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CEED

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CEED: California Earthquake Event Dataset for Machine Learning and Cloud Computing

The California Earthquake Event Dataset (CEED) is a dataset of earthquake waveforms and metadata for machine learning and cloud computing.

Detailed statistics about the dataset are available in this arXiv paper.

Acknowledgments

The seismic data used in this study were collected by (1) the Berkeley Digital Seismic Network (BDSN, doi:10.7932/BDSN) and the USGS Northern California Seismic Network (NCSN, doi:10.7914/SN/NC); and (2) the Southern California Seismic Network (SCSN, doi:10.7914/SN/CI). The original waveform data, metadata, and data products for this study were accessed through the Northern California Earthquake Data Center (doi:10.7932/NCEDC) and the Southern California Earthquake Center (doi:10.7909/C3WD3xH1). Please include acknowledgments and citations of the original data providers when using this dataset.

The dataset structure is shown below, and you can find more information about the format at AI4EPS

 Group: / len:60424
  |- Group: /ci38457511 len:35
  |  |-* begin_time = 2019-07-06T03:19:23.668000
  |  |-* depth_km = 8.0
  |  |-* end_time = 2019-07-06T03:21:23.668000
  |  |-* event_id = ci38457511
  |  |-* event_time = 2019-07-06T03:19:53.040000
  |  |-* event_time_index = 2937
  |  |-* latitude = 35.7695
  |  |-* longitude = -117.5993
  |  |-* magnitude = 7.1
  |  |-* magnitude_type = w
  |  |-* nt = 12000
  |  |-* nx = 35
  |  |-* sampling_rate = 100
  |  |-* source = SC
  |  |- Dataset: /ci38457511/CI.CCC..HH (shape:(3, 12000))
  |  |  |- (dtype=float32)
  |  |  |  |-* azimuth = 141.849479
  |  |  |  |-* back_azimuth = 321.986302
  |  |  |  |-* component = ENZ
  |  |  |  |-* depth_km = -0.67
  |  |  |  |-* distance_km = 34.471389
  |  |  |  |-* dt_s = 0.01
  |  |  |  |-* elevation_m = 670.0
  |  |  |  |-* event_id = ['ci38457511' 'ci38457511' 'ci37260300']
  |  |  |  |-* instrument = HH
  |  |  |  |-* latitude = 35.52495
  |  |  |  |-* local_depth_m = 0.0
  |  |  |  |-* location = 
  |  |  |  |-* longitude = -117.36453
  |  |  |  |-* network = CI
  |  |  |  |-* p_phase_index = 3575
  |  |  |  |-* p_phase_polarity = U
  |  |  |  |-* p_phase_score = 0.8
  |  |  |  |-* p_phase_status = manual
  |  |  |  |-* p_phase_time = 2019-07-06T03:19:59.422000
  |  |  |  |-* phase_index = [ 3575  4184 11826]
  |  |  |  |-* phase_picking_channel = ['HHZ' 'HNN' 'HHZ']
  |  |  |  |-* phase_polarity = ['U' 'N' 'N']
  |  |  |  |-* phase_remark = ['i' 'e' 'e']
  |  |  |  |-* phase_score = [0.8 0.5 0.5]
  |  |  |  |-* phase_status = manual
  |  |  |  |-* phase_time = ['2019-07-06T03:19:59.422000' '2019-07-06T03:20:05.509000' '2019-07-06T03:21:21.928000']
  |  |  |  |-* phase_type = ['P' 'S' 'P']
  |  |  |  |-* s_phase_index = 4184
  |  |  |  |-* s_phase_polarity = N
  |  |  |  |-* s_phase_score = 0.5
  |  |  |  |-* s_phase_status = manual
  |  |  |  |-* s_phase_time = 2019-07-06T03:20:05.509000
  |  |  |  |-* snr = [ 637.9865898   286.9100766  1433.04052911]
  |  |  |  |-* station = CCC
  |  |  |  |-* unit = 1e-6m/s
  |  |- Dataset: /ci38457511/CI.CCC..HN (shape:(3, 12000))
  |  |  |- (dtype=float32)
  |  |  |  |-* azimuth = 141.849479
  |  |  |  |-* back_azimuth = 321.986302
  |  |  |  |-* component = ENZ
  |  |  |  |-* depth_km = -0.67
  |  |  |  |-* distance_km = 34.471389
  |  |  |  |-* dt_s = 0.01
  |  |  |  |-* elevation_m = 670.0
  |  |  |  |-* event_id = ['ci38457511' 'ci38457511' 'ci37260300']
  ......

