首页|Leucogranite mapping via convolutional recurrent neural networks and geochemical survey data in the Himalayan orogen

Leucogranite mapping via convolutional recurrent neural networks and geochemical survey data in the Himalayan orogen

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
  • 维普
Geochemical survey data analysis is recognized as an implemented and feasible way for lithological map-ping to assist mineral exploration.With respect to available approaches,recent methodological advances have focused on deep learning algorithms which provide access to learn and extract information directly from geochemical survey data through multi-level networks and outputting end-to-end classification.Accordingly,this study developed a lithological mapping framework with the joint application of a con-volutional neural network(CNN)and a long short-term memory(LSTM).The CNN-LSTM model is dom-inant in correlation extraction from CNN layers and coupling interaction learning from LSTM layers.This hybrid approach was demonstrated by mapping leucogranites in the Himalayan orogen based on stream sediment geochemical survey data,where the targeted leucogranite was expected to be potential resources of rare metals such as Li,Be,and W mineralization.Three comparative case studies were car-ried out from both visual and quantitative perspectives to illustrate the superiority of the proposed model.A guided spatial distribution map of leucogranites in the Himalayan orogen,divided into high-,moderate-,and low-potential areas,was delineated by the success rate curve,which further improves the efficiency for identifying unmapped leucogranites through geological mapping.In light of these results,this study provides an alternative solution for lithologic mapping using geochemical survey data at a regional scale and reduces the risk for decision making associated with mineral exploration.

Lithological mappingDeep learningConvolutional neural networkLong short-term memoryLeucogranites

Ziye Wang、Tong Li、Renguang Zuo

展开 >

State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan 430074,China

国家自然科学基金国家自然科学基金Natural Science Foundation of Hubei Province(China)Natural Science Foundation of Hubei Province(China)

41972303421023322023AFA0012023AFD232

2024

10.1016/j.gsf.2023.101715

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(1)
  • 5