Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

Abstract

[1] 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 mechanism-based 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 subfault structures as well as of the spatially inhomogeneous location uncertainties.

Objective estimation of spatially variable parameters of epidemic type aftershock sequence model: Application to California

“…It is possible that K might be dependent on the local faulting density, while α might depend on the scaling of this density with the size of the aftershock zone [ Helmstetter , ]. Thus, further understanding may come from local reconstructions of the fault network [ Ouillon and Sornette , ; Wang et al ., ; Nandan et al ., ] coupled with physics‐based models of stress transfer and rate‐and‐state friction [ Dieterich , ].…”

Condensation of earthquake location distributions: Optimal spatial information encoding and application to multifractal analysis of south Californian seismicity

“…The fractals are generated within a cube of 10km side length and contain 3360 points. For a realistic representation of location uncertainties, we use the covariance matrices of 3360 aftershock events following the 1992 M7.3 Landers earthquake [2]. The median values of the square roots of the eigenvalues are σ I =0.89 km, σ II =0.39 km and σ III =0.27 km.…”

“…These can be minimum magnitude, maximum location uncertainty or a specific time or space window. While large magnitude events are of greater importance for risk assessment studies, events with small location uncertainties are crucial for accurately mapping the active/potential unknown structures [1,2]. Some studies prefer limiting their data to the most recent periods, however, there is strong evidence that the notion of time invariance does not hold for seismicity distributions, at least on the time scales covered by existing catalogs.…”

A Nonparametric Approach for Assessing Precision in Georeferenced Point Clouds Best Fit Planes: Toward More Reliable Thresholds

“…Journal of Geophysical Research: Solid Earth,123,10,308. https://doi.org/10.1029/ 2018JB016319 Hodgetts, 2014; Slob et al, 2005;Thiele et al, 2017;Wang et al, 2013). These representations of facets permit best fit plane appraisal to be made in order to estimate the corresponding orientation (Dueholm & Olsen, 1993;Pringle et al, 2006).…”