Bayesian inversion for finite fault earthquake source models I—theory and algorithm

By S. Minson, Mark Simons, James L. Beck. 2013

268 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Geophysics

View Paper: https://www.semanticscholar.org/paper/f9f9b83a1bef648ff7444fb4b87ba29d18f8c627

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. A. Hooper
2. Marco Bagnardi

In Environmental Engineering

1. A. Hooper
2. Marco Bagnardi

In Geophysics

1. A. Hooper
2. Marco Bagnardi

Citations in the corpus

• Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach (2018, methodology): “…a Bayesian approach when inverting geodetic data (e.g., Anderson & Segall, 2013; Fukuda & Johnson, 2010; Hooper et al., 2013; Jolivet et al., 2015; Minson et al., 2013) allows the characterization of posterior probability density functions (PDFs) of source parameters, which are formulated by…”