LAUSR

Geothermal systems are usually characterized by distinct electrical resistivity structures due to their close relationship with the distribution of the temperature, geothermal fluids, and clay minerals. Geophysical electromagnetic (EM) methods have been routinely used for geothermal exploration. However, inversion models independently derived from different EM datasets often exhibit ambiguous resistivity features, causing difficulty in reliable interpretation. This work employs a 3-D Gauss–Newton (GN) approach to jointly invert collocated magnetotelluric (MT) and controlled-source EM (CSEM) data to better characterize subsurface resistivity structures. To effectively integrate complementary information in different datasets, we have developed a data gradient weighting adaptive joint inversion algorithm based on the norms of individual data residuals to balance contributions from each dataset during the joint inversion process. We first demonstrate the effectiveness of the developed approach on synthetic MT and CSEM data generated from a simplified geothermal model. Then, we apply the 3-D joint inversion approach to the MT and CSEM field datasets from the Yanggao region in Shanxi Province, China, to image a potential high-temperature geothermal system. Compared to results from single inversions, the joint inversion results demonstrate improved model characterization for both conductive and resistive structures, which is consistent with the results from the seismic surveys. From the joint inversion results, we can clearly delineate the important components of the geothermal system in the region, including thermal reservoirs, hydrothermal alteration layers, as well as conduction channels connecting them. Finally, we propose a conceptual model of the geothermal system in the region, which will be helpful for future drilling purposes.