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Conventional gravity inversion techniques have limited ability to quantify structural uncertainty in geologic models. In this paper, a stochastic framework is proposed that directly incorporates fault-related and density-related uncertainty into the inversion process. The approach uses Monte Carlo simulation to generate model realizations and the gradual deformation method to further refine models to match observed data. To guarantee that model realizations are structurally restorable, fault displacements are generated using a kinematic modeling approach in which fault model properties such as the number of faults, location, dip, slip, and orientation are considered uncertain. Using a synthetic case study problem, a reference gravity field was inverted to generate a suite of posterior model realizations. Analysis of the posterior models was used to create a fault probability map as well as quantify the distribution of slip and dip of faults in three zones of deformation. Uncertainty in density values was found to be greatly reduced in the top 250 m depth, suggesting limited sensitivity to deeper sources in this example. Following the synthetic case study problem, the inversion approach was applied to a field-observed gravity profile in Dixie Valley, Nevada, and the inversion results were compared to a previously published forward gravity model. By generating a suite of posterior models, structural uncertainty can be better assessed to make more informed decisions in a host of subsurface modeling problems. [ABSTRACT FROM AUTHOR]

• Evaluation of the impact of uncertainty on key geothermal predictors from gravity field data in Dixie Valley geothermal field, Nevada, USA. • Geostatistical simulation used to quantify the effect of irregularly spaced gravity observations on inversion results. • Stochastic approach to gravity inversion to account for model uncertainty. 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. [ABSTRACT FROM AUTHOR]