The quantitative connections between subsurface geologic structure and measured geophysical data allow 3D geologic models to be tested against measurements and geophysical anomalies to be interpreted in terms of geologic structure. Using a Bayesian framework, geophysical inversions are constrained by prior information in the form of a reference geologic model and probability density functions (pdfs) describing petrophysical properties of the different lithologic units. However, it is challenging to select the probabilistic weights and the structure of the prior model in such a way that the inversion process retains relevant geologic insights from the prior while also exploring the full range of plausible subsurface models. In this study, we investigate how the uncertainty of the prior (expressed using probabilistic constraints on commonality and shape) controls the inferred lithologic and mass density structure obtained by probabilistic inversion of gravimetric data measured at the Krafla geothermal system. We combine a reference prior geologic model with statistics for rock properties (grain density and porosity) in a Bayesian inference framework implemented in the GeoModeller software package. Posterior probability distributions for the inferred lithologic structure, mass density distribution, and uncertainty quantification metrics depend on the assumed geologic constraints and measurement error. As the uncertainty of the reference prior geologic model increases, the posterior lithologic structure deviates from the reference prior model in areas where it may be most likely to be inconsistent with the observed gravity data and may need to be revised. In Krafla, the strength of the gravity field reflects variations in the thickness of hyaloclastite and the depth to high-density basement intrusions. Moreover, the posterior results suggest that a WNW–ESE-oriented gravity low that transects the caldera may be associated with a zone of low hyaloclastite density. This study underscores the importance of reliable prior constraints on lithologic structure and rock properties during Bayesian geophysical inversion.

中文翻译：

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重力数据约束的克拉夫拉地热系统的概率地质模型

地下地质结构与测得的地球物理数据之间的定量联系使3D地质模型可以针对测量进行测试，并且可以根据地质结构解释地球物理异常。使用贝叶斯框架，地球物理反演受到参考地质模型和描述不同岩性单元岩石物理特性的概率密度函数（pdf）形式的先验信息的约束。然而，以这样的方式选择先验模型的概率权重和结构是具有挑战性的，即反演过程保留了先验模型的相关地质知识，同时还探索了所有可能的地下模型。在这个研究中，我们研究了先验的不确定性（用对共性和形状的概率约束表示）如何控制通过在克拉夫拉地热系统测量的重量数据的概率反演而获得的推断岩性和质量密度结构。我们在GeoModeller软件包中实现的贝叶斯推断框架中将参考先验地质模型与岩石属性（晶粒密度和孔隙度）的统计数据结合在一起。推断岩性结构的后验概率分布，质量密度分布和不确定性量化指标取决于假定的地质约束和测量误差。随着参考先验地质模型的不确定性增加，后岩性结构在最有可能与观测重力数据不一致并可能需要修改的区域偏离参考先验模型。在克拉夫拉，重力场的强度反映出透明质岩厚度和高密度地下侵入体深度的变化。此外，后验结果表明，横穿破火山口的以WNW-ESE为主的重力低区可能与玻裂石密度低的区域有关。这项研究强调了贝叶斯地球物理反演中可靠的先验约束对岩性结构和岩石性质的重要性。重力场的强度反映出透明质岩厚度和高密度地下侵入体深度的变化。此外，后验结果表明，横穿破火山口的以WNW-ESE为主的重力低区可能与玻裂石密度低的区域有关。这项研究强调了贝叶斯地球物理反演中可靠的先验约束对岩性结构和岩石性质的重要性。重力场的强度反映出透明质岩厚度和高密度地下侵入体深度的变化。此外，后验结果表明，横穿破火山口的以WNW-ESE为主的重力低区可能与玻裂石密度低的区域有关。这项研究强调了贝叶斯地球物理反演中可靠的先验约束对岩性结构和岩石性质的重要性。