Identification of Earthquakes and Anthropogenic Events in Madagascar

Earthquake catalog is a key element in seism hazards. However, it may be contaminated by non-natural earthquake sources. Hence,
This study aims to discriminate natural and non-natural earthquakes through machine learning techniques and spatio-temporal distribution of the events. 
First, 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 of 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.
From the analyses of the spatio-temporal patterns of seismicity, we also found evidence of induced earthquakes associated with the heavy-oil exploration in Tsimiroro, Madagascar with an increase in the rate of earthquake occurrence in 2022.