metadata
license: mit
CEED: California EarthquakE Dataset for Machine Learning and Cloud Computing
The California EarthquakE Dataset (CEED) is a dataset of earthquake waveforms and metadata for machine learning and cloud computing. 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 8192begin_time: the begin time of the waveform dataend_time: the end time of the waveform dataphase_time: the phase arrival timephase_index: the time point index of the phase arrival timephase_type: the phase typephase_polarity: the phase polarity in ('U', 'D', 'N')event_time: the event timeevent_time_index: the time point index of the event timeevent_location: the event location with shape(3,), including latitude, longitude, depthstation_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 8192begin_time: the begin time of the waveform dataend_time: the end time of the waveform dataphase_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 timeevent_time_index: the time point index of the event timeevent_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
Extension
If you want to introduce new features in to labels, we recommend to make a copy of CEED.py and modify the _generate_examples method. Check AI4EPS/EQNet for an example. To load the dataset with your modified script, specify the path to the script in load_dataset function:
ceed = load_dataset("path/to/your/CEED.py", name="station_test", split="test", trust_remote_code=True)