A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran
One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different ge...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2022.2159068 |
| _version_ | 1811153891161538560 |
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| author | Shokouh Riahi Abbas Bahroudi Maysam Abedi Soheila Aslani |
| author_facet | Shokouh Riahi Abbas Bahroudi Maysam Abedi Soheila Aslani |
| author_sort | Shokouh Riahi |
| collection | DOAJ |
| description | One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy. |
| first_indexed | 2024-03-11T23:46:52Z |
| format | Article |
| id | doaj.art-373809aca9ba4fbb927d37c417418bb1 |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:46:52Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-373809aca9ba4fbb927d37c417418bb12023-09-19T09:13:17ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2022.21590682159068A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of IranShokouh Riahi0Abbas Bahroudi1Maysam Abedi2Soheila Aslani3School of Mining Engineering, College of Engineering, University of TehranSchool of Mining Engineering, College of Engineering, University of TehranSchool of Mining Engineering, College of Engineering, University of TehranSchool of Mining Engineering, College of Engineering, University of TehranOne of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy.http://dx.doi.org/10.1080/10106049.2022.2159068remote sensingalteration mappinglineament extractiondata-driven multi-index overlayfuzzy ordered weighted averagingporphyry copper |
| spellingShingle | Shokouh Riahi Abbas Bahroudi Maysam Abedi Soheila Aslani A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran Geocarto International remote sensing alteration mapping lineament extraction data-driven multi-index overlay fuzzy ordered weighted averaging porphyry copper |
| title | A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran |
| title_full | A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran |
| title_fullStr | A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran |
| title_full_unstemmed | A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran |
| title_short | A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran |
| title_sort | comparative analysis of multi index overlay and fuzzy ordered weighted averaging methods for porphyry cu prospectivity mapping using remote sensing data the case study of chahargonbad area se of iran |
| topic | remote sensing alteration mapping lineament extraction data-driven multi-index overlay fuzzy ordered weighted averaging porphyry copper |
| url | http://dx.doi.org/10.1080/10106049.2022.2159068 |
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