Complex Neural Networks for Estimating Epicentral Distance, Depth, and Magnitude of Seismic Waves

Taking advantage of the latest advances in deep learning for seismology, we address earthquake characterization from a data-driven perspective. Many of the usual procedures for extracting information from seismograms require processing a large volume of data using empirical and physics rule-based techniques. In this letter, we propose a novel approach for estimating epicentral distance, depth, and magnitude directly from individual raw three-component seismograms of 1-min length observed by single stations. Our convolutional neural network-based method is able to handle complex-valued representations of the seismic data in the time–frequency domain by using dedicated convolutional and activation functions. In this way, our method benefits both from extracting relevant information through time–frequency domain analysis and from designing a single architecture that deals with complex information. The proposed method achieves a mean absolute error of 4.51 km for epicentral distance, 6.15 km for depth, and 0.26 for magnitude estimation. The experiments were conducted over a publicly available and large database, STanford EArthquake data set (STEAD), and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed approach. Source code and best model are available at
https://github.com/ristea/
stead-earthquake-cnn.