New CNN based tool to discriminate anthropogenic from natural low magnitude seismic events

Over the past 15 years, the deployment of dense permanent seismic networks leads to a dramatic increase in the amount of data to process. The seismic coverage and the station quality pave the way toward a comprehensive catalogue of natural seismicity. This means to i) detect the lowest magnitudes as possible and ii) to discriminate natural from anthropogenic events. To achieve this discrimination, we present a new convolutional neural network (CNN) trained from 60 s long three component spectrograms between 1 and 50 Hz. This CNN is trained using a reliable database of labelled events located in Metropolitan France between January 2020 and June 2021. The application of our trained model on the detected events in Metropolitan France between June and November 2021 gives a high discrimination accuracy of 98.18%. To demonstrate the versatility of our approach, this trained model is applied to different catalogues: from a post-seismic campaign in NW France (48 events) and from University of Utah Seismograph Stations, Utah, USA, (396 events between January and March 2016). We reach an accuracy of 100.00% and 96.72%, respectively, for the discrimination between natural and anthropogenic events. Since each discrimination comes with a level of confidence, our approach can be seen as a decision making tool for the analysts. It also allows to build reliable seismic event catalogues and to reduce the number of mislabelled events in the databases.