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.
