A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification
Geological models are essential components in various applications. To generate reliable realizations, the geostatistical method focuses on reproducing spatial structures from training images (TIs). Moreover, uncertainty plays an important role in Earth systems. It is beneficial for creating an ense...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/11/2708 |
| _version_ | 1797596882243944448 |
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| author | Chen Zuo Zhuo Li Zhe Dai Xuan Wang Yue Wang |
| author_facet | Chen Zuo Zhuo Li Zhe Dai Xuan Wang Yue Wang |
| author_sort | Chen Zuo |
| collection | DOAJ |
| description | Geological models are essential components in various applications. To generate reliable realizations, the geostatistical method focuses on reproducing spatial structures from training images (TIs). Moreover, uncertainty plays an important role in Earth systems. It is beneficial for creating an ensemble of stochastic realizations with high diversity. In this work, we applied a pattern classification distribution (PCD) method to quantitatively evaluate geostatistical modeling. First, we proposed a correlation-driven template method to capture geological patterns. According to the spatial dependency of the TI, region growing and elbow-point detection were launched to create an adaptive template. Second, a combination of clustering and classification was suggested to characterize geological realizations. Aiming at simplifying parameter specification, the program employed hierarchical clustering and decision tree to categorize geological structures. Third, we designed a stacking framework to develop the multi-grid analysis. The contribution of each grid was calculated based on the morphological characteristics of TI. Our program was extensively examined by a channel model, a 2D nonstationary flume system, 2D subglacial bed topographic models in Antarctica, and 3D sandstone models. We activated various geostatistical programs to produce realizations. The experimental results indicated that PCD is capable of addressing multiple geological categories, continuous variables, and high-dimensional structures. |
| first_indexed | 2024-03-11T02:59:02Z |
| format | Article |
| id | doaj.art-dbc596e594dd4fb5ad4fa70bb03b4c90 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T02:59:02Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dbc596e594dd4fb5ad4fa70bb03b4c902023-11-18T08:27:35ZengMDPI AGRemote Sensing2072-42922023-05-011511270810.3390/rs15112708A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty QuantificationChen Zuo0Zhuo Li1Zhe Dai2Xuan Wang3Yue Wang4Department of Big Data Management and Applications, Chang’an University, Xi’an 710064, ChinaDepartment of Big Data Management and Applications, Chang’an University, Xi’an 710064, ChinaDepartment of Big Data Management and Applications, Chang’an University, Xi’an 710064, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai 264005, ChinaXi’an Key Laboratory of Digital Construction and Management for Transportation Infrastructure, Xi’an 710064, ChinaGeological models are essential components in various applications. To generate reliable realizations, the geostatistical method focuses on reproducing spatial structures from training images (TIs). Moreover, uncertainty plays an important role in Earth systems. It is beneficial for creating an ensemble of stochastic realizations with high diversity. In this work, we applied a pattern classification distribution (PCD) method to quantitatively evaluate geostatistical modeling. First, we proposed a correlation-driven template method to capture geological patterns. According to the spatial dependency of the TI, region growing and elbow-point detection were launched to create an adaptive template. Second, a combination of clustering and classification was suggested to characterize geological realizations. Aiming at simplifying parameter specification, the program employed hierarchical clustering and decision tree to categorize geological structures. Third, we designed a stacking framework to develop the multi-grid analysis. The contribution of each grid was calculated based on the morphological characteristics of TI. Our program was extensively examined by a channel model, a 2D nonstationary flume system, 2D subglacial bed topographic models in Antarctica, and 3D sandstone models. We activated various geostatistical programs to produce realizations. The experimental results indicated that PCD is capable of addressing multiple geological categories, continuous variables, and high-dimensional structures.https://www.mdpi.com/2072-4292/15/11/2708geostatistical modelingmultiple-point statisticsuncertainty quantificationsubglacial topographic modelhydrological model |
| spellingShingle | Chen Zuo Zhuo Li Zhe Dai Xuan Wang Yue Wang A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification Remote Sensing geostatistical modeling multiple-point statistics uncertainty quantification subglacial topographic model hydrological model |
| title | A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification |
| title_full | A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification |
| title_fullStr | A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification |
| title_full_unstemmed | A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification |
| title_short | A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification |
| title_sort | pattern classification distribution method for geostatistical modeling evaluation and uncertainty quantification |
| topic | geostatistical modeling multiple-point statistics uncertainty quantification subglacial topographic model hydrological model |
| url | https://www.mdpi.com/2072-4292/15/11/2708 |
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