Article
Engineering, Environmental
Stephanie Thiesen, Uwe Ehret
Summary: In this study, the HER method is used to estimate soil contamination risk, compared with IK and OK models, the results show that in the analyzed dataset, IK and HER predictions perform the best and exhibit comparable accuracy and precision. Compared to IK, the advantages of HER in uncertainty estimation at a fine resolution are that it does not require modeling of multiple indicator variograms, correcting order-relation violations, or defining interpolation/extrapolation of distributions.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Engineering, Environmental
Francky Fouedjio, Celine Scheidt, Liang Yang, Yizheng Wang, Jef Caers
Summary: This paper introduces a method for generating conditional categorical simulations from a set of unconditional categorical simulations, relying on signed distance functions and combining aspects of principal component analysis and Gibbs sampling.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Geosciences, Multidisciplinary
Rodrigo Cesar Vasconcelos dos Santos, Marcelle Martins Vargas, Luis Carlos Timm, Samuel Beskow, Tirzah Moreira Siqueira, Carlos Rogerio Mello, Mauricio Fornalski Soares, Maira Martim de Moura, Klaus Reichardt
Summary: This study aims to assess the influence of spatial variabilities of K-sat and Theta on direct surface runoff (DSR) hydrographs using the Limburg Soil Erosion Model (LISEM) in conjunction with measured K-sat data. The effects of K-sat spatial variability and its uncertainties on DSR hydrographs were evaluated using Sequential Gaussian Simulation. The simulated DSR hydrographs and peak discharges were adequately represented by LISEM.
Article
Environmental Sciences
Andras Bardossy, Sebastian Horning
Summary: The spatial structures of natural variables are often complex and exhibit non-Gaussian spatial dependence. Existing approaches to consider non-Gaussian behavior are limited. This study presents a flexible method for defining non-Gaussian spatial dependence, based on continuous deformation of fields with different Gaussian spatial dependence. The methodology is illustrated with theoretical examples and demonstrated in a real-life example of groundwater quality parameters.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Chenlu Ke, Wei Yang, Qingcong Yuan, Lu Li
Summary: Variable screening is an important tool for dimension reduction in ultrahigh dimensional data analysis. This study proposes a partial sufficient variable screening method for the presence of control variables, which aims to reduce the predictive set without losing regression information. The method achieves variable screening by constraining the reduction of continuous variables using the subpopulations identified by categorical variables. The effectiveness of the method is demonstrated through simulation studies and an application in gene screening for diffuse large-B-cell lymphoma prognosis.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
Article
Computer Science, Interdisciplinary Applications
Mostafa Hadavand, Clayton V. Deutsch
Summary: In this paper, deep learning is used to quantify moments of the conditional distribution of a missing variable, followed by the use of the lambda distribution to parametrize the conditional distribution. Geo-statistical quantification and Bayesian updating are used for data imputation, which is important for handling incomplete geological data.
COMPUTERS & GEOSCIENCES
(2023)
Article
Environmental Sciences
Gabor Szatmari, Mihaly Kocsis, Andras Mako, Laszlo Pasztor, Zsofia Bakacsi
Summary: Eutrophication, water quality, and environmental status of lakes are global issues affected by both external and internal loadings. Applying multivariate geostatistics in water ecosystems can provide coherent and accurate spatial models, taking into account the interdependence among variables and generating predictions at different scales.
Article
Engineering, Multidisciplinary
Jia-Yi Ou-Yang, Dian-Qing Li, Xiao-Song Tang, Yong Liu
Summary: The study utilizes random field theory to characterize spatial variability of material properties and develops a patching algorithm to incorporate sampled data into simulations, which outperforms the conventional Kriging algorithm. The proposed algorithm restricts the influence domain of sampled data within a reasonable range determined by the scale of fluctuation, resulting in a stationary conditional random field in mean and variance suitable for situations with limited known data. Additionally, the algorithm is effective in reducing uncertainty in response prediction and applicable with sparse sampling pattern as demonstrated in a tunnel excavation model.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Oktay Erten, Clayton V. Deutsch
Summary: The paper addresses the issue of joint modeling of petrophysical and seismic properties through a hierarchical simulation framework, preserving correlation structures and enabling independent simulation.
