Event-based features: An improved feature extraction approach to enrich machine learning based labquake forecasting

Earthquake forecasting is a highly complex and challenging task in seismology ultimately aiming to save human lives and infrastructures. In recent years, Machine Learning (ML) methods have demonstrated progressive achievements in earthquake processing and even labquake forecasting. Developing a more general and accurate ML model for more complex and/or limited datasets is obtained by refining the ‘ML models’ and/or enriching the ‘input data’. In this study, we present an event-based approach to enrich the input data by extracting spatio-temporal seismo-mechanical features that are dependent on the origin time and location of each event. Accordingly, we define and analyze a variety of features such as: (a) immediate features, defined as the features which benefit from very short characteristics of the considered event in time and space, (b) time-space features, based on the subsets of acoustic emission (AE) catalog constrained by time and space distance from the considered event, and (c) family features, extracted from topological characteristics of the clustered (family) events extracted from clustering analysis in different time windows. We use AE catalogs recorded by tri-axial stick-slip experiments on rough fault samples to compute event-based features. Then, a random forest classifier is applied to forecast the occurrence of a large magnitude event (M_AE>3.5) in the next time window. Results show that to obtain a more accurate forecasting model, one needs to separate background and clustered activities. Based on our results, the classification accuracy when the entire catalog data is used reaches 73.2%, however, it shows a remarkable improvement for separated background and clustered populations with an accuracy of 82.1% and 89.0%, respectively. Feature importance analysis reveals that not only AE-rate, seismic energy and b-value are important, but also family features developed from a topological tree decomposition play a crucial role for labquake forecasting.