Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data; Remote Sensing; Vol. 15, iss. 15
| Parent link: | Remote Sensing Vol. 15, iss. 15.— 2023.— [3708, 29 p.] |
|---|---|
| Institution som forfatter: | |
| Andre forfattere: | , , , |
| Summary: | Title screen The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb ? Zn/Cu ? Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces. |
| Sprog: | engelsk |
| Udgivet: |
2023
|
| Fag: | |
| Online adgang: | http://earchive.tpu.ru/handle/11683/132537 https://doi.org/10.3390/rs15153708 |
| Format: | Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=379343 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 379343 | ||
| 005 | 20250917145527.0 | ||
| 035 | |a (RuTPU)RU\TPU\retro\36810 | ||
| 035 | |a RU\TPU\network\37951 | ||
| 090 | |a 379343 | ||
| 100 | |a 20230912d2023 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data |f M. Abedini, M. Ziaii, T. V. Timkin, A. B. Pour | |
| 203 | |a Текст |c электронный | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 108 tit.] | ||
| 330 | |a The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). | ||
| 330 | |a Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb ? Zn/Cu ? Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces. | ||
| 461 | |t Remote Sensing | ||
| 463 | |t Vol. 15, iss. 15 |v [3708, 29 p.] |d 2023 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a machine learning | |
| 610 | 1 | |a mining geochemistry | |
| 610 | 1 | |a remote sensing | |
| 610 | 1 | |a random forest | |
| 610 | 1 | |a geochemical zonality | |
| 610 | 1 | |a copper mineralization | |
| 610 | 1 | |a mineral prospectivity mapping | |
| 610 | 1 | |a машинное обучение | |
| 610 | 1 | |a горная геохимия | |
| 610 | 1 | |a дистанционное зондирование | |
| 610 | 1 | |a леса | |
| 610 | 1 | |a геохимическая зональность | |
| 610 | 1 | |a минерализация | |
| 610 | 1 | |a картирование | |
| 610 | 1 | |a перспективность | |
| 610 | 1 | |a полезные ископаемые | |
| 701 | 1 | |a Abedini |b M. |g Mahnaz | |
| 701 | 1 | |a Ziaii |b M. |g Mansour | |
| 701 | 1 | |a Timkin |b T. V. |c geologist |c Associate Professor of Tomsk Polytechnic University, Candidate of geological and mineralogical sciences |f 1983- |g Timothy Vasilyevich |3 (RuTPU)RU\TPU\pers\33150 |9 16968 | |
| 701 | 1 | |a Pour |b A. B. |g Amin Beiranvand | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа природных ресурсов |b Отделение геологии |3 (RuTPU)RU\TPU\col\23542 |
| 801 | 2 | |a RU |b 63413507 |c 20230912 |g RCR | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/132537 |z http://earchive.tpu.ru/handle/11683/132537 | |
| 856 | 4 | |u https://doi.org/10.3390/rs15153708 | |
| 942 | |c CF | ||