Getting Started

Requirements

  • datasets
  • h5py
  • fsspec
  • pytorch

Usage

Import the necessary packages:

import h5py
import numpy as np
import torch
from datasets import load_dataset

We have 6 configurations for the dataset:

  • "station"
  • "event"
  • "station_train"
  • "event_train"
  • "station_test"
  • "event_test"

"station" yields station-based samples one by one, while "event" yields event-based samples one by one. The configurations with no suffix are the full dataset, while the configurations with suffix "_train" and "_test" only have corresponding split of the full dataset. Train split contains data from 1970 to 2019, while test split contains data in 2020.

The sample of station is a dictionary with the following keys:

  • data: the waveform with shape (3, nt), the default time length is 8192
  • begin_time: the begin time of the waveform data
  • end_time: the end time of the waveform data
  • phase_time: the phase arrival time
  • phase_index: the time point index of the phase arrival time
  • phase_type: the phase type
  • phase_polarity: the phase polarity in ('U', 'D', 'N')
  • event_time: the event time
  • event_time_index: the time point index of the event time
  • event_location: the event location with shape (3,), including latitude, longitude, depth
  • station_location: the station location with shape (3,), including latitude, longitude and depth

The sample of event is a dictionary with the following keys:

  • data: the waveform with shape (n_station, 3, nt), the default time length is 8192
  • begin_time: the begin time of the waveform data
  • end_time: the end time of the waveform data
  • phase_time: the phase arrival time with shape (n_station,)
  • phase_index: the time point index of the phase arrival time with shape (n_station,)
  • phase_type: the phase type with shape (n_station,)
  • phase_polarity: the phase polarity in ('U', 'D', 'N') with shape (n_station,)
  • event_time: the event time
  • event_time_index: the time point index of the event time
  • event_location: the space-time coordinates of the event with shape (n_staion, 3)
  • station_location: the space coordinates of the station with shape (n_station, 3), including latitude, longitude and depth

The default configuration is station_test. You can specify the configuration by argument name. For example:

# load dataset
# ATTENTION: Streaming(Iterable Dataset) is complex to support because of the feature of HDF5
# So we recommend to directly load the dataset and convert it into iterable later
# The dataset is very large, so you need to wait for some time at the first time

# to load "station_test" with test split
ceed = load_dataset("AI4EPS/CEED", split="test")
# or
ceed = load_dataset("AI4EPS/CEED", name="station_test", split="test")

# to load "event" with train split
ceed = load_dataset("AI4EPS/CEED", name="event", split="train")

Example loading the dataset 

ceed = load_dataset("AI4EPS/CEED", name="station_test", split="test")

# print the first sample of the iterable dataset
for example in ceed:
    print("\nIterable test\n")
    print(example.keys())
    for key in example.keys():
        if key == "data":
            print(key, np.array(example[key]).shape)
        else:
            print(key, example[key])
    break

# %%
ceed = ceed.with_format("torch")
dataloader = DataLoader(ceed, batch_size=8, num_workers=0, collate_fn=lambda x: x)

for batch in dataloader:
    print("\nDataloader test\n")
    print(f"Batch size: {len(batch)}")
    print(batch[0].keys())
    for key in batch[0].keys():
        if key == "data":
            print(key, np.array(batch[0][key]).shape)
        else:
            print(key, batch[0][key])
    break
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