Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager  

作　　者：Glen T.Nwaila Steven E.Zhang Julie E.Bourdeau Yousef Ghorbani Emmanuel John M.Carranza 

机构地区：[1]Wits Mining Institute,University of the Witwatersrand,1 Jan Smuts Ave.,Johannesburg,2000,South Africa [2]Geological Survey of Canada,601 Booth Street,Ottawa,Ontario,K1A 0E8,Canada [3]Department of Civil,Environmental and Natural Resources Engineering,LuleåUniversity of Technology,SE–97187,Luleå,Sweden [4]Department of Geology,University of the Free State,205 Nelson Mandela Dr,Bloemfontein,9301,South Africa

出　　处：《Artificial Intelligence in Geosciences》2022年第1期71-85,共15页地学人工智能（英文）

基　　金：Supported by a Department of Science and Innovation(DSI)-National Research Foundation(NRF)Thuthuka Grant(Grant UID:121973)and DSI-NRF CIMERA.

摘　　要：Most known mineral deposits were discovered by accident using expensive,time-consuming,and knowledgebased methods such as stream sediment geochemical data,diamond drilling,reconnaissance geochemical and geophysical surveys,and/or remote sensing.Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials,prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration.Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost.Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation.In this study,we extend an artificial intelligence-based,unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager(OLI)satellite imagery and machine learning.The novelty in our method includes:(1)knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures;(2)detection of anomalies occurs only in the variable domain;and(3)a choice of a range of machine learning algorithms to balance between explain-ability and performance.Our new unsupervised method detects anomalies through three successive stages,namely(a)stage Ⅰ–acquisition of satellite imagery,data processing and selection of bands,(b)stage Ⅱ–predictive modelling and anomaly detection,and(c)stage Ⅲ–construction of anomaly maps and analysis.In this study,the new method was tested over the Assen iron deposit in the Transvaal Supergroup(South Africa).It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known.To summarise the anomalies in the area,principal component analysis was used on the reconstruction errors across all modelled bands.Our method enhanced the Assen deposit as an anoma

关 键 词：Anomaly detection Iron deposit Lansat-8 Remote sensing Machine learning Exploration PROSPECTING 

分 类 号：P61[天文地球—矿床学]