Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

2021 ◽  
Author(s):  
Shuai Zhang ◽  
Emmanuel John M. Carranza ◽  
Hantao Wei ◽  
Keyan Xiao ◽  
Fan Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Driven ◽  
Mineral Prospectivity Mapping ◽  
Joint Application ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping

scholarly journals Data-driven based logistic function and prediction-area plot for mineral prospectivity mapping: a case study from the eastern margin of Qinling orogenic belt, central China

2021 ◽  
Author(s):  
Hongyang Bai ◽  
Yuan Cao ◽  
Heng Zhang ◽  
Wenfeng Wang ◽  
Chaojun Jiang ◽  
...  
Keyword(s):  
Logistic Function ◽  
Gold Deposits ◽  
Orogenic Gold ◽  
Central China ◽  
Data Driven ◽  
Orogenic Gold Deposits ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Potential Area ◽  
Prospectivity Mapping

Abstract he present work combines data-driven based logistic function with prediction-area plot for delineating target areas of orogenic gold deposits in eastern margin of Qinling metallogenic belt, central China. Firstly, the values of geological and geochemical information layer were transformed into a series of fuzzy numbers with a range of 0-1 through a data-driven based logistic function on the basis of mineralization theory of the orogenic gold deposits. Secondly, the prediction-area(P-A) plot was performed on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme for mineral prospectivity mapping(MPM) based on the known gold occurrences. What’s more, to further prove the advantages of this method, we also used a knowledge-driven approach for comparison purpose. Finally, with the concentration-area(C-A) fractal model, the fractal thresholds were determined and a mineral prospecting map was generated. The result, five of the six known gold deposits are located in high and moderate potential areas (accounts for 18.6 % of the study area), one in low potential area (accounts for 38.4 % of the study area) and none in weak potential area (accounts for 43 % of the study area), confirmed the joint application of data-driven based logistic function and prediction-area plot a simple, effective and low-cost method for mineral prospectivity mapping, which can be a guidance for further work in the research area.

A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping

2019 ◽  
Vol 29 (1) ◽  
pp. 247-265 ◽  
Author(s):  
Yongliang Chen ◽  
Wei Wu ◽  
Qingying Zhao
Keyword(s):  
Bat Algorithm ◽  
Data Driven ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping
Sign in / Sign up

Export Citation Format

Share Document