Empowering Machine Learning Forecasting of Labquake Using Event-Based Features and Clustering Characteristics

Abstract

Following recent advances of machine learning (ML), we present a novel approach to extract spatiotemporal seismo-mechanical features from Acoustic Emission (AE) catalogs to empower ML-based forecasting. The AE data were recorded during laboratory stick-slip experiments on granite samples cut by rough faults. Based on the features computed for a past time window, a random forest (RF) classifier is used to forecast the occurrence of a large magnitude event (MAE > 3.5) in the next time window. Event-based features allow us to associate informative time-space characteristics to each feature and nearest-neighbor clustering analysis enables us to separate background and clustered seismicity and train individual models. The results show that the separation of AEs enhances the forecasting accuracy from 73.2% for the entire catalog up to 82.1% and 89.0% if background and clustered events are used separately. The presented new approach may be upscaled for applications to forecast tectonic earthquakes.