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--- |
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license: mit |
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--- |
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# Quakeflow_NC |
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## Introduction |
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This dataset is part of the data (1970-2020) from [NCEDC (Northern California Earthquake Data Center)](https://ncedc.org/index.html) and is organized as several HDF5 files. The dataset structure is shown below, and you can find more information about the format at [AI4EPS](https://ai4eps.github.io/homepage/ml4earth/seismic_event_format1/)) |
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Cite the NCEDC and PhaseNet: |
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Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211. |
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NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. |
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Acknowledge the NCEDC: |
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Waveform data, metadata, or data products for this study were accessed through the Northern California Earthquake Data Center (NCEDC), doi:10.7932/NCEDC. |
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``` |
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Group: / len:16227 |
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|- Group: /nc71111584 len:2 |
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| |-* begin_time = 2020-01-02T07:01:19.620 |
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| |-* depth_km = 3.69 |
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| |-* end_time = 2020-01-02T07:03:19.620 |
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| |-* event_id = nc71111584 |
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| |-* event_time = 2020-01-02T07:01:48.240 |
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| |-* event_time_index = 2862 |
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| |-* latitude = 37.6545 |
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| |-* longitude = -118.8798 |
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| |-* magnitude = -0.15 |
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| |-* magnitude_type = D |
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| |-* num_stations = 2 |
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| |- Dataset: /nc71111584/NC.MCB..HH (shape:(3, 12000)) |
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| | |- (dtype=float32) |
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| | | |-* azimuth = 233.0 |
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| | | |-* component = ['E' 'N' 'Z'] |
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| | | |-* distance_km = 1.9 |
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| | | |-* dt_s = 0.01 |
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| | | |-* elevation_m = 2391.0 |
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| | | |-* emergence_angle = 159.0 |
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| | | |-* event_id = ['nc71111584' 'nc71111584'] |
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| | | |-* latitude = 37.6444 |
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| | | |-* location = |
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| | | |-* longitude = -118.8968 |
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| | | |-* network = NC |
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| | | |-* phase_index = [3000 3101] |
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| | | |-* phase_polarity = ['U' 'N'] |
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| | | |-* phase_remark = ['IP' 'ES'] |
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| | | |-* phase_score = [1 2] |
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| | | |-* phase_time = ['2020-01-02T07:01:49.620' '2020-01-02T07:01:50.630'] |
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| | | |-* phase_type = ['P' 'S'] |
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| | | |-* snr = [2.82143 3.055604 1.8412642] |
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| | | |-* station = MCB |
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| | | |-* unit = 1e-6m/s |
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| |- Dataset: /nc71111584/NC.MCB..HN (shape:(3, 12000)) |
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| | |- (dtype=float32) |
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| | | |-* azimuth = 233.0 |
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| | | |-* component = ['E' 'N' 'Z'] |
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...... |
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``` |
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## How to use |
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### Requirements |
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- datasets |
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- h5py |
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- fsspec |
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- torch (for PyTorch) |
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### Usage |
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Import the necessary packages: |
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```python |
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import h5py |
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import numpy as np |
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import torch |
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from torch.utils.data import Dataset, IterableDataset, DataLoader |
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from datasets import load_dataset |
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``` |
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We have 6 configurations for the dataset: |
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- "station" |
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- "event" |
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- "station_train" |
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- "event_train" |
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- "station_test" |
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- "event_test" |
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"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. |
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The sample of `station` is a dictionary with the following keys: |
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- `data`: the waveform with shape `(3, nt)`, the default time length is 8192 |
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- `phase_pick`: the probability of the phase pick with shape `(3, nt)`, the first dimension is noise, P and S |
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- `event_location`: the event location with shape `(4,)`, including latitude, longitude, depth and time |
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- `station_location`: the station location with shape `(3,)`, including latitude, longitude and depth |
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The sample of `event` is a dictionary with the following keys: |
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- `data`: the waveform with shape `(n_station, 3, nt)`, the default time length is 8192 |
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- `phase_pick`: the probability of the phase pick with shape `(n_station, 3, nt)`, the first dimension is noise, P and S |
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- `event_center`: the probability of the event time with shape `(n_station, feature_nt)`, default feature time length is 512 |
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- `event_location`: the space-time coordinates of the event with shape `(n_staion, 4, feature_nt)` |
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- `event_location_mask`: the probability mask of the event time with shape `(n_station, feature_nt)` |
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- `station_location`: the space coordinates of the station with shape `(n_station, 3)`, including latitude, longitude and depth |
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The default configuration is `station_test`. You can specify the configuration by argument `name`. For example: |
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```python |
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# load dataset |
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# ATTENTION: Streaming(Iterable Dataset) is difficult to support because of the feature of HDF5 |
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# So we recommend to directly load the dataset and convert it into iterable later |
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# The dataset is very large, so you need to wait for some time at the first time |
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# to load "station_test" with test split |
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", split="test") |
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# or |
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test") |
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# to load "event" with train split |
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train") |
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``` |
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#### Usage for `station` |
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Then you can change the dataset into PyTorch format iterable dataset, and view the first sample: |
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```python |
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quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test") |
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# for PyTorch DataLoader, we need to divide the dataset into several shards |
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num_workers=4 |
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quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers) |
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# because add examples formatting to get tensors when using the "torch" format |
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# has not been implemented yet, we need to manually add the formatting when using iterable dataset |
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# if you want to use dataset directly, just use |
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# quakeflow_nc.with_format("torch") |
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quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()}) |
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try: |
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isinstance(quakeflow_nc, torch.utils.data.IterableDataset) |
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except: |
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raise Exception("quakeflow_nc is not an IterableDataset") |
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# print the first sample of the iterable dataset |
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for example in quakeflow_nc: |
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print("\nIterable test\n") |
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print(example.keys()) |
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for key in example.keys(): |
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print(key, example[key].shape, example[key].dtype) |
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break |
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dataloader = DataLoader(quakeflow_nc, batch_size=4, num_workers=num_workers) |
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for batch in dataloader: |
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print("\nDataloader test\n") |
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print(batch.keys()) |
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for key in batch.keys(): |
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print(key, batch[key].shape, batch[key].dtype) |
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break |
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``` |
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#### Usage for `event` |
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Then you can change the dataset into PyTorch format dataset, and view the first sample (Don't forget to reorder the keys): |
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```python |
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quakeflow_nc = datasets.load_dataset("AI4EPS/quakeflow_nc", split="test", name="event_test") |
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# for PyTorch DataLoader, we need to divide the dataset into several shards |
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num_workers=4 |
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quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers) |
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quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()}) |
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try: |
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isinstance(quakeflow_nc, torch.utils.data.IterableDataset) |
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except: |
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raise Exception("quakeflow_nc is not an IterableDataset") |
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# print the first sample of the iterable dataset |
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for example in quakeflow_nc: |
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print("\nIterable test\n") |
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print(example.keys()) |
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for key in example.keys(): |
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print(key, example[key].shape, example[key].dtype) |
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break |
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dataloader = DataLoader(quakeflow_nc, batch_size=1, num_workers=num_workers) |
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for batch in dataloader: |
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print("\nDataloader test\n") |
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print(batch.keys()) |
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for key in batch.keys(): |
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print(key, batch[key].shape, batch[key].dtype) |
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break |
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``` |