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

from tqdm import tqdm
from time import time as give_time

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

[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,
)

ERROR 1: PROJ: proj_create_from_database: Open of /home/ebeauce/miniconda3/envs/hy7_py310/share/proj failed
Reading catalog: 100%|██████████| 8/8 [00:00<00:00, 12.84it/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 0x7fa9614c60e0>

[6]:

template_group.catalog.catalog

[6]:

	longitude	latitude	depth	origin_time	cc	tid
event_id
2.0	30.413867	40.749531	16.179688	2012-07-07 06:56:02.160000+00:00	0.432628	2
3.0	30.439746	40.739453	-0.933594	2012-07-07 06:56:02.200000+00:00	0.290606	3
4.0	30.414355	40.749766	9.324219	2012-07-07 06:56:02.600000+00:00	0.330700	4
0.0	30.409473	40.749766	8.917969	2012-07-07 06:56:02.800000+00:00	1.000000	0
1.0	30.365527	40.736641	4.652344	2012-07-07 06:56:03.560000+00:00	0.307322	1
...	...	...	...	...	...	...
3.15	30.439746	40.739453	-0.933594	2012-07-07 16:50:34.760000+00:00	0.244599	3
1.27	30.365527	40.736641	4.652344	2012-07-07 16:50:36.120000+00:00	0.309978	1
2.32	30.413867	40.749531	16.179688	2012-07-07 19:23:20.520000+00:00	0.146354	2
3.16	30.439746	40.739453	-0.933594	2012-07-07 20:23:54.480000+00:00	0.180567	3
1.28	30.365527	40.736641	4.652344	2012-07-07 20:23:55.840000+00:00	0.208675	1

158 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 equality.
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,
)

Computing the similarity matrix...
Computing the inter-template directional errors...

/home/ebeauce/miniconda3/envs/hy7_py310/lib/python3.10/site-packages/BPMF/dataset.py:2884: RuntimeWarning: invalid value encountered in true_divide
  unit_direction /= np.sqrt(np.sum(unit_direction**2, axis=1))[

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%|██████████| 158/158 [00:00<00:00, 2250.65it/s]

0.07s to flag the multiples

The catalog now has a new column: 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
2.0	30.413867	40.749531	16.179688	2012-07-07 06:56:02.160000+00:00	0.432628	2	1.341644e+09	0.00	False
3.0	30.439746	40.739453	-0.933594	2012-07-07 06:56:02.200000+00:00	0.290606	3	1.341644e+09	0.04	False
4.0	30.414355	40.749766	9.324219	2012-07-07 06:56:02.600000+00:00	0.330700	4	1.341644e+09	0.40	False
0.0	30.409473	40.749766	8.917969	2012-07-07 06:56:02.800000+00:00	1.000000	0	1.341644e+09	0.20	True
1.0	30.365527	40.736641	4.652344	2012-07-07 06:56:03.560000+00:00	0.307322	1	1.341644e+09	0.76	False
...	...	...	...	...	...	...	...	...	...
3.15	30.439746	40.739453	-0.933594	2012-07-07 16:50:34.760000+00:00	0.244599	3	1.341680e+09	5059.48	False
1.27	30.365527	40.736641	4.652344	2012-07-07 16:50:36.120000+00:00	0.309978	1	1.341680e+09	1.36	True
2.32	30.413867	40.749531	16.179688	2012-07-07 19:23:20.520000+00:00	0.146354	2	1.341689e+09	9164.40	True
3.16	30.439746	40.739453	-0.933594	2012-07-07 20:23:54.480000+00:00	0.180567	3	1.341693e+09	3633.96	False
1.28	30.365527	40.736641	4.652344	2012-07-07 20:23:55.840000+00:00	0.208675	1	1.341693e+09	1.36	True

158 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"]]

[12]:

template_group.catalog.catalog

[12]:

