Mathematical Geosciences最新文献_第3页

Effects of Fluid Pressure Development on Hydrothermal Mineralization via Cellular Automaton Simulation 通过细胞自动机模拟研究流体压力发展对热液成矿的影响

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-04-02 DOI: 10.1007/s11004-024-10139-4

Yihui Xiong, Renguang Zuo, Oliver P. Kreuzer

{"title":"Effects of Fluid Pressure Development on Hydrothermal Mineralization via Cellular Automaton Simulation","authors":"Yihui Xiong, Renguang Zuo, Oliver P. Kreuzer","doi":"10.1007/s11004-024-10139-4","DOIUrl":"https://doi.org/10.1007/s11004-024-10139-4","url":null,"abstract":"<p>The behavior and evolution trajectory of hydrofracture, which show a close relationship with the hydrothermal mineralization process, is greatly influenced by fluid flow and fluid pressure. However, further investigation is needed to achieve an in-depth understanding of the formation and evolution mechanisms behind the link between the rate of fluid pressure development and the occurrence of induced hydrofracture and mineralization process. We considered different fluid pressure development rates as the initial data for a cellular automaton model. With the increase in the fluid pressure increase rates, the corresponding hydrofracture became more focused, changing in scale from a large number of small-scale hydrofractures to a small number of large-scale hydrofractures. Episodes of fluid pressure fluctuation induced by either low or high fluid pressure increase rates were shown to trigger mineral precipitation and further contribute to the generation of strong spatially structured and enriched geochemical patterns. Moreover, the correlation length at the percolation threshold, which is of great significance to the degree and scale of mineralization, increased with the increasing fluid pressure increase rates. It was concluded that computational grids with high fluid pressure increase rates are much more prone to produce enriched geochemical patterns with strong spatial structures than grids with low fluid pressure increase rates owing to a larger correlation length at the percolation threshold. These results suggest that the way of fluid pressure development is a key factor for quantifying the behavior of hydrofracture and mineralization process.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140588511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation Enhancement GAN for Efficient Reservoir Simulation at Fine Scales 仿真增强型 GAN 用于精细尺度的高效储层仿真

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-03-25 DOI: 10.1007/s11004-024-10136-7

{"title":"Simulation Enhancement GAN for Efficient Reservoir Simulation at Fine Scales","authors":"","doi":"10.1007/s11004-024-10136-7","DOIUrl":"https://doi.org/10.1007/s11004-024-10136-7","url":null,"abstract":"<h3>Abstract</h3> <p>In this paper, an innovative approach for enhancing fluid transport modeling in porous media is presented, which finds application in various fields, including subsurface reservoir modeling. Fluid flow models are typically solved numerically by addressing a system of partial differential equations (PDEs) using methods such as finite difference and finite volume. However, these processes can be computationally demanding, particularly when aiming for high precision on a fine scale. Researchers have increasingly turned to machine learning to explore solutions for PDEs in order to improve simulation efficiency. The proposed method combines an adaptive multi-scale strategy with generative adversarial networks (GAN) to increase simulation efficiency on a fine scale. The devised model, called simulation enhancement GAN (SE-GAN), takes coarse-scale simulation results as input and generates fine-scale results in conjunction with the provided petrophysical properties. With this new approach, a deep learning model is trained to map coarse-scale results to fine-scale outcomes, rather than directly solving the fluid flow model. Case studies reveal that SE-GAN can achieve a significant improvement in accuracy while reducing computational time compared to the original fine-scale simulation solver. A comprehensive evaluation of numerical experiments is conducted to elucidate the benefits and limitations of this method. The potential of SE-GAN in accelerating the numerical solver for reservoir simulations is also demonstrated.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140298068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Branch Convolutional Neural Network and Its Post Hoc Interpretability for Mapping Mineral Prospectivity 双分支卷积神经网络及其用于绘制矿产远景图的事后解释能力

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-03-22 DOI: 10.1007/s11004-024-10137-6

