![]() Furthermore, we implement and test the model on data collected continuously over two months by a personal seismometer in the laboratory. We also observe that the choice of filters in log-mel spectrogram impacts the results much more than the model complexity. 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. After preprocessing the raw earthquake signals, features such as log-mel spectrograms are extracted. ![]() Data taken from the ST anford EA rthquake D ataset (STEAD) are used to train the network. 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. ![]() To effectively respond in a crisis scenario, additional sensors and automation are always necessary. In the present study, we present an intelligent earthquake signal detector that provides added assistance to automate traditional disaster responses. ![]()
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January 2023
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