Author: He XingChen

Last Updated: 2026-02-12

Datasets Overview

SeisPolarity provides a unified interface to access and process multiple seismic polarity datasets. All datasets are stored in HDF5 format for efficient storage and streaming.

Available Datasets

Dataset	Source	Size	Classes	Format
SCSN	Southern California Seismic Network	Large	U/D/N	HDF5
Txed	Texas Earthquake Dataset	Medium	U/D	HDF5
DiTing	China Earthquake Networks Center	Medium	U/D/N	HDF5
Instance	Global seismic data	Medium	U/D/N	HDF5
PNW	Pacific Northwest	Large	U/D/N	HDF5

Datasets

SCSN

The Southern California Seismic Network (SCSN) dataset contains polarity-labeled seismic waveforms from the Southern California Seismic Network. This dataset covers earthquake events from 2000-2020 and is a high-quality dataset with manual annotations.

Warning

Dataset size: waveforms.hdf5 ~660Gb, metadata.csv ~2.2Gb Polarity subset SCSN ~15 GB

Citation

Cheng, Y., Ross, Z. E., Hauksson, E., Ben-Zion, Y. (2023). Refined earthquake focal mechanism catalog for southern California derived with deep learning algorithms. Journal of Geophysical Research: Solid Earth, 128, e2022JB025975. https://doi.org/10.1029/2022JB025975

Ross, Z. E., Meier, M.-A., Hauksson, E. (2018). P wave arrival picking and first-motion polarity determination with deep learning. Journal of Geophysical Research: Solid Earth, 123, 5405-5416. https://doi.org/10.1029/2018JB015510

Txed

The Texas Earthquake Dataset (TXED) is a regional seismic signal benchmark dataset from Texas. This dataset contains a large number of earthquake events and noise waveforms, serving as an important data resource for machine learning in seismology.

Balanced Strategy

Recommended strategy: Polarity Inversion (1:1:1)

This strategy creates a balanced dataset with equal proportions of Up, Down, and Unknown samples:

Each Up and Down sample generates two samples (original + polarity-inverted)
Unknown samples are added to match the total count of (Up + Down) samples
Final distribution: Up = 1/3, Down = 1/3, Unknown = 1/3

from seispolarity import BalancedPolarityGenerator

generator = BalancedPolarityGenerator(
    dataset,
    strategy="polarity_inversion"
)

Warning

Dataset size: waveforms.hdf5 ~70Gb, metadata.csv 120Mb

Citation

Chen, Y., Savvaidis, A., Saad, O. M., Huang, G.-C. D., Siervo, D., O’Sullivan, V., McCabe, C., Uku, B., Fleck, P., Burke, G., Alvarez, N. L., Domino, J., & Grigoratos, I. (2024). TXED: The Texas Earthquake Dataset for AI. Seismological Research Letters, 95(6), 1-13. https://doi.org/10.1785/0220230327

DiTing

The DiTing dataset is a large-scale Chinese seismic benchmark dataset specifically designed for artificial intelligence seismology research. This dataset contains over 640,000 high-quality P-wave first-motion polarity labels, covering more than 1,300 broadband and short-period seismic stations across China.

Citation

Zhao, M., Xiao, Z., Chen, S., & Fang, L. (2023). DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology. Earthquake Science, 36(2), 84-94. https://doi.org/10.1016/j.eqs.2022.01.022

Data Download

The dataset can be requested for download at: https://data.earthquake.cn/

Instance

The INSTANCE dataset is an Italian seismic waveform dataset compiled by the Italian National Institute of Geophysics and Volcanology (INGV), specifically designed for machine learning applications. This dataset contains nearly 1.2 million three-component waveform traces and serves as an important resource for seismological research.

Balanced Strategy

Recommended strategy: Polarity Inversion (1:1:1)

This strategy creates a balanced dataset with equal proportions of Up, Down, and Unknown samples:

Each Up and Down sample generates two samples (original + polarity-inverted)
Unknown samples are added to match the total count of (Up + Down) samples
Final distribution: Up = 1/3, Down = 1/3, Unknown = 1/3

from seispolarity import BalancedPolarityGenerator

generator = BalancedPolarityGenerator(
    dataset,
    strategy="polarity_inversion"
)

Warning

Dataset size:

waveforms (counts) ~160Gb
waveforms (ground motion units) ~310Gb

Citation

Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., & Lauciani, V. (2021). INSTANCE – The Italian Seismic Dataset For Machine Learning. Earth System Science Data, 13, 5509–5542. https://doi.org/10.5194/essd-13-5509-2021

PNW

The Pacific Northwest (PNW) dataset is a machine learning-ready curated dataset containing diverse seismic signals from the Pacific Northwest region. This dataset is compiled by the Pacific Northwest Seismic Network and covers various seismic event types including earthquakes, explosions, and noise.

