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Monitoring Mining Activity and Vegetation Recovery in Rare Earth Element Mining Areas 稀土元素矿区采矿活动与植被恢复监测
3区 地球科学
Mathematical Geosciences Pub Date : 2023-11-13 DOI: 10.1007/s11004-023-10113-6
Yan Liu, Renguang Zuo
{"title":"Monitoring Mining Activity and Vegetation Recovery in Rare Earth Element Mining Areas","authors":"Yan Liu, Renguang Zuo","doi":"10.1007/s11004-023-10113-6","DOIUrl":"https://doi.org/10.1007/s11004-023-10113-6","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"38 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136283452","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
Reconstruction of GPS Coordinate Time Series Based on Low-Rank Hankel Matrix Recovery 基于低秩Hankel矩阵恢复的GPS坐标时间序列重建
3区 地球科学
Mathematical Geosciences Pub Date : 2023-11-13 DOI: 10.1007/s11004-023-10117-2
Jianhuan Gong, Gang Chen, Jiawen Bian, Zhuofan Wang
{"title":"Reconstruction of GPS Coordinate Time Series Based on Low-Rank Hankel Matrix Recovery","authors":"Jianhuan Gong, Gang Chen, Jiawen Bian, Zhuofan Wang","doi":"10.1007/s11004-023-10117-2","DOIUrl":"https://doi.org/10.1007/s11004-023-10117-2","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"63 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282012","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
Teaching Numerical Groundwater Flow Modeling with Spreadsheets: Unconfined Aquifers and Multilayered Vertical Cross-Sections 用电子表格教学数值地下水流动模拟:无承压含水层和多层垂直截面
3区 地球科学
Mathematical Geosciences Pub Date : 2023-11-09 DOI: 10.1007/s11004-023-10112-7
J. Jaime Gómez-Hernández, Daniele Secci
{"title":"Teaching Numerical Groundwater Flow Modeling with Spreadsheets: Unconfined Aquifers and Multilayered Vertical Cross-Sections","authors":"J. Jaime Gómez-Hernández, Daniele Secci","doi":"10.1007/s11004-023-10112-7","DOIUrl":"https://doi.org/10.1007/s11004-023-10112-7","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":" 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135240722","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
Pore Pressure Uncertainty Characterization Coupling Machine Learning and Geostatistical Modelling 孔隙压力不确定性表征耦合机器学习和地质统计建模
3区 地球科学
Mathematical Geosciences Pub Date : 2023-11-06 DOI: 10.1007/s11004-023-10102-9
Amílcar Soares, Rúben Nunes, Paulo Salvadoretti, João Felipe Costa, Teresa Martins, Mario Santos, Leonardo Azevedo
{"title":"Pore Pressure Uncertainty Characterization Coupling Machine Learning and Geostatistical Modelling","authors":"Amílcar Soares, Rúben Nunes, Paulo Salvadoretti, João Felipe Costa, Teresa Martins, Mario Santos, Leonardo Azevedo","doi":"10.1007/s11004-023-10102-9","DOIUrl":"https://doi.org/10.1007/s11004-023-10102-9","url":null,"abstract":"Abstract Pore pressure prediction is fundamental when drilling deep and geologically complex reservoirs. Even in relatively well-characterized hydrocarbon reservoir fields, with a considerable number of drilled wells, when located in challenging geological environments, poor prediction of abnormal pore pressure might result in catastrophic events that can cause harm to human lives and infrastructures. To better quantify drilling risks, the uncertainty associated with the pore pressure prediction should be integrated within the geo-modelling workflow. Leveraging a challenging real case from the Brazilian pre-salt, the work presented herein proposes a seismic-driven gradient pore pressure modelling workflow, which combines machine learning and geostatistical co-simulation to predict high-resolution gradient pore pressure volumes. First, existing angle-dependent seismic reflection data are inverted for P- and S-wave velocity and density. Then, K-nearest neighbor is used to create a regression model between pore pressure gradient and P- and S-wave velocity, density and depth based on the well log information. The trained model is applied to predict a three-dimensional gradient pore pressure model from the models obtained from geostatistical seismic inversion. This gradient pore pressure model is a smooth representation of the highly variable subsurface and is used as secondary variable in stochastic sequential co-simulation with joint probability distributions to generate multiple high-resolution realizations of gradient pore pressure. The ensemble of co-simulated models can be used to assess the spatial uncertainty about the gradient pore pressure predictions. The results of the application example show the ability of the method to reproduce the spatial patterns observed in the seismic data and to reproduce existing gradient pore pressure well logs at two blind well locations, which were not used to condition the gradient pore pressure predictions.","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"70 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679751","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
Extended Multiple Interacting Continua (E-MINC) Model Improvement with a K-Means Clustering Algorithm Based on an Equi-dimensional Discrete Fracture Matrix (ED-DFM) Model 基于等维离散断裂矩阵(ED-DFM)模型的扩展多重相互作用连续体(E-MINC)模型k均值聚类改进
3区 地球科学
Mathematical Geosciences Pub Date : 2023-11-06 DOI: 10.1007/s11004-023-10110-9
Mehmet Onur Dogan
