Recognizing geochemical patterns related to mineralization using a self-organizing map-Reference-Cited by-同舟云学术

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Recognizing geochemical patterns related to mineralization using a self-organizing map

Published:2023-04 Issue: Volume:151 Page:105621
ISSN:0883-2927
Container-title:Applied Geochemistry
language:en
Short-container-title:Applied Geochemistry

Author:

Chen Zhiyi,Xiong Yihui,Yin Bojun,Sun Siquan,Zuo Renguang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Pollution,Environmental Chemistry

Reference45 articles.

1. Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit;Abedi;Arabian J. Geosci.,2013

2. Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran;Bigdeli;J. Geochem. Explor.,2022

3. The short wave infrared (SWIR) spectral characteristics of alteration minerals and applications for ore exploration in the typical skarn-porphyry deposits, Edong ore district, eastern China;Chen;Acta Petrol. Sin.,2019

4. A multi-convolutional autoencoder approach to multivariate geochemical anomaly recognition;Chen;Minerals,2019

5. An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks;Depren;Expert Syst. Appl.,2005

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Extraction of weak geochemical anomalies based on multiple-point statistics and local singularity analysis;Computational Geosciences;2024-01-27

2. Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data;Remote Sensing;2023-07-25

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