Fanfan Yang, Renguang Zuo, Yihui Xiong, Ying Xu, Jiaxin Nie, Gubin Zhang

{"title":"Dual-Branch Convolutional Neural Network and Its Post Hoc Interpretability for Mapping Mineral Prospectivity","authors":"Fanfan Yang, Renguang Zuo, Yihui Xiong, Ying Xu, Jiaxin Nie, Gubin Zhang","doi":"10.1007/s11004-024-10137-6","DOIUrl":"https://doi.org/10.1007/s11004-024-10137-6","url":null,"abstract":"<p>The purpose of mineral prospectivity mapping (MPM) is to discover unknown mineral deposits by means of fusing multisource prospecting information. In recent years, with rapid advancements in artificial intelligence, deep learning algorithms (DLAs) as a groundbreaking technique have exhibited outstanding capabilities in geoscience. However, conventional DLAs for MPM face certain challenges in feature extraction and the fusion of multimodal prospecting data. Moreover, opaque DLAs lead to an insufficient understanding of the predictive results by experts. In this study, a dual-branch convolutional neural network (DBCNN) and its post hoc interpretability were jointly constructed to map gold prospectivity in western Henan Province of China. In particular, channel and spatial attention modules were integrated into two branches to complement the respective advantages of multichannel and high spatial prospecting data for MPM. The Shapley additive explanations (SHAP) framework was then adopted to explain the predictive results by exploring the feature contributions. The comparative experiments illustrated that DBCNN can enhance feature representation and fusion abilities to improve the performance of MPM compared to conventional DLAs. The high-probability areas delineated by the DBCNN model exhibited close spatial relevance with known gold deposits, and the SHAP further confirmed the reliability of the predictive result obtained by the DBCNN model, thereby guiding future gold exploration in this study area.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scale-Independent Variation Rates of Phanerozoic Environmental Variables and Implications for Earth’s Sustainability and Habitability 新生代环境变量的规模独立变化率及其对地球可持续性和宜居性的影响

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-03-21 DOI: 10.1007/s11004-024-10135-8

Haitao Shang

引用次数: 0

KNN-GCN: A Deep Learning Approach for Slope-Unit-Based Landslide Susceptibility Mapping Incorporating Spatial Correlations KNN-GCN：一种基于斜坡单元的包含空间相关性的滑坡易感性绘图深度学习方法

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-02-06 DOI: 10.1007/s11004-023-10132-3

Ding Xia, Huiming Tang, Thomas Glade, Chunyan Tang, Qianyun Wang

{"title":"KNN-GCN: A Deep Learning Approach for Slope-Unit-Based Landslide Susceptibility Mapping Incorporating Spatial Correlations","authors":"Ding Xia, Huiming Tang, Thomas Glade, Chunyan Tang, Qianyun Wang","doi":"10.1007/s11004-023-10132-3","DOIUrl":"https://doi.org/10.1007/s11004-023-10132-3","url":null,"abstract":"<p>Landslides pose a significant risk to human life and property, making landslide susceptibility mapping (LSM) a crucial component of landslide risk assessment. However, spatial correlations among mapping units are often neglected in statistical or machine learning models proposed for LSM. This study proposes KNN-GCN, a deep learning model for slope-unit-based LSM based on a graph convolutional network (GCN) and the <i>K</i>-nearest neighbor (KNN) algorithm. The model was experimentally applied to the Lueyang region and validated through the following steps. Firstly, we collected data for 15 landslide causal factors and from landslide inventories and established a slope unit map (SUM) through slope unit division. Next, we performed a multicollinearity analysis of landslide causal factors and divided the training and test sets at a 7:3 ratio. We then constructed a GCN model based on a slope unit graph (SUG) generated from the SUM using the KNN algorithm. The proposed KNN-GCN model was tuned using a grid search with fivefold cross-validation on the training set, and then trained and validated on training and test sets separately. Finally, the performance of the KNN-GCN model was compared with that of six other models which were categorized into two groups: CG#1 was the traditional KNN, support vector regression (SVC), and automated machine learning (AutoML), and CG#2 included KNN-G, SVC-G and AutoML-G with additional spatial information. Our results demonstrate that the proposed model achieves superior performance (area under the curve [AUC] = 0.8351) and generates the most comprehensible susceptibility map with distinct boundaries between different susceptibility levels. Notably, while the proposed KNN-GCN model displays exceptional performance in slope-unit-based LSM, its implementation requires high-level computing resources, and it is not recommended for small datasets.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Extreme Precipitation Data in a Mining Area 矿区极端降水数据建模

