.697266	2012-07-07 15:26:15.080	1.000000	6	1.341675e+09	11309.96	True	4.898208	2.005268	177.708818	3.423410
50	tm_3.14	30.445117	40.764375	8.054688	2012-07-07 16:50:34.840	0.255224	3	1.341680e+09	5059.76	True	2.982386	1.933982	-169.556250	8.074210
51	tm_1.31	30.411914	40.793125	11.304688	2012-07-07 19:23:20.560	0.146581	1	1.341689e+09	9165.72	True	1.886675	1.435720	-163.098388	1.737751

[22]:

bp_catalog = pd.read_csv(
    os.path.join(BPMF.cfg.OUTPUT_PATH, BACKPROJECTION_CATALOG_FILENAME),
    index_col=0
)
# add event ids
for i in range(len(bp_catalog)):
    bp_catalog.loc[i, "event_id"] = f"bp_{i}"
# convert origin times from string to pandas.Timestamp
bp_catalog["origin_time"] = pd.to_datetime(bp_catalog["origin_time"])
bp_catalog

[22]:

	longitude	latitude	depth	origin_time	hmax_unc	hmin_unc	az_hmax_unc	Mw	Mw_err	event_id
0	30.402637	40.765937	10.238281	2012-07-07 06:56:02.560	1.554406	1.320863	-167.389837	2.688609	2.603238e-02	bp_0
1	30.408496	40.765937	10.238281	2012-07-07 07:07:45.720	1.386711	1.049999	148.121750	3.912620	1.835205e-31	bp_1
2	30.404590	40.762187	10.136719	2012-07-07 07:12:06.200	1.204078	0.954942	138.520075	NaN	NaN	bp_2
3	30.411914	40.793125	11.304688	2012-07-07 07:14:25.000	1.886675	1.435720	-163.098388	2.809426	1.748471e-02	bp_3
4	30.397754	40.765937	10.644531	2012-07-07 07:24:34.000	1.477016	1.028668	137.014524	2.604610	2.720755e-02	bp_4
5	30.409961	40.768125	10.289062	2012-07-07 07:34:42.800	2.157981	1.659802	125.916805	NaN	NaN	bp_5
6	30.413867	40.759375	9.273438	2012-07-07 08:17:35.080	1.807823	1.109721	70.558859	NaN	NaN	bp_6
7	30.445117	40.764375	8.054688	2012-07-07 08:48:43.800	2.982386	1.933982	-169.556250	NaN	NaN	bp_7
8	30.402637	40.764687	10.035156	2012-07-07 09:20:12.480	1.116928	0.884104	159.472964	NaN	NaN	bp_8
9	30.316699	40.754688	0.285156	2012-07-07 10:16:39.800	3.378762	1.691182	-143.772340	NaN	NaN	bp_9
10	30.421680	40.764375	9.273438	2012-07-07 10:41:34.320	1.921096	1.396241	81.876681	NaN	NaN	bp_10
11	30.294482	40.628281	-1.517578	2012-07-07 15:26:15.080	4.898208	2.005268	177.708818	NaN	NaN	bp_11

[23]:

# catalog merging parameters
dt_criterion_pd = pd.Timedelta(DT_CRITERION_SEC, "s")
HMAX_UNC_CRITERION_KM = 10.

[24]:

final_catalog = tm_catalog.copy()
missing_event = np.zeros(len(bp_catalog), dtype=bool)

for i in tqdm(range(len(bp_catalog)), desc="Loop through BP cat"):
    if bp_catalog.iloc[i]["hmax_unc"] > HMAX_UNC_CRITERION_KM:
        continue
    t_min = bp_catalog.iloc[i]["origin_time"] - dt_criterion_pd
    t_max = bp_catalog.iloc[i]["origin_time"] + dt_criterion_pd
    subset_tm = (tm_catalog["origin_time"] > t_min) & (tm_catalog["origin_time"] < t_max)
    if np.sum(subset_tm) == 0:
        missing_event[i] = True

final_catalog = pd.concat(
        (tm_catalog, bp_catalog[missing_event]),
        ignore_index=True
        )
final_catalog.sort_values(
        "origin_time", ascending=True, inplace=True
        )

Loop through BP cat: 100%|██████████| 12/12 [00:00<00:00, 1574.19it/s]

