Unsupervised clustering of catalogue-driven features for characterizing temporal evolution of labquake stress

Abstract

Earthquake forecasting poses significant challenges, especially due to the elusive nature of stress states in fault systems. To tackle this problem, we use features derived from seismic catalogues obtained from acoustic emission (AE) signals recorded during triaxial stick-slip experiments on natural fractures in three Westerly granite samples. We extracted 47 physically explainable features from AE data that described spatio-temporal evolution of stress and damage in the vicinity of the fault surface. These features are then subjected to unsupervised clustering using the K-means method, revealing three distinct stages with a proper agreement with the temporal evolution of stress. The recovered stages correspond to the mechanical behaviour of the rock, characterized as initial stable (elastic) deformation, followed by a transitional stage leading to an unstable deformation prior to failure. Notably, AE rate, clustering-localization features, fractal dimension, b-value, interevent time distribution, and correlation integral are identified as significant features for the unsupervised clustering. The systematically evolving stages can provide valuable insights for characterizing preparatory processes preceding earthquake events associated with geothermal activities and waste-water injections. In order to address the upscaling issue, we propose to use the most important features and, in case of normalization challenge, removing non-universal features, such as AE rate. Our findings hold promise for advancing earthquake prediction methodologies based on laboratory experiments and catalogue-driven features.