October 2021: Xinyan Li

Oct 14, 2021, at 14:00 PT

Recording¶

Abstract¶

Collecting and interpreting multiple potential-field datasets have been popular in resource explorations because potential-field data contains valuable information about subsurface structures and compositions. The general interpretation workflow involves two steps. In the first step, potential-field data sets are inverted, either separately or jointly, to obtain multiple physical property models. In the second step, a process called geology differentiation is typically applied where inverted physical property values are classified into distinct classes. The implicit assumption here is that the recovered physical property relationships from geophysical inversions are reliable. However, whether this assumption holds true or not remains to be investigated. Thus, our first research question is: (1) under what conditions would the recovered physical property relationships become unreliable? Moreover, it is well known that the standard L₂-norm inversions would underestimate physical property values. How the underestimation affects the recoverability is underexplored. On the other hand, the sparse norm regularization method has proved to be able to recover models with compact boundaries and elevated values. Therefore, our second research question is: (2) how would the sparse norm inversions affect the recoverability of physical property relationships?

To investigate the recoverability of physical property relationships and to answer these questions, we have designed six geological scenarios with causative geological units at various depths and differed in physical property magnitudes. For each scenario, we simulated gravity and magnetic data, performed separate and joint inversions in both smooth L₂-norm and sparse L₁₂-norm, and followed by geology differentiation. Our work shows that (1) the recoverability of physical property relationships is significantly affected by the depths of the source bodies, and (2) joint sparsity inversion results in the best recoverability consistently in all scenarios. Our work provides a strong motivation for implementing joint sparsity inversion when the goal is to identify different geological units based on potential-field data. We are currently applying the same workflow to the airborne gravity and magnetic data collected over the QUEST project in British Columbia of Canada, where the objective is to identify prospective areas for future mineral exploration hindered by the thick glacial cover.

Bio¶

Xinyan Li is pursuing her Ph.D. degree in geophysics, in the Department of Earth and Atmospheric Sciences at the University of Houston. She received a B.Sc. degree in geophysics in 2017 also from the University of Houston. Her research interests focus on maximizing the value of geophysical data through 3D mixed Lp-norm joint inversion and geology differentiation. She currently works on joint inversion and recoverability analysis using multiple potential-field data, primarily applied to mineral prospectivity mapping in the QUEST project area in British Columbia of Canada.