Focused time-lapse inversion of radio and audio magnetotelluric data

TL;DR

This study explores advanced inversion techniques for radio and audio magnetotelluric data to improve groundwater monitoring, demonstrating that time-lapse difference inversion with non-l2 norms enhances model accuracy and reduces artifacts.

Contribution

It introduces novel inversion methods incorporating geostatistical regularization, alternative norms, and constraints, specifically tailored for AMT and RMT data in groundwater applications.

Findings

Time-lapse difference inversion improves model accuracy.

Non l2 norms better resolve sharp and smooth interfaces.

Constraints reduce artifacts and oscillations.

Abstract

Geoelectrical techniques are widely used to monitor groundwater processes, while surprisingly few studies have considered audio (AMT) and radio (RMT) magnetotellurics for such purposes. In this numerical investigation, we analyze to what extent inversion results based on AMT and RMT monitoring data can be improved by (1) time-lapse difference inversion; (2) incorporation of statistical information about the expected model update (i.e., the model regularization is based on a geostatistical model); (3) using alternative model norms to quantify temporal changes (i.e., approximations of l1 and Cauchy norms using iteratively reweighted least-squares), (4) constraining model updates to predefined ranges (i.e., using Lagrange Multipliers to only allow either increases or decreases of electrical resistivity with respect to background conditions). To do so, we consider a simple illustrative model and a more realistic test case related to seawater intrusion. The results are encouraging and show significant improvements when using time-lapse difference inversion with non l2 model norms. Artifacts that may arise when imposing compactness of regions with temporal changes can be suppressed through inequality constraints to yield models without oscillations outside the true region of temporal changes. Based on these results, we recommend approximate l1-norm solutions as they can resolve both sharp and smooth interfaces within the same model.