By utilizing the projection-pursuit multivariate transform, each uncorrelated factor can be simulated independently, with super-secondary variables generated from previously simulated factors and secondary variables for co-simulation.
NATURAL RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Rodrigo Cesar Vasconcelos dos Santos, Mauricio Fornalski Soares, Luis Carlos Timm, Tirzah Moreira Siqueira, Carlos Rogerio Mello, Samuel Beskow, Douglas Rodrigo Kaiser
Summary: This study utilized sequential Gaussian simulation to simulate the spatial variability of saturated soil hydraulic conductivity (K-sat) in a subtropical watershed in Southern Brazil. The results showed that lower K-sat uncertainty estimates were found in densely sampled areas, while higher uncertainty estimates were obtained in soils located at steeper areas of the watershed and alongside the main watercourse.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Environmental Sciences
Marcos Vinicius da Silva, Heliton Pandorfi, Alexandre Manitoba da Rosa Ferraz Jardim, Jose Francisco de Oliveira-Junior, Jesiele Silva da Divincula, Pedro Rogerio Giongo, Thieres George Freire da Silva, Gledson Luiz Pontes de Almeida, Geber Barbosa de Albuquerque Moura, Pabricio Marcos Oliveira Lopes
Summary: This study focused on characterizing the patterns of monthly rainfall in the mesoregions of Zona da Mata and Metropolitan of Recife in the state of Pernambuco, Brazil. Geostatistical techniques and multivariate analysis were used to analyze meteorological data from a 20-year time series, revealing the influence of oceanic and topographical factors on rainfall dynamics.
Article
Computer Science, Interdisciplinary Applications
J. Padarian, A. B. McBratney
Summary: Uncertainty assessment is crucial for spatial modelling, not only analytically but also as a means of communication. However, end users often struggle to understand uncertainty maps alongside prediction maps. This study proposes an approach to integrate prediction and uncertainty into a single, variable resolution digital map, where uncertainty is encoded as pixel size. The quadtree algorithm is used to recursively partition the map, aggregating pixels with high uncertainty. The resulting maps allow users to easily identify areas with high uncertainty.
COMPUTERS & GEOSCIENCES
(2023)
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
Soil Science
Jianhua Jin, Baozhong Zhang, Xiaomin Mao
Summary: The study introduced a stratified soil moisture sampling method based on the spatial autocorrelation of auxiliary variables (SSAV) which showed significantly lower mean relative error and standard deviation of soil moisture compared to the SRS and STRS methods. The root mean squared error between observed and estimated soil moisture with the SSAV method was also lower, demonstrating higher accuracy and precision in monitoring. Overall, the SSAV method is recommended for placing soil moisture sampling points to estimate mean soil moisture.
SOIL & TILLAGE RESEARCH
(2022)
Article
Engineering, Marine
Emmanouil A. Varouchakis
Summary: This technical note applied a geostatistical model to investigate the spatial distribution of source rock data based on total organic carbon weight concentration. The median polish kriging method was used to address row and column effects, with ordinary kriging methodology applied to residuals. The study also utilized sequential Gaussian simulation to quantify uncertainty and recommended the use of median polish kriging method for similar applications.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Geosciences, Multidisciplinary
Liang Yang, Peter Achtziger-Zupancic, Jef Caers
Summary: Implicit methods for modeling geological structures, such as the implicit potential field method, have been developed for over a decade, allowing for automatic model construction by incorporating various types of data. However, applying this method to large-scale 3D modeling of geological structures remains challenging due to complex nature and difficulty in estimating an adequate variogram model. A divide-and-conquer strategy is proposed to address this challenge, creating intermediate 3D geological models matching subsets of data and then recombining them while preserving data and geological rule constraints.
NATURAL RESOURCES RESEARCH
(2021)
Article
Geosciences, Multidisciplinary
Noah Athens, Jef Caers
Summary: A stochastic framework is proposed in this paper to incorporate fault-related and density-related uncertainty into the inversion process, using Monte Carlo simulation and gradual deformation method. The approach aims to assess structural uncertainty through generating model realizations and refining them to match observed data.
