A Geometric Approach to Joint Inversion with Applications to Contaminant Source Zone Characterization
Alireza Aghasi, Itza Mendoza-Sanchez, Eric L. Miller, C. Andrew, Ramsburg, Linda M. Abriola

TL;DR
This paper introduces a new geometric joint inversion method using parametric level sets to effectively characterize subsurface contaminant source zones by integrating hydrological and electrical data, improving reconstruction accuracy.
Contribution
The paper proposes a novel iterative joint inversion approach with a parametric level set technique for efficient, shape-based reconstruction of subsurface contaminant zones from multiple data types.
Findings
The method accurately reconstructs DNAPL source zone geometries.
Joint inversion outperforms single-data approaches in accuracy.
The parametric level set reduces computational complexity significantly.
Abstract
This paper presents a new joint inversion approach to shape-based inverse problems. Given two sets of data from distinct physical models, the main objective is to obtain a unified characterization of inclusions within the spatial domain of the physical properties to be reconstructed. Although our proposed method generally applies to many types of inversion problems, the main motivation here is to characterize subsurface contaminant source-zones by processing down gradient hydrological data and cross-gradient electrical resistance tomography (ERT) observations. Inspired by Newton's method for multi-objective optimization, we present an iterative inversion scheme that suggests taking descent steps that can simultaneously reduce both data-model misfit terms. Such an approach, however, requires solving a non-smooth convex problem at every iteration, which is computationally expensive for a…
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.
