--- license: mit --- # Quakeflow_NC ## Introduction 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/)) Cite the NCEDC and PhaseNet: Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211. NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Acknowledge the NCEDC: Waveform data, metadata, or data products for this study were accessed through the Northern California Earthquake Data Center (NCEDC), doi:10.7932/NCEDC. ``` Group: / len:16227 |- Group: /nc71111584 len:2 | |-* begin_time = 2020-01-02T07:01:19.620 | |-* depth_km = 3.69 | |-* end_time = 2020-01-02T07:03:19.620 | |-* event_id = nc71111584 | |-* event_time = 2020-01-02T07:01:48.240 | |-* event_time_index = 2862 | |-* latitude = 37.6545 | |-* longitude = -118.8798 | |-* magnitude = -0.15 | |-* magnitude_type = D | |-* num_stations = 2 | |- Dataset: /nc71111584/NC.MCB..HH (shape:(3, 12000)) | | |- (dtype=float32) | | | |-* azimuth = 233.0 | | | |-* component = ['E' 'N' 'Z'] | | | |-* distance_km = 1.9 | | | |-* dt_s = 0.01 | | | |-* elevation_m = 2391.0 | | | |-* emergence_angle = 159.0 | | | |-* event_id = ['nc71111584' 'nc71111584'] | | | |-* latitude = 37.6444 | | | |-* location = | | | |-* longitude = -118.8968 | | | |-* network = NC | | | |-* phase_index = [3000 3101] | | | |-* phase_polarity = ['U' 'N'] | | | |-* phase_remark = ['IP' 'ES'] | | | |-* phase_score = [1 2] | | | |-* phase_time = ['2020-01-02T07:01:49.620' '2020-01-02T07:01:50.630'] | | | |-* phase_type = ['P' 'S'] | | | |-* snr = [2.82143 3.055604 1.8412642] | | | |-* station = MCB | | | |-* unit = 1e-6m/s | |- Dataset: /nc71111584/NC.MCB..HN (shape:(3, 12000)) | | |- (dtype=float32) | | | |-* azimuth = 233.0 | | | |-* component = ['E' 'N' 'Z'] ...... ``` ## How to use ### Requirements - datasets - h5py - fsspec - pytorch ### Usage Import the necessary packages: ```python 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: ```python # load dataset # ATTENTION: Streaming(Iterable Dataset) is difficult 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 quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", split="test") # or quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test") # to load "event" with train split quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train") ``` #### Example loading the dataset ```python quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test") # print the first sample of the iterable dataset for example in quakeflow_nc: 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 # %% quakeflow_nc = quakeflow_nc.with_format("torch") dataloader = DataLoader(quakeflow_nc, 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 ```