MATHEMATICAL GEOSCIENCES
(2022)
Article
Environmental Sciences
Lijing Wang, Peter K. Kitanidis, Jef Caers
Summary: Bayesian inversion is commonly used to quantify uncertainty of hydrological variables. This paper proposes a hierarchical Bayesian framework to quantify uncertainty of both global and spatial variables. The authors present a machine learning-based inversion method and a local dimension reduction method to efficiently estimate posterior probabilities and update spatial fields. Using three case studies, they demonstrate the importance of quantifying uncertainty of global variables for predictions and the acceleration effect of the local PCA approach.
WATER RESOURCES RESEARCH
(2022)
Article
Water Resources
Yizheng Wang, Markus Zechner, John Michael Mern, Mykel J. Kochenderfer, Jef Karel Caers
Summary: This paper proposes a method of planning groundwater remediation as a POMDP, which can derive better remediation strategies by optimizing the trade-off between information gathering and future scenario performance. Empirical results show that DESPOT outperforms handcrafted heuristics and optimization methods by incorporating both previous information and future reward.
ADVANCES IN WATER RESOURCES
(2022)
Editorial Material
Geosciences, Multidisciplinary
Jef Caers
Summary: Jef Caers argues that research on the energy transition should involve all communities and break the pattern of traditional industry-funded research, based on his personal experience.
Article
Computer Science, Interdisciplinary Applications
Lijing Wang, Luk Peeters, Emma J. MacKie, Zhen Yin, Jef Caers
Summary: Modeling complex geological interfaces is crucial in geosciences, and various data sources can be used for this purpose. This study presents a data-knowledge-driven trend surface analysis method to construct stochastic geological interfaces. By integrating different information sources, a Metropolis-Hastings sampling framework is used to quantify the uncertainty of the geological interfaces. The method is demonstrated in three different test cases: Greenland subglacial topography, magmatic intrusion, and buried river valleys in Australia.
COMPUTERS & GEOSCIENCES
(2023)
Article
Geosciences, Multidisciplinary
Lijing Wang, Hyojin Kim, Birgitte Hansen, Anders V. Christiansen, Troels N. Vilhelmsen, Jef Caers
HYDROGEOLOGY JOURNAL
(2023)
Article
Geochemistry & Geophysics
G. Volpe, G. Pozzi, C. Collettini, E. Spagnuolo, P. Achtziger-Zupanc, A. Zappone, L. Aldega, M. A. Meier, D. Giardini, M. Cocco
Summary: Fluid induced fault reactivation experiments were conducted at BedrettoLab to characterize frictional properties and permeability of a selected fault zone. Field investigation and X-ray powder diffraction analysis were used to characterize fault zone microstructures and fault gouge mineralogy. Frictional and permeability characterization were performed using BRAVA in the laboratory, and the experimental results were integrated with field investigations to identify the seismogenic potential and hydraulic stimulation feasibility of the selected fault.
Article
Geosciences, Multidisciplinary
John Mern, Jef Caers
Summary: Geoscientific models rely on geoscientific data, so improving models often requires acquiring additional data. The value of information and Bayesian optimal survey design can guide the acquisition of additional data, but they usually focus on evaluating one campaign at a time. In real settings, especially in Earth resource exploration, planning a large sequence of data acquisition campaigns is necessary. This study formulates the problem as a partially observable Markov decision process and presents methodologies to solve it using Monte Carlo planning methods, demonstrating its effectiveness in reducing uncertainty.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Article
Engineering, Multidisciplinary
Junling Fang, Bin Gong, Jef Caers
Summary: This paper discusses the application of Bayesian theory in fractured reservoirs and identifies the problems with the Bayesian prior. The authors use global sensitivity analysis and approximate Bayesian computation methods to address this issue and successfully reduce the uncertainty of key parameters.
