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Detecting earthquakes: a novel deep learning-based approach for effective disaster response

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Abstract

In the present study, we present an intelligent earthquake signal detector that provides added assistance to automate traditional disaster responses. To effectively respond in a crisis scenario, additional sensors and automation are always necessary. Deep learning has achieved success in various low signal-to-noise ratio tasks, which motivated us to propose a novel 3-dimensional (3D) CNN-RNN-based earthquake detector from a demonstration paradigm to real-time implementation. Data taken from the ST anford EA rthquake D ataset (STEAD) are used to train the network. After preprocessing the raw earthquake signals, features such as log-mel spectrograms are extracted. Once the model has learned spatial and temporal information from low-frequency earthquake waves, it can be employed in real time to distinguish small and large earthquakes from seismic noise with an accuracy, sensitivity, and specificity of 99.057%, 98.488%, and 99.621%, respectively. We also observe that the choice of filters in log-mel spectrogram impacts the results much more than the model complexity. Furthermore, we implement and test the model on data collected continuously over two months by a personal seismometer in the laboratory. The inference speed for a single prediction is 2.27 seconds, and the system delivers a stable detection of all 63 major earthquakes from November 2019 to December 2019 reported by the Japan Meteorological Agency.

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Acknowledgements

This work is supported by JSPS KAKENHI Grant No. 16H02884, 17K00365, and 19K12017.

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Authors and Affiliations

  1. Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8552, Japan

    Muhammad Shakeel, Katsutoshi Itoyama, Kenji Nishida & Kazuhiro Nakadai

  2. Honda Research Institute Japan Co., Ltd., 8-1, Honcho, Wako, Saitama, 351-0188, Japan

    Kazuhiro Nakadai

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  1. Muhammad Shakeel
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Shakeel, M., Itoyama, K., Nishida, K. et al. Detecting earthquakes: a novel deep learning-based approach for effective disaster response. Appl Intell 51, 8305–8315 (2021). https://doi.org/10.1007/s10489-021-02285-7

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