QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach

Gravey, Mathieu; Mariethoz, Grégoire

doi:10.5194/gmd-13-2611-2020

Cited by 56 publications

(39 citation statements)

References 47 publications

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“…Many MPS algorithms exist today (Gravey and Mariethoz, 2020;Guardiano and Srivastava, 1993;T. M. Hansen et al, 2016;Hoffimann et al, 2017;Mariethoz et al, 2010;Straubhaar et al, 2011;Strebelle, 2002;Tahmasebi et al, 2012).…”

Section: Mps Modelingmentioning

confidence: 99%

“…tTEM is, therefore, ideal for high-resolution mapping when focusing on the uppermost 50 to 70 m of the subsurface. None of the previous studies has investigated the geological and redox architecture simultaneously, although these two are related and sometimes coevolved (Grenthe et al, 1992;B. Hansen et al, 2016;Wilkin et al, 1996;Yan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures

Madsen

Kim

Kallesøe

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Nitrate contamination of subsurface aquifers is an ongoing environmental challenge due to nitrogen (N) leaching from intensive N fertilization and management on agricultural fields. The distribution and fate of nitrate in aquifers are primarily governed by geological, hydrological and geochemical conditions of the subsurface. Therefore, we propose a novel approach to modeling both geology and redox architectures simultaneously in high-resolution 3D (25m×25m×2m) using multiple-point geostatistical (MPS) simulation. Data consist of (1) mainly resistivities of the subsurface mapped with towed transient electromagnetic measurements (tTEM), (2) lithologies from borehole observations, (3) redox conditions from colors reported in borehole observations, and (4) chemistry analyses from water samples. Based on the collected data and supplementary surface geology maps and digital elevation models, the simulation domain was subdivided into geological elements with similar geological traits and depositional histories. The conceptual understandings of the geological and redox architectures of the study system were introduced to the simulation as training images for each geological element. On the basis of these training images and conditioning data, independent realizations were jointly simulated of geology and redox inside each geological element and stitched together into a larger model. The joint simulation of geological and redox architectures, which is one of the strengths of MPS compared to other geostatistical methods, ensures that the two architectures in general show coherent patterns. Despite the inherent subjectivity of interpretations of the training images and geological element boundaries, they enable an easy and intuitive incorporation of qualitative knowledge of geology and geochemistry in quantitative simulations of the subsurface architectures. Altogether, we conclude that our approach effectively simulates the consistent geological and redox architectures of the subsurface that can be used for hydrological modeling with nitrogen (N) transport, which may lead to a better understanding of N fate in the subsurface and to future more targeted regulation of agriculture.

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Section: Mps Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures

Madsen

Kim

Kallesøe

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

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“…As for Gaussian based modeling, multiple algorithms exist that allow sampling from MPS models, such as ENESIM (Guardiano and Srivastava 1993), GENESIM (Hansen et al 2016), Direct Sampling (DS) (Mariethoz et al 2010), SNESIM (Strebelle 2002), IMPALA (Straubhaar et al 2011), CCSIM (Tahmasebi et al 2012), QS (Gravey and Mariethoz 2020). For an extended review of MPS sampling strategies and their drawbacks and benefits, the reader is referred to Mariethoz and Caers (2015).…”

Section: Mps Modelingmentioning

confidence: 99%

Choosing between Gaussian and MPS simulation: the role of data information content—a case study using uncertain interpretation data points

Madsen

Møller

Hansen

2021

Stoch Environ Res Risk Assess

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Integration of geophysical data with information such as from boreholes and expert domain knowledge is often performed as cognitive or explicit geological modeling when generating deterministic geological models of the subsurface. However, such cognitive geological models lack the ability to express the uncertainty of layer boundaries. To remedy the shortcomings of this strategy we propose a novel stochastic methodology combining the efforts of probabilistic data integration and cognitive modeling. We treat geological interpretation points from the cognitive model as uncertain “soft” data. These data are then combined with analogous geology in a probabilistic model. We test two ways of combining and sampling from such a probabilistic model. Firstly, a high-entropy setup based on Gaussian distributions simulation. Secondly, lower entropy (and conceivable more realistic) geological solutions are obtained from multiple-point geostatistics (MPS). We apply both ways of solving the problem at a study site near Horsens, Denmark, where airborne transient electromagnetic measurements, seismic data, and borehole information are available and interpreted in cognitive modeling. We explain the complete framework for integrating the uncertain interpretations in geostatistical simulation. Results show that both the Gaussian simulation and multiple-point geostatistical approach allows satisfactory simulations of uncertain geological interpretations and are consistent with prior geological knowledge. Our results suggest that the number of uncertain data points and their information content play a pivotal role in selecting the most appropriate simulation method for the given framework. MPS simulations allow connectivity in scenarios with few data points due to the low entropy of the model. When the number of soft data increases, Gaussian simulation is less prone to produce simulation artifacts, faster to apply, and could be considered preferential to using MPS simulation.

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“…satellite images) to fill remote sensing gaps (e.g. Gravey and Mariethoz, 2020;Mariethoz et al, 2012;Yin et al, 2017;Zakeri and Mariethoz, 2021;Zuo et al, 2020). MPS approaches use the training images (TIs) as explicit prior models to generate realistic topographical models and quantify spatial uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)

Yin

Zuo

MacKie

et al. 2021

Preprint

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Abstract. The subglacial bed topography is critical for modeling the evolution of Thwaites Glacier in the Amundsen Sea Embayment (ASE), where rapid ice loss threatens the stability of the West Antarctic Ice Sheet. However, mapping of subglacial topography is subject to high uncertainty. This is mainly because the bed topography is measured by airborne ice-penetrating radar along flight lines with large gaps up to tens of kilometers. Deterministic interpolation approaches do not reflect such spatial uncertainty. While traditional geostatistical simulation can model such uncertainty, it may be difficult to apply because of the significant non-stationary spatial variation of topography over such large surface area. In this study, we develop a non-stationary multiple-point geostatistical approach to interpolate large areas with irregular geophysical data and apply it to model the spatial uncertainty of entire ASE basal topography. We collect 166 high-resolution topographic training images (TIs) to train the gap-filling of radar data gaps, thereby simulating realistic topography maps. The TIs are extensively sampled from deglaciated regions in the Arctic as well as Antarctica. To address the non-stationarity in topographic modeling, we introduce a Bayesian framework that models the posterior distribution of non-stationary training images to the local modeling domain. Sampling from this distribution then provide candidate training images for local topographic modeling with uncertainty, constrained to radar flight line data. Compared to traditional MPS approaches without considering TI sampling, our approach demonstrates significant improvement in the topographic modeling quality and efficiency of the simulation algorithm. Finally, we simulate multiple realizations of high-resolution ASE topographic maps. We use the multiple realizations to investigate the impact of basal topography uncertainty on subglacial hydrological flow patterns.

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QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach

Cited by 56 publications

References 47 publications

3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures

3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures

Choosing between Gaussian and MPS simulation: the role of data information content—a case study using uncertain interpretation data points

Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)

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