Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities  

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

机构地区：[1]Wits Mining Institute,University of the Witwatersrand,1 Jan Smuts Ave.,Johannesburg,2000,South Africa [2]Department of Earth Sciences,Uppsala University,SE 75236,Uppsala,Sweden [3]Department of Geology,University of the Free State,205 Nelson Mandela Dr.,Bloemfontein,9301,South Africa [4]School of Chemistry,University of Lincoln,Joseph Banks Laboratories,Green Lane,Lincoln,Lincolnshire,LN67DL,United Kingdom

出　　处：《Artificial Intelligence in Geosciences》2023年第1期137-149,共13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：Evolution in geoscientific data provides the mineral industry with new opportunities.A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity.This direction is more significant where traditional geochemical data are not ideal,which is the case for evaluating unconventional resources,such as tailing storage facilities(TSFs),because they are not static due to sedimentation,compaction and changes associated with hydrospheric and lithospheric processes(e.g.,erosion,saltation and mobility of chemical constituents).In this paper,we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin(South Africa).Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF,we trained a machine learning model to predict in-situ gold grades.Subsequently,we deployed the model to the Lindum TSF,which is 3 km away,over a period of a few years(2015-2019).We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF.Additionally,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal migration.These findings suggest that dynamic mineral resource models and live geochemical monitoring(e.g.,of elemental mobility and structural changes)are possible without additional physical sampling.

关 键 词：Big geochemical data Mine waste valorisation Tailings storage facilities Sentinel-2 Remote sensing Machine learning 

分 类 号：P59[天文地球—地球化学]