Post-processing for uncertainty reduction in computed 3D geological models

Yamamoto, Jorge Kazuo; Koike, Katsuaki; Kikuda, Antonio Tadashi; Campanha, Ginaldo Ademar da Cruz; Endlen, Arthur

doi:10.1016/j.tecto.2014.07.013

Cited by 18 publications

(19 citation statements)

References 9 publications

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“…This average image still contains high uncertainties. To reduce the uncertainty in the average model, we can resample and then post-process it using the new sample ( [15], p. 236-237). If the post-processed model is not good enough, we can resample it and perform the post-processing again.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, we can apply a new post-processing technique using exhaustive resampling of the most likely model ( [15], p. 236-237). This post-processing technique reads the binary file for the most likely model to search for uncertain cells.…”

Section: Post-processingmentioning

confidence: 99%

“…The method includes two different post-processing procedures: (1) applying an averaging filter to reduce the effect of noise;nd (2) exhaustive resampling and then post-processing the computed model with this new sample. The latter postprocessing procedure is based on ( [15], p. 236-237).…”

Section: Introductionmentioning

confidence: 99%

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Post-processing of sequential indicator simulation realizations for modeling geologic bodies

et al. 2015

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Sequential indicator simulation realizations contain unavoidable artifacts that are geologically unrealistic. This happens because unlikely types can be drawn randomly from the cumulative distribution and be assigned to a cell in the simulated model. This cell may then be used as previously simulated data when a cell in its neighborhood is visited during a random walk. The sequential process sometimes results in geologically unrealistic realizations. However, different realizations can reveal hidden features. Each realization contains both reliable geological information and noise that is displayed as unlikely types. This paper proposes applying the averaging filter that is commonly used in seismic reflection data to improve the signal to noise ratio. After applying this filter, all L realizations will be condensed into a single geological model that contains certain and uncertain cells. This average model is then exhaustively sampled for the certain cells, and this new sample is used to post-process the uncertain cells to reduce the uncertainty. This resampling and post-processing procedure can be repeated until the final model is considered to be good enough. The proposed method is tested with a model of a dike that crosscuts two sedimentary units. The synthetic geologic model was sampled with 24 drill holes. The resultsshow that the final geological model with reduced uncertainty reproduces very well the sedimentary units and the orientation of the dike as well. The dike shape is not fully reproduced and still presents uncertainties because of lack of neighbor data.

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Section: Methodsmentioning

confidence: 99%

Section: Post-processingmentioning

confidence: 99%

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Post-processing of sequential indicator simulation realizations for modeling geologic bodies

et al. 2015

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“…1). Yamamoto et al [7] showed a strong correlation between the interpolation variance and information entropy. This paper presents one more application of the concept of uncertainty zone for volumetric simulation.…”

Section: Review Of Previous Papersmentioning

confidence: 98%

Determination of volumetric uncertainty for geological bodies detected by boreholes

et al. 2015

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“…Bistacchi et al, 2008;Bond et al, 2007a;Lindsay et al, 2012;Tacher et al, 2006;Wellmann et al, 2010Wellmann et al, , 2014Wellmann and Regenauer-Lieb, 2012;Yamamoto et al, 2014). Since models are a function of the data used, some of the approaches tend to analyse uncertainties in the input data before modelling (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Abstract. Exhumed basement rocks are often dissected by faults, the latter controlling physical parameters such as rock strength, porosity, or permeability. Knowledge on the threedimensional (3-D) geometry of the fault pattern and its continuation with depth is therefore of paramount importance for applied geology projects (e.g. tunnelling, nuclear waste disposal) in crystalline bedrock. The central Aar massif (Central Switzerland) serves as a study area where we investigate the 3-D geometry of the Alpine fault pattern by means of both surface (fieldwork and remote sensing) and underground ground (mapping of the Grimsel Test Site) information. The fault zone pattern consists of planar steep major faults (kilometre scale) interconnected with secondary relay faults (hectometre scale). Starting with surface data, we present a workflow for structural 3-D modelling of the primary faults based on a comparison of three extrapolation approaches based on (a) field data, (b) Delaunay triangulation, and (c) a best-fitting moment of inertia analysis. The quality of these surface-data-based 3-D models is then tested with respect to the fit of the predictions with the underground appearance of faults. All three extrapolation approaches result in a close fit ( > 10 %) when compared with underground rock laboratory mapping. Subsequently, we performed a statistical interpolation based on Bayesian inference in order to validate and further constrain the uncertainty of the extrapolation approaches. This comparison indicates that fieldwork at the surface is key for accurately constraining the geometry of the fault pattern and enabling a proper extrapolation of major faults towards depth. Considerable uncertainties, however, persist with respect to smaller-sized secondary structures because of their limited spatial extensions and unknown reoccurrence intervals.

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Post-processing for uncertainty reduction in computed 3D geological models

Cited by 18 publications

References 9 publications

Post-processing of sequential indicator simulation realizations for modeling geologic bodies

Post-processing of sequential indicator simulation realizations for modeling geologic bodies

Determination of volumetric uncertainty for geological bodies detected by boreholes

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

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