WaveDP

A large deep learning model to reduce background noise from seismic signals in the time domain and measuring signal arrival times. The model was developed by Chengping Chai, Derek Rose, Scott Stewart, Nathan Martindale, Mark Adams, Anibely Torres Polanco, Lisa Linville, Christopher Stanley, and Philip Bingham.

Introduction

This model is trained on STEAD (Mousavi et al., 2019) for reducing background noise from seismic signals and measuring signal arrival times simultaneously. The model was trained using earthquake data at local distances (0-350 km). The model uses 57s-long three-component seismograms sampled at 100 Hz as input. The output of the model are denoised three-component seismograms and probability channels corresponding to P wave, S wave, and noise. These probabilities can be used to compute P- and S-wave arrival times.

Installation

Creating a virtual environment is highly encouraged.

pip3 install wavedp

To install only for CPU (e.g., macOS), you may use the following commands.

pip3 install torch torchvision torchaudio
pip3 install wavedp

Example Usage

import h5py
import numpy as np  
from wavedp.wavedp import DenoiserPicker
model = DenoiserPicker()
#
fid = h5py.File("example_waveforms.h5", "r")
data_group = fid["data"]
example_data = []
true_p_index = []
true_s_index = []
for akey in data_group.keys():
    dataset = data_group[akey]
    example_data.append(dataset[...])
    true_p_index.append(float(dataset.attrs["p_arrival_sample"]))
    true_s_index.append(float(dataset.attrs["s_arrival_sample"]))
fid.close()
#
example_data = np.array(example_data)
denoised, picker_prob = model.predict_probability(example_data)
p_index, s_index, denoised = model.predict_arrivals(example_data)
print("True P-wave arrival index:", true_p_index)
print("Predicted P-wave arrival index:", p_index)
print("True S-wave arrival index:", true_s_index)
print("Predicted S-wave arrival index:", s_index)

Test the Package

First, go to the top directory of the package. Then, using the following commands.

cd tests
python run_tests.py

Known Limitations

• The model may have a less-than-optimal performance for earthquake data outside of a source-receiver distance range of 10–110 km and a magnitude range of 0–4.5 because of biases in the training data.
• The model may produce false detections when applied to continuous seismic data.
• The model may not perform well for earthquake data at larger distances or for non-earthquake sources.

Reference

TBD

License

GNU GENERAL PUBLIC LICENSE version 3

Credit

The architecture of the deep learning model was modified from SeisBench (Woollam et al., 2022).