Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone

Geostatistical Rock Physics Inversion for Predicting the Spatial Distribution of Porosity and Saturation in the Critical Zone
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预测关键带孔隙度和饱和度空间分布的地统计岩石物理反演

DOI:
10.1007/s11004-022-10006-0
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发表时间:
2022
影响因子:
2.6
通讯作者:
Riebe, Clifford S.
Riebe, Clifford S.
中科院分区:
地球科学3区
文献类型:
--
作者:
Grana, Dario;Parsekian, Andrew D.;Flinchum, Brady A.;Callahan, Russell P.;Smeltz, Natalie Y.;Li, Ang;Hayes, Jorden L.;Carr, Brad J.;Singha, Kamini;Riebe, Clifford S.

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了解近地表地下水系统的地下结构和功能,包括流体流动,地质力学和风化过程,需要准确预测岩石物理性质的空间分布,如岩石和流体(空气和水)的体积分数。这些属性可以预测地球物理测量,如电阻率层析成像和折射地震数据,通过解决岩石物理反问题。贝叶斯反演方法的基础上的Monte Carlo实现贝叶斯更新问题的开发,以产生多个实现的孔隙度和含水饱和度的地球物理数据的条件。模型实现使用地统计算法生成,并根据集合平滑的方法,一种有效的贝叶斯数据同化技术更新。先验分布包括空间相关函数,使得模型实现模仿地质空间连续性。反演的结果包括一组孔隙度和含水饱和度的实现,以及最可能的模型及其不确定性,这对于了解临界区的流体流动,地质力学和风化过程至关重要。所提出的方法是验证两个合成的数据集的动机南塞拉利昂临界区天文台,然后应用于收集的数据在拉勒米,怀俄明州附近的一个山坡上。反演结果与实测值吻合较好,荣誉空间相关先验模型,为地表风化岩石提供了地质真实的岩石物理模型。
Understanding the subsurface structure and function in the near-surface groundwater system, including fluid flow, geomechanical, and weathering processes, requires accurate predictions of the spatial distribution of petrophysical properties, such as rock and fluid (air and water) volumetric fractions. These properties can be predicted from geophysical measurements, such as electrical resistivity tomography and refraction seismic data, by solving a rock physics inverse problem. A Bayesian inversion approach based on a Monte Carlo implementation of the Bayesian update problem is developed to generate multiple realizations of porosity and water saturation conditioned on geophysical data. The model realizations are generated using a geostatistical algorithm and updated according to the ensemble smoother approach, an efficient Bayesian data assimilation technique. The prior distribution includes a spatial correlation function such that the model realizations mimic the geological spatial continuity. The result of the inversion includes a set of realizations of porosity and water saturation, as well as the most likely model and its uncertainty, that are crucial to understand fluid flow, geomechanical, and weathering processes in the critical zone. The proposed approach is validated on two synthetic datasets motivated by the Southern Sierra Critical Zone Observatory and is then applied to data collected on a mountain hillslope near Laramie, Wyoming. The inverted results match the measurements, honor the spatial correlation prior model, and provide geologically realistic petrophysical models of weathered rock at Earth’s surface.
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