{"title":"Extended Multiple Interacting Continua (E-MINC) Model Improvement with a K-Means Clustering Algorithm Based on an Equi-dimensional Discrete Fracture Matrix (ED-DFM) Model","authors":"Mehmet Onur Dogan","doi":"10.1007/s11004-023-10110-9","DOIUrl":"https://doi.org/10.1007/s11004-023-10110-9","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"217 2‐3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679940","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
A Dynamic Extreme Value Model with Application to Volcanic Eruption Forecasting 动态极值模型在火山喷发预测中的应用
3区 地球科学
Mathematical Geosciences Pub Date : 2023-10-30 DOI: 10.1007/s11004-023-10109-2
Michele Nguyen, Almut E. D. Veraart, Benoit Taisne, Chiou Ting Tan, David Lallemant
{"title":"A Dynamic Extreme Value Model with Application to Volcanic Eruption Forecasting","authors":"Michele Nguyen, Almut E. D. Veraart, Benoit Taisne, Chiou Ting Tan, David Lallemant","doi":"10.1007/s11004-023-10109-2","DOIUrl":"https://doi.org/10.1007/s11004-023-10109-2","url":null,"abstract":"Abstract Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundations to accurately characterise extremes. This motivates the development of extreme value models for extreme event forecasting. In this paper, a dynamic extreme value model is proposed for forecasting volcanic eruptions. This is inspired by one recently introduced for financial risk forecasting with high-frequency data. Using a case study of the Piton de la Fournaise volcano, it is shown that the modelling framework is widely applicable, flexible and holds strong promise for natural hazard forecasting. The value of using EVT-informed thresholds to identify and model extreme events is shown through forecast performance, and considerations to account for the range of observed events are discussed.","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022623","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
Lithology Identification of UAV Oblique Photography Images Based on Semantic Segmentation Neural Network Algorithm 基于语义分割神经网络算法的无人机斜摄影图像岩性识别
3区 地球科学
Mathematical Geosciences Pub Date : 2023-10-16 DOI: 10.1007/s11004-023-10108-3
Siyu Luo, Senlin Yin, Juan Chen, Youxin Wu, Xu Chen
{"title":"Lithology Identification of UAV Oblique Photography Images Based on Semantic Segmentation Neural Network Algorithm","authors":"Siyu Luo, Senlin Yin, Juan Chen, Youxin Wu, Xu Chen","doi":"10.1007/s11004-023-10108-3","DOIUrl":"https://doi.org/10.1007/s11004-023-10108-3","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112616","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
Space–Time Distribution of Trichloroethylene Groundwater Concentrations: Geostatistical Modeling and Visualization 三氯乙烯地下水浓度时空分布:地质统计建模与可视化
3区 地球科学
Mathematical Geosciences Pub Date : 2023-10-14 DOI: 10.1007/s11004-023-10107-4
Pierre Goovaerts, Alexa Rihana-Abdallah, Yuncong Pang
{"title":"Space–Time Distribution of Trichloroethylene Groundwater Concentrations: Geostatistical Modeling and Visualization","authors":"Pierre Goovaerts, Alexa Rihana-Abdallah, Yuncong Pang","doi":"10.1007/s11004-023-10107-4","DOIUrl":"https://doi.org/10.1007/s11004-023-10107-4","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135803411","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
Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods 基于数据驱动方法的时空滑坡易感性建模
3区 地球科学
Mathematical Geosciences Pub Date : 2023-10-14 DOI: 10.1007/s11004-023-10105-6
Zhice Fang, Yi Wang, Cees van Westen, Luigi Lombardo
{"title":"Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods","authors":"Zhice Fang, Yi Wang, Cees van Westen, Luigi Lombardo","doi":"10.1007/s11004-023-10105-6","DOIUrl":"https://doi.org/10.1007/s11004-023-10105-6","url":null,"abstract":"","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135803360","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: 2
The Many Forms of Co-kriging: A Diversity of Multivariate Spatial Estimators 协同克里格的多种形式:多元空间估计量的多样性
3区 地球科学
Mathematical Geosciences Pub Date : 2023-10-12 DOI: 10.1007/s11004-023-10104-7
Peter A. Dowd, Eulogio Pardo-Igúzquiza
{"title":"The Many Forms of Co-kriging: A Diversity of Multivariate Spatial Estimators","authors":"Peter A. Dowd, Eulogio Pardo-Igúzquiza","doi":"10.1007/s11004-023-10104-7","DOIUrl":"https://doi.org/10.1007/s11004-023-10104-7","url":null,"abstract":"Abstract In this expository review paper, we show that co-kriging, a widely used geostatistical multivariate optimal linear estimator, has a diverse range of extensions that we have collected and illustrated to show the potential of this spatial interpolator. In the context of spatial stochastic processes, this paper covers scenarios including increasing the spatial resolution of a spatial variable (downscaling), solving inverse problems, estimating directional derivatives, and spatial interpolation taking boundary conditions into account. All these spatial interpolators are optimal linear estimators in the sense of being unbiased and minimising the variance of the estimation error.","PeriodicalId":51117,"journal":{"name":"Mathematical Geosciences","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013532","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
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