	longitude	latitude	depth	origin_time	cc	tid	origin_time_sec	interevent_time_sec	unique_event
event_id
0.0	30.409473	40.749766	8.917969	2012-07-07 06:56:02.800000+00:00	1.000000	0	1.341644e+09	0.20	True
0.1	30.409473	40.749766	8.917969	2012-07-07 06:56:52.720000+00:00	0.472709	0	1.341644e+09	0.20	True
0.2	30.409473	40.749766	8.917969	2012-07-07 07:07:46.040000+00:00	0.406057	0	1.341645e+09	0.16	True
3.3	30.439746	40.739453	-0.933594	2012-07-07 07:09:59.600000+00:00	0.247610	3	1.341645e+09	132.64	True
0.3	30.409473	40.749766	8.917969	2012-07-07 07:10:12.680000+00:00	0.180725	0	1.341645e+09	0.64	True
4.3	30.414355	40.749766	9.324219	2012-07-07 07:10:20.080000+00:00	0.398994	4	1.341645e+09	7.40	True
4.4	30.414355	40.749766	9.324219	2012-07-07 07:10:39.240000+00:00	0.377552	4	1.341645e+09	18.12	True
2.4	30.413867	40.749531	16.179688	2012-07-07 07:10:53.600000+00:00	0.392783	2	1.341645e+09	13.32	True
1.6	30.365527	40.736641	4.652344	2012-07-07 07:11:08.560000+00:00	1.000000	1	1.341645e+09	0.76	True
1.7	30.365527	40.736641	4.652344	2012-07-07 07:11:20.040000+00:00	0.186660	1	1.341645e+09	11.36	True
1.8	30.365527	40.736641	4.652344	2012-07-07 07:11:34.480000+00:00	0.231058	1	1.341645e+09	14.44	True
4.7	30.414355	40.749766	9.324219	2012-07-07 07:12:06.240000+00:00	0.520927	4	1.341645e+09	0.40	True
2.7	30.413867	40.749531	16.179688	2012-07-07 07:14:24.960000+00:00	1.000000	2	1.341645e+09	137.68	True
2.8	30.413867	40.749531	16.179688	2012-07-07 07:15:44.840000+00:00	0.184301	2	1.341645e+09	78.48	True
4.9	30.414355	40.749766	9.324219	2012-07-07 07:18:17.120000+00:00	0.394677	4	1.341645e+09	152.28	True
0.10	30.409473	40.749766	8.917969	2012-07-07 07:22:16.480000+00:00	0.169376	0	1.341646e+09	238.40	True
1.12	30.365527	40.736641	4.652344	2012-07-07 07:23:09.600000+00:00	0.538661	1	1.341646e+09	0.76	True
2.9	30.413867	40.749531	16.179688	2012-07-07 07:24:09.520000+00:00	0.188573	2	1.341646e+09	59.92	True
4.11	30.414355	40.749766	9.324219	2012-07-07 07:24:34.080000+00:00	1.000000	4	1.341646e+09	0.40	True
2.11	30.413867	40.749531	16.179688	2012-07-07 07:26:09.880000+00:00	0.213727	2	1.341646e+09	94.76	True
2.12	30.413867	40.749531	16.179688	2012-07-07 07:27:21.520000+00:00	0.243206	2	1.341646e+09	71.64	True
4.13	30.414355	40.749766	9.324219	2012-07-07 07:29:49+00:00	0.208277	4	1.341646e+09	146.80	True
0.14	30.409473	40.749766	8.917969	2012-07-07 07:34:43.160000+00:00	0.517379	0	1.341646e+09	0.20	True
1.15	30.365527	40.736641	4.652344	2012-07-07 07:35:59.280000+00:00	0.338245	1	1.341647e+09	0.76	True
6.8	30.363086	40.727969	-0.882812	2012-07-07 07:39:18.800000+00:00	0.282421	6	1.341647e+09	0.12	True
3.12	30.439746	40.739453	-0.933594	2012-07-07 08:00:23.280000+00:00	0.201788	3	1.341648e+09	1264.48	True
2.14	30.413867	40.749531	16.179688	2012-07-07 08:10:46.520000+00:00	0.159820	2	1.341649e+09	621.88	True
4.17	30.414355	40.749766	9.324219	2012-07-07 08:15:48.920000+00:00	0.216080	4	1.341649e+09	0.44	True
4.18	30.414355	40.749766	9.324219	2012-07-07 08:17:35.040000+00:00	0.453781	4	1.341649e+09	0.44	True
2.17	30.413867	40.749531	16.179688	2012-07-07 08:30:27.200000+00:00	0.220648	2	1.341650e+09	771.12	True
2.18	30.413867	40.749531	16.179688	2012-07-07 08:41:19.160000+00:00	0.232859	2	1.341650e+09	651.96	True
2.19	30.413867	40.749531	16.179688	2012-07-07 08:45:27.560000+00:00	0.223718	2	1.341651e+09	247.76	True
4.20	30.414355	40.749766	9.324219	2012-07-07 08:46:34.360000+00:00	0.275816	4	1.341651e+09	0.40	True
2.21	30.413867	40.749531	16.179688	2012-07-07 08:47:00.520000+00:00	0.411563	2	1.341651e+09	25.08	True
3.13	30.439746	40.739453	-0.933594	2012-07-07 08:48:43.720000+00:00	1.000000	3	1.341651e+09	0.04	True
1.20	30.365527	40.736641	4.652344	2012-07-07 08:50:03+00:00	0.151161	1	1.341651e+09	77.80	True
4.22	30.414355	40.749766	9.324219	2012-07-07 09:10:00.680000+00:00	0.389309	4	1.341652e+09	0.40	True
1.22	30.365527	40.736641	4.652344	2012-07-07 09:10:56.680000+00:00	0.163008	1	1.341652e+09	0.96	True
2.24	30.413867	40.749531	16.179688	2012-07-07 09:20:12.040000+00:00	0.693182	2	1.341653e+09	555.36	True
6.13	30.363086	40.727969	-0.882812	2012-07-07 09:27:11.240000+00:00	0.360983	6	1.341653e+09	0.08	True
2.25	30.413867	40.749531	16.179688	2012-07-07 09:42:13.400000+00:00	0.207445	2	1.341654e+09	902.16	True
2.26	30.413867	40.749531	16.179688	2012-07-07 09:46:04.520000+00:00	0.250705	2	1.341654e+09	230.48	True
2.27	30.413867	40.749531	16.179688	2012-07-07 09:52:19.200000+00:00	0.150789	2	1.341655e+09	374.04	True
1.25	30.365527	40.736641	4.652344	2012-07-07 09:59:28.280000+00:00	0.191176	1	1.341655e+09	0.96	True
2.28	30.413867	40.749531	16.179688	2012-07-07 10:05:20.640000+00:00	0.158694	2	1.341656e+09	352.36	True
5.0	30.420000	40.750000	-2.000000	2012-07-07 10:16:39.560000+00:00	1.000000	5	1.341656e+09	678.92	True
6.14	30.363086	40.727969	-0.882812	2012-07-07 10:41:35.520000+00:00	1.000000	6	1.341658e+09	0.12	True
2.29	30.413867	40.749531	16.179688	2012-07-07 11:15:32.240000+00:00	0.219736	2	1.341660e+09	2036.72	True
2.30	30.413867	40.749531	16.179688	2012-07-07 11:23:41.120000+00:00	0.289398	2	1.341660e+09	488.24	True
2.31	30.413867	40.749531	16.179688	2012-07-07 12:13:39.280000+00:00	0.268495	2	1.341663e+09	2997.52	True
7.0	30.340000	40.740000	22.000000	2012-07-07 15:26:15.280000+00:00	1.000000	7	1.341675e+09	11555.36	True
1.27	30.365527	40.736641	4.652344	2012-07-07 16:50:36.120000+00:00	0.309978	1	1.341680e+09	1.36	True
2.32	30.413867	40.749531	16.179688	2012-07-07 19:23:20.520000+00:00	0.146354	2	1.341689e+09	9164.40	True
1.28	30.365527	40.736641	4.652344	2012-07-07 20:23:55.840000+00:00	0.208675	1	1.341693e+09	1.36	True

