Objective Estimation of Spatially Variable Parameters of Epidemic Type Aftershock Sequence Model: Application to California
Shyam Nandan, Guy Ouillon, Stefan Wiemer, Didier Sornette

TL;DR
This paper introduces a novel method using EM algorithm and Voronoi tessellation to estimate spatially variable ETAS model parameters, revealing significant spatial variations and correlations with heat flow in California earthquakes.
Contribution
It develops an efficient approach for spatially varying parameter estimation in the ETAS model, validated with synthetic data and applied to California earthquake data.
Findings
ETAS parameters vary significantly across California.
Branching ratio correlates positively with surface heat flow.
Triggering is dominated by small earthquakes.
Abstract
The ETAS model is widely employed to model the spatio-temporal distribution of earthquakes, generally using spatially invariant parameters. We propose an efficient method for the estimation of spatially varying parameters, using the Expectation-Maximization (EM) algorithm and spatial Voronoi tessellation ensembles. We use the Bayesian Information Criterion (BIC) to rank inverted models given their likelihood and complexity and select the best models to finally compute an ensemble model at any location. Using a synthetic catalog, we also check that the proposed method correctly inverts the known parameters. We apply the proposed method to earthquakes included in the ANSS catalog that occurred within the time period 1981-2015 in a spatial polygon around California. The results indicate a significant spatial variation of the ETAS parameters. We find that the efficiency of earthquakes to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
