Abstract
Abstract. Characterizing the zone of damaged and altered rock surrounding a fault surface is highly relevant to geotechnical and geo-environmental engineering works in the subsurface. Evaluating the uncertainty associated with 3D geologic modeling of these fault zones is made possible using the popular and flexible input-based uncertainty propagation approach to geologic model uncertainty assessment – termed probabilistic geomodeling. To satisfy the automation requirements of probabilistic geomodeling while still preserving the key geometry of fault zones in the subsurface, a clear and straightforward modeling approach is developed based on four geologic inputs used in implicit geologic modeling algorithms (surface trace, structural orientation, vertical termination depth and fault zone thickness). The rationale applied to identifying and characterizing the various sources of uncertainty affecting each input are explored and provided using open-source codes. In considering these sources of uncertainty, a novel model formulation is implemented using prior geologic knowledge (i.e., empirical and theoretical relationships) to parameterize modeling inputs which are typically subjectively interpreted by the modeler (e.g., vertical termination depth of fault zones). Additionally, the application of anisotropic spherical distributions to modeling disparate levels of information available regarding a fault zone's dip azimuth and dip angle is demonstrated, providing improved control over the structural orientation uncertainty envelope. The probabilistic geomodeling approach developed is applied to a simple fault zone geologic model built from historically available geologic mapping data, allowing for a visual comparison of the independent contributions of each modeling input on the combined model uncertainty, revealing that vertical termination depth and structural orientation uncertainty dominate model uncertainty at depth, while surface trace uncertainty dominates model uncertainty near the ground surface. The method is also successfully applied to a more complex fault network model containing intersecting major and minor fault zones. The impacts of the model parameterization choices, the fault zone modeling approach and the effects of fault zone interactions on the final geologic model uncertainty assessment are discussed.
Highlights
Building on the existing literature on understanding the uncertainties about faults in the subsurface (Choi et al, 2016; Shipton et al, 2019; Torabi et al, 2019b), this study develops a novel, dedicated approach to leveraging probabilistic geomodeling to characterize the uncertainty in fault zones using 3D geologic models
This study focuses on the first method of applying prior knowledge from published structural geology literature (Torabi et al, 2019a) to parameterize the reasoning behind subjective inputs used for probabilistic geomodeling of fault zones
This study explores a novel application of anisotropic spherical distributions in probabilistic geomodeling: characterizing subjective bias in the structural orientation uncertainty in fault zones
Summary
Three-dimensional (3D) geologic models are becoming the state of the art for the prediction and communication of subsurface geology in a wide range of projects (Turner and Gable, 2007; Wellmann and Caumon, 2018) including regional geologic characterization (Stafleu et al, 2012; Waters et al, 2015), natural resource exploration (Zhou et al, 2007, 2015; Zhou, 2009; Anderson et al, 2014), structural geology (Bond et al, 2015; Ailleres et al, 2019), geotechnical site characterization (Thum and De Paoli, 2015; Zhu et al, 2013), geophysics (Guillen et al, 2008; Høyer et al, 2015; Anderson et al, 2014), hydrology (Watson et al, 2015), and mining (Wellmann et al, 2018; Yang et al, 2019). The development of novel probabilistic geomodeling approaches to address specific aspects of 3D geologic modeling will lead to growth in the field by broadening the usability of the method and by advancing the understanding of the method’s strengths and limitations. In addition to assessing the uncertainty in a single geologic model, probabilistic geomodeling using Monte Carlo sampling naturally fits into Bayesian inference schemes (de la Varga and Wellmann, 2016; Salvatier et al, 2016; Scalzo et al, 2019; Thiele et al, 2019), allowing for future refinement of model uncertainty as new information is made available. Building on the existing literature on understanding the uncertainties about faults in the subsurface (Choi et al, 2016; Shipton et al, 2019; Torabi et al, 2019b), this study develops a novel, dedicated approach to leveraging probabilistic geomodeling to characterize the uncertainty in fault zones using 3D geologic models. A simplified approach to modeling fault zones in 3D geologic models is developed in this study based on the key elements defining fault zone geometry at a practical level of detail
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