[13]:

fig = template_group.catalog.plot_map()

../../_images/tutorial_notebooks_8_build_earthquake_catalog_18_0.png

Relocate events

All earthquakes detected with the same template are simply given the template location. However, their actual locations are spread around. Here, we create an Event instance for each of the unique events and use phasenet and NLLoc to relocate them.

[14]:

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)

2022-09-26 15:42:42.656085: 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
2022-09-26 15:42:42.656130: 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.

[15]:

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

[20]:

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)]])
    # 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,
        inter_op_parallelism_threads=n_CPUs,
        intra_op_parallelism_threads=n_CPUs,
        data_folder=DATA_FOLDER,
    )
    event.relocate()
    events.append(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

Pred: 100%|██████████| 1/1 [00:00<00:00,  4.87it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.43it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.39it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.26it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.22it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.21it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.69it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.34it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.31it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.05it/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,  5.79it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.88it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.28it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.26it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.00it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.43it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.80it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.12it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.43it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.23it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.25it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.64it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.24it/s]

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

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

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.15it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.20it/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,  6.24it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.60it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.29it/s]

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

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

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.16it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.17it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.81it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.66it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.98it/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,  5.89it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.62it/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,  5.27it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.63it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.58it/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,  5.37it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.85it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  6.23it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.79it/s]

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

Pred: 100%|██████████| 1/1 [00:00<00:00,  5.76it/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,  6.08it/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,  6.13it/s]

[29]:

fig = template_group.catalog.plot_map(lat_margin=0.01, lon_margin=0.02, s=50)

../../_images/tutorial_notebooks_8_build_earthquake_catalog_23_0.png

[21]:

fig = events[0].plot(figsize=(15, 15))

../../_images/tutorial_notebooks_8_build_earthquake_catalog_24_0.png

[26]:

template_group.catalog.longitude - template_group.catalog.catalog.longitude

[26]:

event_id
0.0     0.0
0.1     0.0
0.2     0.0
3.3     0.0
0.3     0.0
4.3     0.0
4.4     0.0
2.4     0.0
1.6     0.0
1.7     0.0
1.8     0.0
4.7     0.0
2.7     0.0
2.8     0.0
4.9     0.0
0.10    0.0
1.12    0.0
2.9     0.0
4.11    0.0
2.11    0.0
2.12    0.0
4.13    0.0
0.14    0.0
1.15    0.0
6.8     0.0
3.12    0.0
2.14    0.0
4.17    0.0
4.18    0.0
2.17    0.0
2.18    0.0
2.19    0.0
4.20    0.0
2.21    0.0
3.13    0.0
1.20    0.0
4.22    0.0
1.22    0.0
2.24    0.0
6.13    0.0
2.25    0.0
2.26    0.0
2.27    0.0
1.25    0.0
2.28    0.0
5.0     0.0
6.14    0.0
2.29    0.0
2.30    0.0
2.31    0.0
7.0     0.0
1.27    0.0
2.32    0.0
1.28    0.0
Name: longitude, dtype: float64