Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China
This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation;...
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/10029 |
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| author | Bo Zhao Dehui Zhang Rongzhen Zhang Zhu Li Panpan Tang Haoming Wan |
| author_facet | Bo Zhao Dehui Zhang Rongzhen Zhang Zhu Li Panpan Tang Haoming Wan |
| author_sort | Bo Zhao |
| collection | DOAJ |
| description | This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran’s I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen ore-forming elements (Cu, Pb, Zn, W, Sn, Mo, F, Au, Fe<sub>2</sub>O<sub>3</sub>, etc.) in stream sediment samples in the Bayantala-Mingantu district, North China. The results showed that the OGE analysis performed at lower levels of scale greatly improved the quality of anomaly recognition: more than 80% of the known ore spots, no matter what their scales and mineral species, were predicted in less than 45% of the study area, and most of the ore spots falling outside the delineated anomalous regions occur nearby them. OGE can extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it outperforms other convolutional autoencoder models such as GAUGE in anomaly detection. |
| first_indexed | 2024-03-09T22:00:33Z |
| format | Article |
| id | doaj.art-64e5501d9b804a2482fa9354e80d668d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:00:33Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-64e5501d9b804a2482fa9354e80d668d2023-11-23T19:49:49ZengMDPI AGApplied Sciences2076-34172022-10-0112191002910.3390/app121910029Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North ChinaBo Zhao0Dehui Zhang1Rongzhen Zhang2Zhu Li3Panpan Tang4Haoming Wan5Research Center of Big Data Technology, Nanhu Laboratory, Jiaxing 314000, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100086, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100086, ChinaSchool of Earth Sciences and Resources, China University of Geosciences, Beijing 100086, ChinaResearch Center of Big Data Technology, Nanhu Laboratory, Jiaxing 314000, ChinaResearch Center of Big Data Technology, Nanhu Laboratory, Jiaxing 314000, ChinaThis research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran’s I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen ore-forming elements (Cu, Pb, Zn, W, Sn, Mo, F, Au, Fe<sub>2</sub>O<sub>3</sub>, etc.) in stream sediment samples in the Bayantala-Mingantu district, North China. The results showed that the OGE analysis performed at lower levels of scale greatly improved the quality of anomaly recognition: more than 80% of the known ore spots, no matter what their scales and mineral species, were predicted in less than 45% of the study area, and most of the ore spots falling outside the delineated anomalous regions occur nearby them. OGE can extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it outperforms other convolutional autoencoder models such as GAUGE in anomaly detection.https://www.mdpi.com/2076-3417/12/19/10029object-based image analysisgraph neural networkgeochemical anomaliesfractal dimension |
| spellingShingle | Bo Zhao Dehui Zhang Rongzhen Zhang Zhu Li Panpan Tang Haoming Wan Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China Applied Sciences object-based image analysis graph neural network geochemical anomalies fractal dimension |
| title | Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China |
| title_full | Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China |
| title_fullStr | Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China |
| title_full_unstemmed | Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China |
| title_short | Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China |
| title_sort | delineation and analysis of regional geochemical anomaly using the object oriented paradigm and deep graph learning a case study in southeastern inner mongolia north china |
| topic | object-based image analysis graph neural network geochemical anomalies fractal dimension |
| url | https://www.mdpi.com/2076-3417/12/19/10029 |
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