Effects of source model variations on Coulomb stress analyses of a multi-fault intraplate earthquake sequence

Abstract

Fault models are quickly produced and iteratively improved over weeks to years following a major earthquake, to characterise the dynamics of rupture, evaluate the role of stress transfer, and contribute to earthquake forecasting. We model Coulomb stress transfer (ΔCFS) between the largest foreshock (Mw 5.4; 1 year prior to first mainshock) and three Mw 6.1 to 6.5 earthquakes that occurred in a 12-hour period on January 22, 1988 in central Australia (Tennant Creek earthquake sequence) to investigate the role of static stress transfer in earthquake triggering relative to progressive source model development. The effects of fault model variance are studied using ΔCFS modelling of five different fault source model sequences (27 total models) using different inputs from seismic and geospatial data. Some initial models do not yield positive ΔCFS changes proximal to hypocentres but in all models, preceding earthquakes generate positive ΔCFS (≥0.1 bar) on ≥10 to 30% of the forthcoming receiver fault rupture areas. The most refined and data-integrative model reveals ΔCFS ≥ +0.7 to +13 bars within 2 km of impending hypocentres and large (≥30 to 99%) areas of positive ΔCFS. When compared to global compilations of threshold ΔCFS prior to impending ruptures (average = 3.71 bar, median = 1 bar), this suggests that Coulomb stress change theory adequately explains the Tennant Creek rupture sequence. In the most-refined model, earthquake inter-event times decrease as ΔCFS increases, suggesting that higher stress magnitudes may have more rapidly (within hours) triggered successive events, thus accounting for some temporal aspects of this sequence. ΔCFS analyses provide a useful framework for understanding the spatiotemporal aspects of some intraplate earthquakes. The progressive refinement of source models using emergent data may reduce epistemic uncertainties in the role of stress transfer that result from different model inputs, approaches, and results.