Balanced Strategy

Recommended strategy: Min-Based (1:1:1)

This strategy creates a balanced dataset by sampling equally from all classes up to the minimum class count:

Count samples in each polarity class (Up, Down, Unknown)
Determine the minimum count among all classes
Sample equally from each class up to the minimum count
Final distribution: Up = 1/3, Down = 1/3, Unknown = 1/3

from seispolarity import BalancedPolarityGenerator

generator = BalancedPolarityGenerator(
    dataset,
    strategy="min_based"
)

Citation

Ni, Y., Hutko, A., Skene, F., Denolle, M., Malone, S., Bodin, P., Hartog, R., & Wright, A. (2023). Curated Pacific Northwest AI-ready Seismic Dataset. Seismica, 2(1), 368. https://doi.org/10.26443/seismica.v2i1.368

Loading Datasets

Automatic Download

SeisPolarity can automatically download datasets:

from seispolarity import get_dataset_path, WaveformDataset

# Download from Hugging Face (default)
data_path = get_dataset_path("SCSN", "train", cache_dir="./datasets")

# Or use ModelScope (recommended for users in China)
data_path = get_dataset_path("SCSN", "train", use_hf=False)

Load from Local Files

from seispolarity import WaveformDataset

# Disk streaming (suitable for large datasets)
dataset = WaveformDataset(
    path="data/scsn_train.hdf5",
    name="SCSN_Train",
    preload=False
)

# RAM preloading (suitable for small datasets)
dataset = WaveformDataset(
    path="data/scsn_train.hdf5",
    name="SCSN_Train",
    preload=True
)

Dataset API

WaveformDataset

The main class for loading waveform data.

from seispolarity import WaveformDataset

dataset = WaveformDataset(
    path="data.hdf5",          # HDF5 file path
    name="SCSN",               # Dataset name
    preload=False,             # Whether to preload into RAM
    data_key="X",              # HDF5 key for waveforms
    label_key="Y",             # HDF5 key for labels
    p_pick_position=300,      # P-wave arrival position
    pick_key="p_pick",        # Use p_pick as P-wave arrival point
    crop_left=200,             # Samples before P-pick
    crop_right=200,            # Samples after P-pick
    allowed_labels=[0, 1, 2]   # Allowed labels (0: Up, 1: Down, 2: Unknown)
)

Data Format

Waveforms are stored in HDF5 files with the following structure:

waveforms.hdf5
├── X                # Waveform data (N_samples, N_channels)
├── Y                # P-value labels (N_samples,)
├── Z                # Clarity (only required for ditingmotion)   
├── metadata         # Additional metadata (optional)
└── ...

Label Encoding

0: Up (positive polarity)
1: Down (negative polarity)
2: Unknown

DataLoader

Create a PyTorch DataLoader for training:

loader = dataset.get_dataloader(
    batch_size=1024,
    num_workers=4,
    shuffle=True,
    pin_memory=True
)

Data Inspection

Basic Statistics

from seispolarity import WaveformDataset

dataset = WaveformDataset(path="data.hdf5", name="SCSN")

# Get dataset statistics
print(f"Total samples: {len(dataset)}")
print(f"Label distribution: {dataset.label_distribution}")
print(f"Waveform shape: {dataset.waveform_shape}")

Multi-Dataset Training

Combine multiple datasets:

from seispolarity import MultiWaveformDataset

# Create multiple datasets
dataset1 = WaveformDataset(path="scsn.hdf5", name="SCSN")
dataset2 = WaveformDataset(path="txed.hdf5", name="Txed")

# Combine them
combined = MultiWaveformDataset([dataset1, dataset2])

Balanced Sampling

For datasets with label imbalance, use balanced sampling:

from seispolarity import BalancedPolarityGenerator

generator = BalancedPolarityGenerator(
    dataset,
    strategy="polarity_inversion"  # or "min_based"
)
loader = generator.get_dataloader(batch_size=256)

Download Locations

Datasets can be downloaded from:

Hugging Face: https://huggingface.co/datasets/chuanjun1978/Seismic-AI-Data
ModelScope: https://www.modelscope.cn/datasets/chuanjun/Seismic-AI-Data/ (recommended for users in China)

For more details, see the Installation Guide.