[25]:

final_catalog.set_index("event_id", drop=False, inplace=True)
final_catalog

[25]:

	event_id	longitude	latitude	depth	origin_time	cc	tid	origin_time_sec	interevent_time_sec	unique_event	hmax_unc	hmin_unc	az_hmax_unc	vmax_unc	Mw	Mw_err
event_id
tm_1.0	tm_1.0	30.399707	40.764062	10.136719	2012-07-07 06:56:02.200	0.432075	1.0	1.341644e+09	0.00	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_3.1	tm_3.1	30.445117	40.764375	8.054688	2012-07-07 06:56:52.160	0.246272	3.0	1.341644e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_1.2	tm_1.2	30.404590	40.767187	10.238281	2012-07-07 07:07:45.440	0.403376	1.0	1.341645e+09	652.96	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_3.3	tm_3.3	30.445117	40.764375	8.054688	2012-07-07 07:09:59.680	0.239296	3.0	1.341645e+09	133.84	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_0.3	tm_0.3	30.404590	40.762187	10.136719	2012-07-07 07:10:12.440	0.257793	0.0	1.341645e+09	0.36	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_5.3	tm_5.3	30.397754	40.765937	10.644531	2012-07-07 07:10:20.000	0.402187	5.0	1.341645e+09	7.56	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_5.4	tm_5.4	30.398242	40.763125	10.289062	2012-07-07 07:10:39.160	0.377112	5.0	1.341645e+09	19.12	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_1.4	tm_1.4	30.413867	40.761875	10.898438	2012-07-07 07:10:53.640	0.394400	1.0	1.341645e+09	13.60	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_3.4	tm_3.4	30.413867	40.760625	10.898438	2012-07-07 07:11:07.280	0.681143	3.0	1.341645e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_5.6	tm_5.6	30.407520	40.762812	10.136719	2012-07-07 07:11:36.080	0.181182	5.0	1.341645e+09	28.52	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_0.8	tm_0.8	30.406543	40.762812	10.238281	2012-07-07 07:12:06.200	1.000000	0.0	1.341645e+09	0.04	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_1.7	tm_1.7	30.400684	40.763437	10.136719	2012-07-07 07:14:25.000	1.000000	1.0	1.341645e+09	138.80	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_1.8	tm_1.8	30.411914	40.793125	11.304688	2012-07-07 07:15:44.880	0.183907	1.0	1.341645e+09	79.52	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_5.9	tm_5.9	30.397754	40.765937	10.644531	2012-07-07 07:18:17.040	0.398268	5.0	1.341645e+09	152.16	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_0.11	tm_0.11	30.404590	40.762187	10.136719	2012-07-07 07:22:16.240	0.178471	0.0	1.341646e+09	239.16	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_0.12	tm_0.12	30.402148	40.744375	6.632812	2012-07-07 07:22:43.880	0.169656	0.0	1.341646e+09	27.64	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_3.6	tm_3.6	30.393359	40.753750	2.671875	2012-07-07 07:23:08.320	0.491056	3.0	1.341646e+09	24.44	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_1.9	tm_1.9	30.396777	40.769062	10.746094	2012-07-07 07:24:09.560	0.188800	1.0	1.341646e+09	60.96	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_5.11	tm_5.11	30.400684	40.765312	10.746094	2012-07-07 07:24:34.000	1.000000	5.0	1.341646e+09	0.24	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_1.11	tm_1.11	30.411914	40.793125	11.304688	2012-07-07 07:26:09.920	0.211587	1.0	1.341646e+09	95.88	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_1.12	tm_1.12	30.314746	40.715938	0.996094	2012-07-07 07:27:21.560	0.244670	1.0	1.341646e+09	71.64	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_5.13	tm_5.13	30.397754	40.765937	10.644531	2012-07-07 07:29:48.920	0.210121	5.0	1.341646e+09	147.00	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_3.8	tm_3.8	30.445605	40.765312	8.003906	2012-07-07 07:34:42.600	0.424897	3.0	1.341646e+09	0.04	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_3.9	tm_3.9	30.445117	40.764375	8.054688	2012-07-07 07:35:58.000	0.260982	3.0	1.341647e+09	75.08	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_3.10	tm_3.10	30.445117	40.764375	8.054688	2012-07-07 07:39:17.400	0.202312	3.0	1.341647e+09	199.16	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_3.11	tm_3.11	30.445117	40.764375	8.054688	2012-07-07 