Towards a better understanding of the recoverability of physical property relationships from geophysical inversions of multiple potential-field data sets

Abstract

SUMMRY Collecting multiple potential-field data sets to image the subsurface has been popular in resource explorations. How to effectively integrate multiphysics data and extract useful information about the subsurface geology is an active area of research. Among existing approaches, geophysical inversions followed by geological classification has shown promising results. This procedure contains two steps. First, multiple geophysical data sets are inverted, either separately or jointly, to obtain their corresponding physical property models. Secondly, the inverted values are visualized in a scatterplot and classified into different groups, a process known as geology differentiation. The implicit assumption for geology differentiation is that the recovered physical property relationships are reliable. However, whether this assumption is generally valid or not remains unknown. Moreover, it is well known that the standard smooth inversion would underestimate the physical property values. On the other hand, the sparse inversion is able to recover compact anomalies with elevated magnitudes. However, it remains unclear as to how smooth and sparse inversions affect the recoverability. To understand the recoverability of physical property relationships, we have designed six geological scenarios with three geological anomalies located at different depths and assigned with different physical property values. For each scenario, we performed four inversions: both separate and joint inversions using smooth L2-norm and sparse mixed L12-norm regularizations. Each inversion is followed by geology differentiation. We have found (1) that the recovered physical property relationships from geophysical inversions are not always reliable, (2) the depths of the source bodies and inversion strategies significantly affect the recoverability, and (3) joint sparsity inversion consistently gives rise to the best recoverability.