An Initial Exploration of Bayesian Model Calibration for Estimating the Composition of Rocks and Soils on Mars

TL;DR

This paper investigates Bayesian model calibration using the plasma physics code ATOMIC to estimate rock and soil composition on Mars from LIBS spectra, employing emulators for computational efficiency.

Contribution

It introduces the novel application of ATOMIC-based Bayesian calibration and emulators for LIBS spectrum disaggregation in planetary science.

Findings

Successfully recovered compositions of test spectra

Demonstrated feasibility of Bayesian calibration with ATOMIC

Developed a Gaussian process emulator for rapid analysis

Abstract

The Mars Curiosity rover carries an instrument, ChemCam, designed to measure the composition of surface rocks and soil using laser-induced breakdown spectroscopy (LIBS). The measured spectra from this instrument must be analyzed to identify the component elements in the target sample, as well as their relative proportions. This process, which we call disaggregation, is complicated by so-called matrix effects, which describe nonlinear changes in the relative heights of emission lines as an unknown function of composition due to atomic interactions within the LIBS plasma. In this work we explore the use of the plasma physics code ATOMIC, developed at Los Alamos National Laboratory, for the disaggregation task. ATOMIC has recently been used to model LIBS spectra and can robustly reproduce matrix effects from first principles. The ability of ATOMIC to predict LIBS spectra presents an exciting opportunity to perform disaggregation in a manner not yet tried in the LIBS community, namely via Bayesian model calibration. However, using it directly to solve our inverse problem is computationally intractable due to the large parameter space and the computation time required to produce a single output. Therefore we also explore the use of emulators as a fast solution for this analysis. We discuss a proof of concept Gaussian process emulator for disaggregating two-element compounds of sodium and copper. The training and test datasets were simulated with ATOMIC using a Latin hypercube design. After testing the performance of the emulator, we successfully recover the composition of 25 test spectra with Bayesian model calibration.