08:00:23.360	0.199350	3.0	1.341648e+09	1265.96	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_1.14	tm_1.14	30.411914	40.793125	11.304688	2012-07-07 08:10:46.560	0.158986	1.0	1.341649e+09	623.20	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_0.17	tm_0.17	30.338428	40.717969	1.021484	2012-07-07 08:15:48.880	0.246277	0.0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_0.18	tm_0.18	30.429492	40.763125	8.460938	2012-07-07 08:17:35.000	0.563850	0.0	1.341649e+09	0.04	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_1.17	tm_1.17	30.411914	40.793125	11.304688	2012-07-07 08:30:27.240	0.218191	1.0	1.341650e+09	772.24	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_2.19	tm_2.19	30.404590	40.762187	10.136719	2012-07-07 08:41:19.560	0.304813	2.0	1.341650e+09	0.08	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_1.19	tm_1.19	30.411914	40.793125	11.304688	2012-07-07 08:45:27.600	0.225189	1.0	1.341651e+09	248.04	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_2.21	tm_2.21	30.404590	40.762187	10.136719	2012-07-07 08:46:34.360	0.321005	2.0	1.341651e+09	0.08	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_1.21	tm_1.21	30.411914	40.793125	11.304688	2012-07-07 08:47:00.560	0.407170	1.0	1.341651e+09	26.20	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_3.12	tm_3.12	30.441211	40.764375	8.257812	2012-07-07 08:48:43.800	1.000000	3.0	1.341651e+09	0.08	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_5.21	tm_5.21	30.397754	40.765937	10.644531	2012-07-07 09:10:00.600	0.390236	5.0	1.341652e+09	0.28	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_5.22	tm_5.22	30.397754	40.765937	10.644531	2012-07-07 09:10:55.640	0.152766	5.0	1.341652e+09	54.96	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_1.24	tm_1.24	30.402637	40.763437	9.933594	2012-07-07 09:20:12.080	0.693933	1.0	1.341653e+09	556.44	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_5.24	tm_5.24	30.353320	40.670625	1.960938	2012-07-07 09:27:10.120	0.332328	5.0	1.341653e+09	417.68	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_1.25	tm_1.25	30.411914	40.793125	11.304688	2012-07-07 09:42:13.440	0.206876	1.0	1.341654e+09	903.28	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_2.27	tm_2.27	30.404590	40.762187	10.136719	2012-07-07 09:46:04.920	0.341435	2.0	1.341654e+09	0.08	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_5.26	tm_5.26	30.397754	40.765937	10.644531	2012-07-07 09:59:27.240	0.186767	5.0	1.341655e+09	802.32	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_1.27	tm_1.27	30.411914	40.793125	11.304688	2012-07-07 10:05:20.680	0.159305	1.0	1.341656e+09	353.36	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_4.0	tm_4.0	30.322070	40.759375	2.164062	2012-07-07 10:16:39.800	1.000000	4.0	1.341656e+09	679.12	True	3.378762	1.691182	-143.772340	2.733743	NaN	NaN
tm_5.27	tm_5.27	30.427539	40.764375	9.273438	2012-07-07 10:41:34.400	0.335804	5.0	1.341658e+09	0.28	True	1.477016	1.028668	137.014524	1.263067	NaN	NaN
tm_0.30	tm_0.30	30.404590	40.762187	10.136719	2012-07-07 11:15:32.640	0.298780	0.0	1.341660e+09	0.08	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_0.31	tm_0.31	30.404590	40.762187	10.136719	2012-07-07 11:23:41.520	0.385650	0.0	1.341660e+09	0.04	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_1.30	tm_1.30	30.331348	40.719062	0.996094	2012-07-07 12:13:39.320	0.266582	1.0	1.341663e+09	2997.80	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN
tm_2.33	tm_2.33	30.404590	40.762187	10.136719	2012-07-07 12:17:45.120	0.167869	2.0	1.341663e+09	245.44	True	1.204078	0.954942	138.520075	1.142778	NaN	NaN
tm_6.0	tm_6.0	30.318896	40.676719	2.697266	2012-07-07 15:26:15.080	1.000000	6.0	1.341675e+09	11309.96	True	4.898208	2.005268	177.708818	3.423410	NaN	NaN
tm_3.14	tm_3.14	30.445117	40.764375	8.054688	2012-07-07 16:50:34.840	0.255224	3.0	1.341680e+09	5059.76	True	2.982386	1.933982	-169.556250	8.074210	NaN	NaN
tm_1.31	tm_1.31	30.411914	40.793125	11.304688	2012-07-07 19:23:20.560	0.146581	1.0	1.341689e+09	9165.72	True	1.886675	1.435720	-163.098388	1.737751	NaN	NaN