Article
Geochemistry & Geophysics
Zhen Yin, Maisha Amaru, Yizheng Wang, Lewis Li, Jef Caers
Summary: By using a data-driven Bayesian approach, high-resolution lithological models can be built and the uncertainties of soft data can be quantified. This approach avoids underestimating reservoir model uncertainty and the generated lithofacies models are less likely to be falsified by observed data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Zhen Yin, Chen Zuo, Emma J. MacKie, Jef Caers
Summary: In this study, researchers 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 the Thwaites Glacier in the Amundsen Sea Embayment. By collecting high-quality topographic training images and using a Bayesian framework, they improve the quality and efficiency of the topographic modelling. They also simulate multiple realizations of high-resolution topographic maps to investigate the impact of basal topography uncertainty on subglacial hydrological flow patterns.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Engineering, Environmental
Francky Fouedjio, Celine Scheidt, Liang Yang, Yizheng Wang, Jef Caers
Summary: This paper introduces a method for generating conditional categorical simulations from a set of unconditional categorical simulations, relying on signed distance functions and combining aspects of principal component analysis and Gibbs sampling.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Yapo Abole Serge Innocent Oboue, Yunfeng Chen, Sergey Fomel, Wei Zhong, Yangkang Chen
Summary: Strong noise can disrupt the recorded seismic waves and negatively impact subsequent seismological processes. To improve the signal-to-noise ratio (S/N) of seismological data, we introduce MATamf, an open-source MATLAB code package based on an advanced median filter (AMF) that simultaneously attenuates various types of noise and improves S/N. Experimental results demonstrate the usefulness and advantages of the proposed AMF workflow in enhancing the S/N of a wide range of seismological applications.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Upkar Singh, P. N. Vinayachandran, Vijay Natarajan
Summary: The Bay of Bengal maintains its salinity distribution due to the cyclic flow of high salinity water and the mixing with freshwater. This paper introduces an advection-based feature definition and algorithms to track the movement of high salinity water, validated through comparison with observed data.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Bijal Chudasama, Nikolas Ovaskainen, Jonne Tamminen, Nicklas Nordback, Jon Engstro, Ismo Aaltonen
Summary: This contribution presents a novel U-Net convolutional neural network (CNN)-based workflow for automated mapping of bedrock fracture traces from aerial photographs acquired by unmanned aerial vehicles (UAV). The workflow includes training a U-Net CNN using a small subset of photographs with manually traced fractures, semantic segmentation of input images, pixel-wise identification of fracture traces, ridge detection algorithm and vectorization. The results show the effectiveness and accuracy of the workflow in automated mapping of bedrock fracture traces.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Ruizhen Wang, Siyang Wan, Weitao Chen, Xuwen Qin, Guo Zhang, Lizhe Wang
Summary: This paper proposes a novel framework to generate a finer soil strength map based on RCI, which uses ensemble learning models to obtain USCS soil classification and predict soil moisture, in order to improve the resolution and reliability of existing soil strength maps.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhanlong Chen, Xiaochuan Ma, Houpu Li, Xuwei Xu, Xiaoyi Han
Summary: Simulated terrains are important for landform and terrain research, disaster prediction, rescue and disaster relief, and national security. This study proposes a deep learning method, IGPN, that integrates global information and pattern features of the local terrain to generate accurate simulated terrains quickly.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniele Secci, Vanessa A. Godoy, J. Jaime Gomez-Hernandez
Summary: Neural networks excel in various machine learning applications, but lack physical interpretability and constraints, limiting their accuracy and reliability in predicting complex physical systems' behavior. Physics-Informed Neural Networks (PINNs) integrate neural networks with physical laws, providing an effective tool for solving physical problems. This article explores recent developments in PINNs, emphasizing their application in solving unconfined groundwater flow, and discusses challenges and opportunities in this field.
COMPUTERS & GEOSCIENCES
(2024)
Article
Computer Science, Interdisciplinary Applications
Renguang Zuo, Ying Xu
Summary: This study proposes a hybrid deep learning model consisting of a one-dimensional convolutional neural network (1DCNN) and a graph convolutional network (GCN) to extract joint spectrum-spatial features from geochemical survey data for mineral exploration. The physically constrained hybrid model performs better in geochemical anomaly recognition compared to other models.
COMPUTERS & GEOSCIENCES
(2024)