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-01-31 DOI: 10.1007/s11004-023-10126-1

Ourania-Anna Lymperi, Emmanouil A. Varouchakis

{"title":"Modeling Extreme Precipitation Data in a Mining Area","authors":"Ourania-Anna Lymperi, Emmanouil A. Varouchakis","doi":"10.1007/s11004-023-10126-1","DOIUrl":"https://doi.org/10.1007/s11004-023-10126-1","url":null,"abstract":"<p>In recent decades, extreme precipitation events have increased in frequency and intensity in Greece and across regions of the Mediterranean, with significant environmental and socioeconomic impacts. Therefore, extensive statistical analysis of the extreme rainfall characteristics on a dense temporal scale is crucial for areas with important economic activity. For this reason, this paper uses the daily precipitation measurements of four meteorological stations in a mining area of northeastern Chalkidiki peninsula from 2006 to 2021. Three statistical approaches were carried out to develop the best-fitting probability distribution for annual extreme precipitation conditions, using the maximum likelihood method for parameter estimation: the block maxima of the generalized extreme value (GEV) distribution and the peak over threshold of the generalized Pareto distribution (GPD) based on extreme value theory (EVT), and the gamma distribution. Based upon this fitting distribution procedure, return periods for the extreme precipitation values were calculated. Results indicate that EVT distributions satisfactorily fit extreme precipitation, with GPD being the most appropriate, and lead to similar conclusions regarding extreme events.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139647853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability 将地质知识融入深度学习，提高与矿化和可解释性相关的地球化学异常识别能力

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-01-20 DOI: 10.1007/s11004-023-10133-2

{"title":"Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability","authors":"","doi":"10.1007/s11004-023-10133-2","DOIUrl":"https://doi.org/10.1007/s11004-023-10133-2","url":null,"abstract":"<h3>Abstract</h3> <p>Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical survey data. Yet purely data-driven DL algorithms often lack logical explanations and geological consistency, occasionally clashing with known geological insights and complicating model interpretation. A deep understanding of the geological processes forming the target mineral deposit is vital for accurate anomaly detection. Here, we introduce an adversarial autoencoder (AAE) network that integrates prior geological knowledge to identify geochemical anomalies linked to tungsten mineralization in southern Jiangxi Province, China. Considering the geochemical patterns linked to tungsten mineralization, Yanshanian granites and faults were strategically chosen as ore-controlling factors. The methodology employed multifractal singularity analysis to quantitatively measure the correlations between these ore-controlling factors and known tungsten deposits, aiming to establish an ore-forming regularity. This regularity serves as a priori distribution to control the encoder network's latent vector, refining the model's output. A comparison of detected geochemical anomalies under different constraints (AAE, Granite_AAE, Fault_AAE, and Fault_Granite_AAE) revealed that AAE models incorporating prior geological information consistently outperformed unconstrained models in terms of anomaly detection. Integrating geological expertise with DL, our study overcomes the challenges of models relying purely on data or theory, offering a promising approach to geochemical exploration.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Parametric Classes of Isotropic Covariance Functions for Spatial Random Fields 空间随机域各向同性协方差函数的混合参数类

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-01-16 DOI: 10.1007/s11004-023-10123-4

Alfredo Alegría, Fabián Ramírez, Emilio Porcu

{"title":"Hybrid Parametric Classes of Isotropic Covariance Functions for Spatial Random Fields","authors":"Alfredo Alegría, Fabián Ramírez, Emilio Porcu","doi":"10.1007/s11004-023-10123-4","DOIUrl":"https://doi.org/10.1007/s11004-023-10123-4","url":null,"abstract":"<p>Covariance functions are the core of spatial statistics, stochastic processes, machine learning, and many other theoretical and applied disciplines. The properties of the covariance function at small and large distances determine the geometric attributes of the associated Gaussian random field. Covariance functions that allow one to specify both local and global properties are certainly in demand. This paper provides a method for finding new classes of covariance functions having such properties. We refer to these models as hybrid, as they are obtained as scale mixtures of piecewise covariance kernels against measures that are also defined as piecewise linear combinations of parametric families of measures. To illustrate our methodology, we provide new families of covariance functions that are proved to be richer than other well-known families proposed in earlier literature. More precisely, we derive a hybrid Cauchy–Matérn model, which allows us to index both long memory and mean square differentiability of the random field, and a hybrid hole-effect–Matérn model which is capable of attaining negative values (hole effect) while preserving the local attributes of the traditional Matérn model. Our findings are illustrated through numerical studies with both simulated and real data.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revisiting the Geochemical Classification of Zircon Source Rocks Using a Machine Learning Approach 利用机器学习方法重新审视锆石源岩的地球化学分类

