A spatio-temporal binary grid-based clustering model for seismicity analysis

This paper presents a spatio-temporal binary grid-based clustering model for determining complex earthquake clusters with different shapes and heterogeneous densities present in a catalog. The 3D occurrence of earthquakes is mapped into a 2D-low memory sparse matrix through a grid mechanism in the binary domain with consideration of spatio-temporal attributes. Then, image-transformation of a non-empty sets binary feature matrix, a clustering strategy is implemented with logical AND operator as similarity measure among the binary vectors. This approach is applied to solve the problem of seismicity declustering which separates the clustering and non-clustering patterns of seismicity for real-world earthquake catalogs of Japan (1972–2020) and Eastern Mediterranean (1966–2020). Results demonstrate that the proposed method has a significant reduction in both computation and memory footprint with few tuning parameters. Background earthquakes have an impression on the homogeneous Poisson process with fair memory-less characteristics in the time domain as evident from graphical and statistical analysis. Overall seismicity and observed background activity both have similar multi-fractal behavior with a deviation of \(\pm 0.04\) $\pm 0.04$ . The comparative analysis is carried out with benchmark declustering models: Gardner–Knopoff, Uhrhammer, Gruenthal window-based method, and Reasenberg’s approach, and superior performance of the proposed method is found in most cases.