Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping
Abstract Accurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing...
Saved in:
| Main Authors: | Yue‐Lin Dong, Zhen‐Jie Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024JH000311 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Alteration mineral identification and metallogenic prediction of porphyry deposits based on geochemical data
by: Weikang ZHANG, et al.
Published: (2024-09-01) -
Geology and mineralization of the Duobaoshan supergiant porphyry Cu-Au-Mo-Ag deposit (2.36 Mt) in Heilongjiang Province, China: A review
by: Sen Zhang, et al.
Published: (2023-01-01) -
Application of tensor CSAMT with high-power orthogonal signal sources in Jiama porphyry copper deposit, South Tibet
by: Peng-liang Yu, et al.
Published: (2023-01-01) -
Imaging Baogutu granitic intrusions in Western Junggar, NW China using an audio-frequency magnetotelluric array
by: Bo Yang, et al.
Published: (2025-03-01) -
Metallogenic Regularity and Exploration Targets of Rutile Deposits in the Panxi-Huidong Area, Southwest China
by: Zhiquan JIA, et al.
Published: (2025-08-01)