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.

重力场数据构成了用于新地热开发勘探模型的重要基础数据集，为盆地几何形状和断层位置提供了约束。然而，数据和模型不确定性对重力解释的影响在传统的重力建模方法中经常被忽视，导致地热勘探模型可能存在偏差或低估风险。在这项研究中，重力建模的随机方法用于研究这些对内华达州中部迪克西谷地热田重力数据的影响。为了解决由不规则间隔重力测量插值引起的数据不确定性，Dixie Valley 重力场的实现是通过地统计模拟生成的，并独立反演以显示稀疏数据采样如何影响反演结果。反演是使用伪 3D 方法进行的，其中使用一种新颖的随机方法来反演次平行剖面，该方法是为了解决结构和密度的不确定性而开发的。数据密度高的区域在数据实现中显示出相对一致的反演结果，而数据密度低的区域则相反，这表明数据不确定性对地基深度估计以及模型断层位置都有显着影响。倒置断层位置与总水平重力场梯度高度相关，能够对断层相关特性进行联合仿真，以估计剖面断面以外的断层位置和密度变化。通过采用随机建模方法，