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-01-16 DOI: 10.1007/s11004-023-10128-z

Keita Itano, Hikaru Sawada

{"title":"Revisiting the Geochemical Classification of Zircon Source Rocks Using a Machine Learning Approach","authors":"Keita Itano, Hikaru Sawada","doi":"10.1007/s11004-023-10128-z","DOIUrl":"https://doi.org/10.1007/s11004-023-10128-z","url":null,"abstract":"<p>Trace element fingerprints preserved in zircons offer clues to their origin and crystallization conditions. Numerous geochemical indicators have been established to evaluate the source rock characteristics from a geochemical perspective; however, multivariate trace element data have not been sufficiently investigated statistically. As substantial amounts of zircon data from a wide range of rock types have become accessible over the past few decades, it is now essential to reassess the utility of trace elements in discriminating source rock types. We employed a new zircon trace element dataset and established classification models to distinguish eight types of source rocks: igneous (acidic, intermediate, basic, kimberlite, carbonatite, and nepheline syenite), metamorphic, and hydrothermal. Whereas a conventional decision tree analysis was unable to correctly classify the new dataset, the random forest and support vector machine algorithms achieved high-precision classifications (> 80% precision, recall, and F1 score). This work confirms that trace element composition is a helpful tool for province studies and mineral exploration using detrital zircons. However, the compiled dataset with many missing values leaves room for improving the models. Trace elements, such as P and Sc, which cannot be measured by quadrupole inductively coupled plasma mass spectrometry, are vital for more accurate classification.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of Reservoir Fracture Properties from Seismic Data Using Markov Chain Monte Carlo Methods 利用马尔可夫链蒙特卡洛方法从地震数据估算储层裂缝属性

IF 2.6 3区地球科学

Mathematical Geosciences Pub Date : 2024-01-10 DOI: 10.1007/s11004-023-10129-y

{"title":"Estimation of Reservoir Fracture Properties from Seismic Data Using Markov Chain Monte Carlo Methods","authors":"","doi":"10.1007/s11004-023-10129-y","DOIUrl":"https://doi.org/10.1007/s11004-023-10129-y","url":null,"abstract":"<h3>Abstract</h3> <p>The knowledge of fracture properties and its geometrical patterns is often required for the analysis of mechanical and flow properties in fractured reservoirs, as fracture characterization plays a critical role in the optimization of hydrocarbon production or estimation of storage capacity of subsurface reservoirs. A stochastic method based on a Markov chain Monte Carlo (MCMC) algorithm is proposed to estimate fracture properties using a rock physics model for fractured rocks. Two implementations are presented: a Metropolis algorithm based on a Gaussian prior distribution and an extended Metropolis algorithm with an informative prior obtained from multiple-point statistics simulations. The results are compared to a Bayesian analytical approach where the solution is based on a linearized approximation of the rock physics model. The novelty of the proposed approach is the use of a training image, that is, a conceptual geological model, to account for the spatial distribution of the fractures. Two fracture properties are considered, namely fracture density and aspect ratio, and the spatial distribution and geometrical characteristics of fractures are also investigated to understand the connectivity patterns that control fluid flow. The MCMC approach with a training image is more computationally demanding but provides geometrical models of the spatial distribution of fractures. The inversion results show that the prediction accuracy of fracture density and aspect ratio obtained by the MCMC methods is similar to the one obtained with the analytical approach, and that the MCMC methods provide a reliable assessment of the posterior uncertainty as well.</p>","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139411468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0