Compute earthquake magnitudes

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.

[27]:

for tp in template_group.templates:
    if "Mw" in tp.aux_data:
        self_detection = (final_catalog["tid"] == float(tp.tid)) & (final_catalog["cc"] > 0.999)
        final_catalog.loc[self_detection, "Mw"] = tp.aux_data["Mw"]
        final_catalog.loc[self_detection, "Mw_err"] = tp.aux_data["Mw_err"]
# final_catalog

[58]:

ref_events = final_catalog.dropna(subset=["Mw"])
ref_events
for event_id in events:
    event = events[event_id]
    event_id = f"tm_{event_id}"
    tid = float(event.aux_data["tid"])
    # print(tid)
    subset = ref_events.tid == tid
    if np.sum(subset) > 0:
        peak_amp = event.aux_data["peak_amplitudes"]
        Mls = []
        for i, row in ref_events[subset].iterrows():
            peak_amp_ref = events[row.name[3:]].aux_data["peak_amplitudes"]
            ratios = (peak_amp/peak_amp_ref).flatten()
            ratios = ratios[~np.isinf(ratios) & (ratios > 0.)]
            Ml_diff = np.median(np.log10(ratios))
            # Ml_i = row.Mw + 2./3. * Ml_diff
            Ml_i = row.Mw + Ml_diff
            Mls.append(Ml_i)
        final_catalog.loc[event_id, "Ml"] = np.median(Mls)
final_catalog[["origin_time", "longitude", "latitude", "depth", "hmax_unc", "vmax_unc", "Mw", "Ml"]]

[58]:

	origin_time	longitude	latitude	depth	hmax_unc	vmax_unc	Mw	Ml
event_id
tm_1.0	2012-07-07 06:56:02.200	30.399707	40.764062	10.136719	1.886675	1.737751	NaN	2.586553
tm_3.1	2012-07-07 06:56:52.160	30.445117	40.764375	8.054688	2.982386	8.074210	NaN	NaN
tm_1.2	2012-07-07 07:07:45.440	30.404590	40.767187	10.238281	1.886675	1.737751	NaN	4.005050
tm_3.3	2012-07-07 07:09:59.680	30.445117	40.764375	8.054688	2.982386	8.074210	NaN	NaN
tm_0.3	2012-07-07 07:10:12.440	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_5.3	2012-07-07 07:10:20.000	30.397754	40.765937	10.644531	1.477016	1.263067	NaN	1.087575
tm_5.4	2012-07-07 07:10:39.160	30.398242	40.763125	10.289062	1.477016	1.263067	NaN	1.145118
tm_1.4	2012-07-07 07:10:53.640	30.413867	40.761875	10.898438	1.886675	1.737751	NaN	1.165784
tm_3.4	2012-07-07 07:11:07.280	30.413867	40.760625	10.898438	2.982386	8.074210	NaN	NaN
tm_5.6	2012-07-07 07:11:36.080	30.407520	40.762812	10.136719	1.477016	1.263067	NaN	1.124363
tm_0.8	2012-07-07 07:12:06.200	30.406543	40.762812	10.238281	1.204078	1.142778	NaN	NaN
tm_1.7	2012-07-07 07:14:25.000	30.400684	40.763437	10.136719	1.886675	1.737751	2.809426	2.809426
tm_1.8	2012-07-07 07:15:44.880	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	1.251298
tm_5.9	2012-07-07 07:18:17.040	30.397754	40.765937	10.644531	1.477016	1.263067	NaN	0.990920
tm_0.11	2012-07-07 07:22:16.240	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_0.12	2012-07-07 07:22:43.880	30.402148	40.744375	6.632812	1.204078	1.142778	NaN	NaN
tm_3.6	2012-07-07 07:23:08.320	30.393359	40.753750	2.671875	2.982386	8.074210	NaN	NaN
tm_1.9	2012-07-07 07:24:09.560	30.396777	40.769062	10.746094	1.886675	1.737751	NaN	1.027156
tm_5.11	2012-07-07 07:24:34.000	30.400684	40.765312	10.746094	1.477016	1.263067	2.604610	2.604610
tm_1.11	2012-07-07 07:26:09.920	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	1.171287
tm_1.12	2012-07-07 07:27:21.560	30.314746	40.715938	0.996094	1.886675	1.737751	NaN	0.869616
tm_5.13	2012-07-07 07:29:48.920	30.397754	40.765937	10.644531	1.477016	1.263067	NaN	1.004545
tm_3.8	2012-07-07 07:34:42.600	30.445605	40.765312	8.003906	2.982386	8.074210	NaN	NaN
tm_3.9	2012-07-07 07:35:58.000	30.445117	40.764375	8.054688	2.982386	8.074210	NaN	NaN
tm_3.10	2012-07-07 07:39:17.400	30.445117	40.764375	8.054688	2.982386	8.074210	NaN	NaN
tm_3.11	2012-07-07 08:00:23.360	30.445117	40.764375	8.054688	2.982386	8.074210	NaN	NaN
tm_1.14	2012-07-07 08:10:46.560	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	1.096352
tm_0.17	2012-07-07 08:15:48.880	30.338428	40.717969	1.021484	1.204078	1.142778	NaN	NaN
tm_0.18	2012-07-07 08:17:35.000	30.429492	40.763125	8.460938	1.204078	1.142778	NaN	NaN
tm_1.17	2012-07-07 08:30:27.240	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	1.085124
tm_2.19	2012-07-07 08:41:19.560	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_1.19	2012-07-07 08:45:27.600	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	1.000992
tm_2.21	2012-07-07 08:46:34.360	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_1.21	2012-07-07 08:47:00.560	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	1.066457
tm_3.12	2012-07-07 08:48:43.800	30.441211	40.764375	8.257812	2.982386	8.074210	NaN	NaN
tm_5.21	2012-07-07 09:10:00.600	30.397754	40.765937	10.644531	1.477016	1.263067	NaN	1.246354
tm_5.22	2012-07-07 09:10:55.640	30.397754	40.765937	10.644531	1.477016	1.263067	NaN	0.934968
tm_1.24	2012-07-07 09:20:12.080	30.402637	40.763437	9.933594	1.886675	1.737751	NaN	2.351699
tm_5.24	2012-07-07 09:27:10.120	30.353320	40.670625	1.960938	1.477016	1.263067	NaN	1.231549
tm_1.25	2012-07-07 09:42:13.440	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	0.972295
tm_2.27	2012-07-07 09:46:04.920	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_5.26	2012-07-07 09:59:27.240	30.397754	40.765937	10.644531	1.477016	1.263067	NaN	0.991324
tm_1.27	2012-07-07 10:05:20.680	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	0.886426
tm_4.0	2012-07-07 10:16:39.800	30.322070	40.759375	2.164062	3.378762	2.733743	NaN	NaN
tm_5.27	2012-07-07 10:41:34.400	30.427539	40.764375	9.273438	1.477016	1.263067	NaN	1.485605
tm_0.30	2012-07-07 11:15:32.640	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_0.31	2012-07-07 11:23:41.520	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_1.30	2012-07-07 12:13:39.320	30.331348	40.719062	0.996094	1.886675	1.737751	NaN	1.012254
tm_2.33	2012-07-07 12:17:45.120	30.404590	40.762187	10.136719	1.204078	1.142778	NaN	NaN
tm_6.0	2012-07-07 15:26:15.080	30.318896	40.676719	2.697266	4.898208	3.423410	NaN	NaN
tm_3.14	2012-07-07 16:50:34.840	30.445117	40.764375	8.054688	2.982386	8.074210	NaN	NaN
tm_1.31	2012-07-07 19:23:20.560	30.411914	40.793125	11.304688	1.886675	1.737751	NaN	0.854499