Classifying earthquakes and mining activity with deep neural networks

Abstract

<p>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.</p><p> </p><p>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.</p><p> </p><p>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.</p><p> </p><p>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.</p><p><br><br></p><p>[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.</p><p> </p><p>[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.</p><p> </p><p>[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.</p>