Fast multiple-point simulation using a data-driven path and an efficient gradient-based search

Abdollahifard, Mohammad Javad

doi:10.1016/j.cageo.2015.10.010

Cited by 25 publications

(8 citation statements)

References 27 publications

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“…The Fig. 10 a, b Two realizations produced using FPSIM [14], c, d two realizations obtained from IQ [12]. Template size is 35 × 35 for both methods, number replicas N rep is 3 for FPSIM, and 10 for IQ.…”

Section: Application Of the Methodologymentioning

confidence: 99%

“…While pixel-based methods transfer only one pixel (voxel) from each found match [5][6][7][8][9][10], patch-based methods achieve higher speed and better pattern reproduction performance by transferring a large number of pixels (voxels) at once [11][12][13][14][15][16][17][18]. However, patch-based methods suffer from some important limitations including difficulty of handling conditioning data, low variability, and verbatim copy of the large patches of the TI into the SG (i.e., large portions copied identically from the TI).…”

Section: Introductionmentioning

confidence: 99%

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Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors

2019

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Continuous growth of multiple-point simulation algorithms for modeling environmental variables necessitates a straightforward, reliable, robust, and distinctive method for evaluating the quality of output images. A good simulation method should produce realizations consistent with the training image (TI). Moreover, it should be capable of producing diverse realizations to effectively model the variability of real fields. In this paper, the pattern innovation capability is evaluated by estimating the coherence map using keypoint detection and matching, without assuming any access to the simulation process. Local binary patterns, as distinctive and effective texture descriptors, are also employed to evaluate the consistency of realizations with the TI. Our proposed method provides absolute measures in the interval [0,1], allowing MPS algorithms to be evaluated on their own. Experiments show that the produced scores are consistent with human perception and robust for different realizations obtained using the same method, allowing for a reliable judgment using a few realizations. While a human observer is highly sensitive to discontinuities and insensitive to verbatim copies, the proposed method considers both factors simultaneously.

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Section: Application Of the Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors

2019

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“…Besides, the reduction on the search space can be controlled by a parameter α. As an example, for the experiments with 2D categorical TIs, to be presented in Chapter 5, we use α = 0.5%, which reduces the original LSHSIM: A Locality Sensitive Hashing Based Method for Multiple-Point Geostatistics 24 space of patterns by a factor of at least 200 and, hence, is comparable to the reduction of hundreds of times reported in (Abdollahifard, 2016). It is also worth mentioning that the evolution of methods belonging to MPS has followed the same path as in the computer vision area, where it is named as texture synthesis, such as described by the review of Mariethoz & Lefebvre (2014).…”

Section: Multiple-point Geostatisticsmentioning

confidence: 99%

“…Recently, Abdollahifard (2016) proposed the FPSIM method which explores two points: (i) a new path strategy that prioritizes data-events placed in the contour between the filled and empty regions of a realization; (ii) a search scheme that is based on the gradient vector of the central pixel of data-events. This search first compares this gradient vector with the gradient of each TI's pixel, in order to obtain a set of candidate patterns, and then performs a search in this set using the Euclidean distance.…”

Section: Multiple-point Geostatisticsmentioning

confidence: 99%

LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics

Moura

Laber

Lopes

et al. 2017

Computers & Geosciences

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Reservoir modeling is a very important task that permits the representation of a geological region of interest. Given the uncertainty involved in the process, one wants to generate a considerable number of possible scenarios so as to find those which best represent this region. Then, there is a strong demand for quickly generating each simulation. Since its inception, many methodologies have been proposed for this purpose and, in the last two decades, multiple-point geostatistics (MPS) has been the dominant one. This methodology is strongly based on the concept of training image (TI) and the use of its characteristics, which are called patterns. In this work, we propose a new MPS method that combines the application of a technique called Locality Sensitive Hashing (LSH), which permits to accelerate the search for patterns similar to a target one, with a Run-Length Encoding (RLE) compression technique that speeds up the calculation of the Hamming similarity. We have performed experiments with both categorical and continuous images which showed that LSHSIM is computationally efficient and produce good quality realizations, while achieving a reasonable space of uncertainty. In particular, for categorical data, the results suggest that LSHSIM is faster than MS-CCSIM, one of the state-of-the-art methods.

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“…As for complex geological backgrounds or elements' spatial distribution patterns, more than two statistical points should be described. The multiple-point geostatistical simulation is a target-based simulation method, which focuses on multiple-point patterns in space and absorbs the advantages of traditional two-point geostatistics to better characterize the spatial variability and delineate the uncertainty of variables [20][21][22][23][24]. Relevant simulation algorithms include pixel-based simulations, such as the extended normal equations simulation (ENESIM) algorithm [20], the single normal equation simulation (SNESIM) algorithm [25], and direct sampling (DS) algorithm [26,27]; pattern-based simulations, such as the simulation of patterns (SIMPAT) algorithm [28]; the filter-based pattern simulation (FILTERSIM) algorithm [29], and the cross-correlation-based simulation (CCSIM) algorithm [30].…”

Section: Introductionmentioning

confidence: 99%

Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

Liu

Guo

et al. 2021

Minerals

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The smoothing effect of data interpolation could cause useful information loss in geochemical mapping, and the uncertainty assessment of geochemical anomaly could help to extract reasonable anomalies. In this paper, multiple-point geostatistical simulation and local singularity analysis (LSA) are proposed to identify regional geochemical anomalies and potential mineral resources areas. Taking Cu geochemical data in the Mila Mountain Region, southern Tibet, as an example, several conclusions were obtained: (1) geochemical mapping based on the direct sampling (DS) algorithm of multiple-point geostatistics can avoid the smoothing effect through geochemical pattern simulation; (2) 200 realizations generated by the direct sampling simulation reflect the uncertainty of an unsampled value, and the geochemical anomaly of each realization can be extracted by local singularity analysis, which shows geochemical anomaly uncertainty; (3) the singularity-quantile (S-Q) analysis method was used to determine the separation thresholds of E-type α, and uncertainty analysis was carried out on the copper anomaly to obtain the anomaly probability map, which should be more reasonable than the interpolation-based geochemical map for geochemical anomaly identification. According to the anomaly probability and favorable geological conditions in the study area, several potential mineral resource targets were preliminarily delineated to provide direction for subsequent mineral exploration.

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Fast multiple-point simulation using a data-driven path and an efficient gradient-based search

Cited by 25 publications

References 27 publications

Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors

Quantitative evaluation of multiple-point simulations using image segmentation and texture descriptors

LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics

Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

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