Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

TL;DR

This paper presents ACLUD, a novel method for reconstructing fault networks from seismic data that incorporates location uncertainties and uses multiple validation techniques to improve accuracy.

Contribution

The paper introduces ACLUD, a new clustering method that accounts for location uncertainties and employs multiple validation procedures for fault network reconstruction.

Findings

ACLD accurately reconstructs fault networks from synthetic data.

Validation with focal mechanisms improves solution reliability.

Method performs well even with background seismicity.

Abstract

We introduce the Anisotropic Clustering of Location Uncertainty Distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, we apply six different validation procedures in order to select the corresponding best fault network. Two of the validation steps (cross-validation and Bayesian Information Criterion (BIC) process the fit residuals, while the four others look for solutions that provide the best agreement with independently observed focal mechanisms. Tests on synthetic catalogs allow us to qualify the performance of the fitting method and of the various validation procedures. The ACLUD method is able to provide solutions that are close to the expected ones, especially for the BIC and focal mechanismbased techniques. The clustering method complemented by the validation step based on focal mechanisms provides good solutions even in the presence of a significant spatial background seismicity rate. Our new fault reconstruction method is then applied to the Landers area in Southern California and compared with previous clustering methods. The results stress the importance of taking into account undersampled sub-fault structures as well as of the spatially inhomogeneous location uncertainties.