Classifying earthquakes and mining activity with deep neural networks

Earthquake detection and phase picking are central problems of seismic activity analysis. Traditional approaches [1] and machine learning methods [2] are applied in this domain, typically performing well on commonly investigated standard datasets reaching above 99% accuracy in seismic activity detection.

 

Unfortunately, most databases in the literature contain only earthquake data as detectable activities and spurious activities such as mining are not included in these datasets. We have investigated a recently published deep neural network-based method [3] and found that these detectors are fooled by mining activity.

 

To solve this problem, we have created a complex dataset that contains 1200 independently recorded mining and earthquake activities from Central Europe. Our dataset poses a more complex problem than commonly investigated datasets such as the STanford EArthquake Dataset and can be viewed as an extension of that.

 

We have trained a convolutional neural network containing five convolutional and three fully-connected layers to classify these signals on this dataset and reached a 94% classification accuracy, which demonstrates that the categorization of mining activity and earthquakes is possible with modern machine learning approaches.

[1] Galiana-Merino, J. J., Rosa-Herranz, J. L., & Parolai, S. (2008). Seismic P Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 3815-3826.

 

[2] Zhu, W., & Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261-273.

 

[3] Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature communications, 11(1), 1-12.