Hamiltonian Monte Carlo Probabilistic Joint Inversion of 2D (2.75D) Gravity and Magnetic Data. Issue 20 (20th October 2022)

• Record Type: Journal Article
• Authors: Zunino, Andrea; Ghirotto, Alessandro; Armadillo, Egidio; Fichtner, Andreas
• Is Part Of: Geophysical research letters. Volume 49:Issue 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-20
• Subjects: magnetic anomaly -- gravity anomaly -- joint inversion -- Hamiltonian Monte Carlo -- polygonal bodies; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2022GL099789
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Two‐dimensional modeling of gravity and magnetic anomalies in terms of polygonal bodies is a popular approach to infer possible configurations of geological structures in the subsurface. Alternatively to the traditional trial‐and‐error manual fit of measured data, here we illustrate a probabilistic strategy to solve the inverse problem. First we derive a set of formulae for solving a 2.75‐dimensional forward model, where the polygonal bodies have a given finite lateral extent, and then we devise a Hamiltonian Monte Carlo algorithm to jointly invert gravity and magnetic data for the geometry and properties of the polygonal bodies. This probabilistic approach fully addresses the nonlinearity of the forward model and provides uncertainty estimation. The result of the inversion is a collection of models which represent the posterior distribution, analysis of which provides estimates of sought properties and may reveal different scenarios. Plain Language Summary: A probabilistic Monte Carlo method to infer the shape and properties of geological bodies in the subsurface from gravity and magnetic data is discussed in this paper. The geological bodies are represented as polygons with constant density and magnetic properties. The results of applying the proposed algorithm is a collection of models of the subsurface that can be statistically analyzed to infer structure and properties of the subsurface. Key Points: Contrary to the traditional manual approach, an objective procedure to jointly invert gravity and magnetic anomalies for 2.75D polygons Sampling using gradient information is an efficient nonlinear strategy to explore plausible solutions, where deterministic methods fail Interrogation of the collection of posterior solutions provides statistical answers to complex questions including uncertainty estimation