Article
Environmental Sciences
Stylianos Hadjipetrou, Gregoire Mariethoz, Phaedon Kyriakidis
Summary: Offshore wind plays a significant role in future energy systems. SAR can provide fine-resolution wind field estimates, but suffers from limited coverage and irregular revisit times. To combine the advantages of SAR and physical model outputs, multiple-point geostatistics is used to generate synthetic fine-resolution wind speed patterns conditioned on regional reanalysis information at a coarser scale.
Article
Geosciences, Multidisciplinary
Thomas Mejer Hansen
Summary: Geostatistical models quantitatively define spatial relations between model parameters, allowing for estimation and simulation properties at unsampled locations. A novel theory is introduced in this study to compute the entropy of the underlying multivariate probability density, providing insight into the information content related to different choices of simulation algorithms and tuning parameters. The self-information and average entropy of multiple realizations are used to evaluate the efficiency and accuracy of the models.
MATHEMATICAL GEOSCIENCES
(2021)
Review
Environmental Sciences
Fatemeh Zakeri, Gregoire Mariethoz
Summary: The study provides a review of the applications of geostatistical simulation to remote sensing data, discussing different models relevant to satellite remote sensing data and their advantages. Applications of geostatistical simulation models are categorized in various domains of natural sciences, including soil, vegetation, topography, and atmospheric science.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Engineering, Environmental
Zhesi Cui, Qiyu Chen, Gang Liu, Xiaogang Ma, Xiang Que
Summary: The paper proposes a new MPS simulation method, CCPSIM algorithm, based on conditional conduction probability to mitigate the uncertainty of MPS realizations. CCPSIM is able to accurately characterize complex spatial structures of heterogeneous phenomena and reduce uncertainty in MPS realizations.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Geology
Yelena van der Grijp, Richard Minnitt, David Rose
Summary: This innovative approach utilizes a Direct Sampling multiple-point statistics algorithm to produce stochastic models of lithology and gold grade distributions in structurally complex gold deposits. The method shows potential for modeling finely intercalated folded lithologies and performs well with sparse or even absence of data, utilizing auxiliary variables. The ensemble of realizations obtained through this method can serve as a stochastic model for the poorly informed part of the deposit, incorporating modern developments in artificial intelligence and machine learning.
ORE GEOLOGY REVIEWS
(2021)
Article
Computer Science, Interdisciplinary Applications
Oli D. Johannsson, Thomas Mejer Hansen
Summary: In geostatistics, conditional information is classified as hard data (no uncertainty) or soft data (associated with uncertainty). 2-point Gaussian simulation methods can handle both types of data, while multiple-point statistical simulation methods mainly focus on conditioning to hard data. The traditional approach for soft data is to consider only co-located soft data. This study proposes a probabilistic solution that combines conditional categorical information with a categorical training image, allowing for the consideration of both co-located and non-co-located hard and soft data.
COMPUTERS & GEOSCIENCES
(2023)
Article
Astronomy & Astrophysics
Julien Straubhaar, Philippe Renard
Summary: Stochastic modeling is commonly used in environmental sciences for analyzing complex systems, with multiple-point statistics providing efficient simulation tools. The direct sampling algorithm is a flexible technique for simulating fields while considering similar pattern conditioning data.
EARTH AND SPACE SCIENCE
(2021)
Article
Engineering, Petroleum
Dailu Zhang, Hongbing Zhang, Quan Ren, Xiang Zhao
Summary: This article presents a nonstationary modeling method for simulating the distribution of sedimentary facies, which takes advantage of the multiscale spatial feature of patterns. The spatial location of the patterns is introduced as auxiliary information in the classification and simulation processes. The method incorporates multiscale results during the modeling procedure and utilizes fuzzy-rough sets for seismic attribute selection. The proposed simulation method is applicable for revealing detailed subsurface models, especially under complex geological conditions and limited information.
Article
Geochemistry & Geophysics
Jixin Huang, Chuanfeng Wang, Lixin Wang, Xun Hu, Wenjie Feng, Yanshu Yin
Summary: This paper proposes a novel method of 3D multipoint geostatistical inversion based on 2D training images directly. The 2D training image is scanned to acquire the multipoint statistical probability, which is then fused into the 3D probability. The rock facies types and patterns are obtained through random sampling. The iterative adaptive spatial sampling method is used to ensure that the error is below a given threshold.
Article
Computer Science, Interdisciplinary Applications
Oli D. Johannsson, Thomas Mejer Hansen
Summary: The paper introduces an MPS estimation algorithm to directly compute and store parameter-wise conditional statistics for potentially faster and more accurate estimation. The method is demonstrated on two types of MPS algorithms and compared with sequential simulation and estimation methods.
COMPUTERS & GEOSCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Chao Liu, Deli Wang, Han Zhang, Wei Wu, Wenzhi Sun, Ting Zhao, Nenggan Zheng
Summary: Reconstructing neuron morphologies from fluorescence microscope images is crucial for neuroscience studies. This study proposes a strategy of using two-stage generative models to simulate training data with voxel-level labels, resulting in realistic 3D images with underlying voxel labels. The results show that networks trained on synthetic data outperform those trained on manually labeled data in segmentation performance.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Interdisciplinary Applications
Leandro P. de Figueiredo, Tcharlies Schmitz, Rafael Lunelli, Mauro Roisenberg, Daniel Santana de Freitas, Dario Grana
Summary: This study introduces a new algorithm, Direct Multivariate Simulation, for simulating random fields of nonparametric multivariate joint distributions, with numerical solutions to improve algorithm efficiency. The method is demonstrated by sampling a 6-variate joint distribution with strong nonlinear dependence among the variables, and the results are validated through comparison and computation of experimental semi-variograms, among other methods.
COMPUTERS & GEOSCIENCES
(2021)
Article
Environmental Sciences
Chen Zuo, Zhuo Li, Zhe Dai, Xuan Wang, Yue Wang
Summary: This paper investigates the pattern classification distribution method in geostatistical modeling, aiming to quantitatively evaluate geological realizations. The proposed correlation-driven template method captures geological patterns, and clustering and classification characterize the realizations. A stacking framework is designed for multi-grid analysis.
Article
Geosciences, Multidisciplinary
Akshat Chandna, Sanjay Srinivasan
Summary: This paper presents a methodology for combining information from geomechanical and stochastic approaches in order to obtain a fracture modeling approach that is both geologically realistic and consistent with the geomechanical conditions for fracture propagation.
MATHEMATICAL GEOSCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Sebastien Strebelle
Summary: Multiple-point statistics simulation aims to replicate geological heterogeneity patterns in training images for reservoir studies, borrowing techniques from computer vision and machine learning. However, the goal of geological modeling is to make useful predictions and decisions, with a focus on key features and project constraints when selecting modeling techniques.
MATHEMATICAL GEOSCIENCES
(2021)