Methods and uncertainty-estimations of 3D structural modelling in crystalline rocks: A case study

Schneeberger, Raphael; Varga, Miguel De la; Egli, Daniel; Berger, Alfons; Kober, Florian; Wellmann, Florian; Herwegh, Marco

doi:10.5194/se-2017-47

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…The Grimsel area mainly consists of the Variscan intrusive and Post-Variscan metamorphic lithologies of the Aar Massive. The GTS is located at the boundary between the Central Aare Granite (CAGr) and the Grimsel Granodiorite (GrGr) 19 . Both lithologies consist of plutonic rocks of the Early Permian age that intruded the crystalline continental crust 299 ± 2 Ma ago 20 .…”

Section: Methodsmentioning

confidence: 99%

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Comprehensive geological dataset describing a crystalline rock mass for hydraulic stimulation experiments

et al. 2018

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High-resolution 3D geological models are crucial for underground development projects and corresponding numerical simulations with applications in e.g., tunneling, hydrocarbon exploration, geothermal exploitation and mining. Most geological models are based on sparse geological data sampled pointwise or along lines (e.g., boreholes), leading to oversimplified model geometries. In the framework of a hydraulic stimulation experiment in crystalline rock at the Grimsel Test Site, we collected geological data in 15 boreholes using a variety of methods to characterize a decameter-scale rock volume. The experiment aims to identify and understand relevant thermo-hydro-mechanical-seismic coupled rock mass responses during high-pressure fluid injections. Prior to fluid injections, we characterized the rock mass using geological, hydraulic and geophysical prospecting. The combination of methods allowed for compilation of a deterministic 3D geological analog that includes five shear zones, fracture density information and fracture locations. The model may serve as a decameter-scale analog of crystalline basement rocks, which are often targeted for enhanced geothermal systems. In this contribution, we summarize the geological data and the resulting geological interpretation.

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Section: Methodsmentioning

confidence: 99%

“…The S1 and S3 shear zones are persistent structures that can be traced over a distance of around 500 m to the surface 19 . Both shear zones have a core (i.e., zone of highest deformation) and an asymmetric habitus.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive geological dataset describing a crystalline rock mass for hydraulic stimulation experiments

et al. 2018

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“…Figure 1 shows several natural and human-made exposures of faults cutting granite gneiss at Grimselpass in the Swiss Alps. At this location faults are typically localised along mafic dykes (Schneeberger et al 2016(Schneeberger et al , 2017, and can be traced across the landscape in air photos for 4.6 km (Belgrano et al 2016). Figure 1a & b show two contrasting ways that the faults are exposed depending on the slope that they cut.…”

Section: Outcrop Scale Vs Human Scalementioning

confidence: 95%

Fault fictions: systematic biases in the conceptualization of fault-zone architecture

Shipton¹,

Roberts²,

Comrie

et al. 2019

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Mental models are a human's internal representation of the real world and have an important role in the way a human understands and reasons about uncertainties, explores potential options, and makes decisions. However, they are susceptible to biases. Issues associated with mental models have not yet received much attention in geosciences, yet systematic biases can affect the scientific process of any geological investigation; from the inception of how the problem is viewed, through selection of appropriate hypotheses and data collection/processing methods, to the conceptualisation and communication of results. This article draws on findings from cognitive science and system dynamics, with knowledge and experiences of field geology, to consider the limitations and biases presented by mental models in geoscience, and their effect on predictions of the physical properties of faults in particular. We highlight a number of biases specific to geological investigations and propose strategies for debiasing. Doing so will enhance how multiple data sources can be brought together, and minimise controllable geological uncertainty to develop more robust geological models. Critically, we argue that there is a need for standardised procedures that guard against biases, permitting data from multiple studies to be combined and communication of assumptions to be made. While we use faults to illustrate potential biases in mental models and the implications of these biases, our findings can be applied across the geoscience discipline. What do you think of when you think of a geologist? In a study of the representations of scientists in 222 Hollywood films, Weingart et al. (2003) found that scientists were predominantly white (96%), male (82%), American (49%), and middle aged (40% between 35 and 49 years old). They found interesting variations in the characters of scientists based on discipline. Medical researchers, physicists, chemists, and psychologists were most likely to be portrayed as 'mad scientists', whereas anthropologists, astronomers, zoologists, and geologists were more likely to be depicted as 'good' or 'benevolent'. Further, 'benevolent' scientists were often portrayed as naïve! The stereotypical portrayal of scientists in movies is an example of analogous thinking; that is, people's 'mental models', which are internal representations of something in the real world (Johnson-Laird 1983). Such analogous thinking helps people to interact with and make sense of a (complex) external reality, often by allowing them to solve problems or make judgments quickly and efficiently. However, mental models are developed from biased input information and are thus inherently limited. This can be both beneficial and problematic. Every model is a simplification of reality and therefore every model is wrong (Sterman 2002; Poeter 2007). Increasing the complexity of a mental model does not necessarily make the model more useful, however overly simplified models can omit key factors. For humans, models are the foundation of decision making, ...

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“…Such a multi-point rule could consider the throw distribution on the fault surface [as in Freeman et al, 1990] and could further reduce the number of possible scenarios. The association likelihoods should also be revised during the interpretation to capture that crossing faults at large-scale are unlikely [Schneeberger et al, 2017]. The conditional likelihood of associating two evidence, knowing that two other observations belong to the same fault, has not been considered yet.…”

Section: Geological Rules Considering Multi-points Costs and Conditiomentioning

confidence: 99%

“…and exploitation [Hollund et al, 2002, Thore et al, 2002, Rivenaes et al, 2005, Richards et al, 2015, Julio et al, 2015a, waste disposal [Mann, 1993, Schneeberger et al, 2017, environmental engineering [Rosenbaum and Culshaw, 2003] and geotechnical works [Zhu et al, 2003].…”

Section: Introductionmentioning

confidence: 99%

Structural Interpretation of Sparse Fault Data Using Graph Theory and Geological Rules

2019

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Structural uncertainties arise partly from the association of sparse fault interpretations made from 2D seismic lines or limited outcrop observations. We propose a graph formalism to describe the problem of associating spatial fault evidence. A combinatorial analysis shows that the number of association scenarios is related to the Bell number and increases exponentially. This makes the complete exploration of the uncertainties computationally highly challenging. We formulate prior geological knowledge as numerical rules to reduce the number of scenarios and to make structural interpretation more repeatable and objective. We use the Bron-Kerbosch graph algorithm to detect the major possible structures. This framework opens the way to a numerically assisted exploration of uncertainties during structural interpretation.

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Methods and uncertainty-estimations of 3D structural modelling in crystalline rocks: A case study

Abstract: Published by Copernicus Publications on behalf of the European Geosciences Union. R. Schneeberger et al.: 3-D structural modelling in crystalline rocks J 0 1 J 0 2 J 0 3 J 0 6 J 0 7 J 0 8

Cited by 5 publications

References 35 publications

Comprehensive geological dataset describing a crystalline rock mass for hydraulic stimulation experiments

Comprehensive geological dataset describing a crystalline rock mass for hydraulic stimulation experiments

Fault fictions: systematic biases in the conceptualization of fault-zone architecture

Structural Interpretation of Sparse Fault Data Using Graph Theory and Geological Rules

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