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

摘要

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.