Gravity inversion for geothermal exploration with uncertainty quantification

Abstract

Gravity field data constitute an important underlying dataset used in exploration models for new geothermal development, providing constraints on both basin geometry and fault locations. However, the impact of data and model uncertainty on gravity interpretation is often overlooked in conventional gravity modeling approaches, leading to geothermal exploration models that may be biased or undercount risk. In this study, a stochastic approach to gravity modeling is used to investigate these impacts on gravity data from Dixie Valley geothermal field in central Nevada. To address data uncertainty due to interpolation of irregularly spaced gravity measurements, realizations of the gravity field in Dixie Valley are generated by geostatistical simulation and independently inverted to show how inversion results are affected by sparse data sampling. Inversion is performed using a pseudo 3D approach in which subparallel profiles are inverted using a novel stochastic methodology that was developed to account for structural and density uncertainty. Whereas areas with high data density show relatively consistent inversion results across data realizations, areas with low data density show the opposite, indicating that data uncertainty has a marked impact on both depth-to-basement estimates as well as modeled fault locations. Inverted fault locations show high correlation with the total horizontal gravity field gradient, enabling co-simulation of fault-related properties to estimate fault location and density variation beyond profile transects. By employing stochastic modeling approaches, fault properties needed for geothermal exploration such as distance-to-fault maps are estimated with uncertainty bounds at specified depths.