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Abstract:
Earthquake source is among the key element within the framework of seismic hazard. However, Seismic waves can be of natural origin or from anthropogenic sources such as mining-related events. Hence, without a proper approach for source discrimination, an earthquake catalog for hazard assessment can be contaminated. In this study, we propose a Convolutional Neural Network based on spectrograms to perform the waveform classification. It is targeted to applications in Madagascar. The approach consists of three main steps: (1) generation of the time–frequency representation of ground-motion recordings (spectrogram); (2) training and validation of the model using spectrograms of ground shaking; (3) testing and prediction. To measure the compatibility between output predictions and given ground truth labels, we adopt the commonly used loss function and accuracy measure. Given that the spatial distribution of the seismic data in Madagascar is non-uniform, we perform two-step analyses. First, we adopt a supervised approach for 6051 known events in the central part of Madagascar. Then, we use the outcome for the second step training and perform the prediction for non-categorized events throughout the country.The results show that our model has the potential to separate earthquakes from mining-related events. For the supervised approach, among the 20% used for testing, 97.48% and 2.52% of the events give correct and incorrect labels, respectively. These pre-trained data are subsequently used to perform predictions for unlabeled events throughout Madagascar. Our results show that the model could learn the features of the classes even for data coming from different parts of Madagascar.
