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Deriving big geochemical data from high-resolution remote sensing data via machine learning:Application to a tailing storage facility in the Witwatersrand goldfields

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Artificial Intelligence in Geosciences 2023年第1期4卷 9-21页

作者： steven e.zhang Glen T.Nwaila Julie e.Bourdeau Yousef Ghorbani emmanuel John M.Carranza Natural Resources Canada Geological Survey of Canada601 Booth StreetOttawaOntarioK1A 0E8Canada Wits Mining Institute WMI University of the WitwatersrandPrivate Bag 32050WitsSouth Africa Department of Civil Environmental and Natural Resources EngineeringLuleåUniversity of TechnologySE 97187LuleåSweden School of Chemistry University of LincolnJoseph Banks LaboratoriesGreen LaneLincolnLincolnshireLN67DLUnited Kingdom Department of Geology University of the Free State205 Nelson Mandela DrBloemfontein9301South Africa

Remote sensing data is a cheap form of surficial geoscientific data,and in terms of veracity,velocity and volume,can sometimes be considered big *** spatial and spectral resolution continues to improve over time,and some modern satellites,such as the Copernicus Programme’s Sentinel-2 remote sensing satellites,offer a spatial resolution of 10 m across many of their spectral *** abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical *** ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data,which can be used for numerous downstream activities,particularly where data timeliness,volume and velocity are *** benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry,which currently entirely relies on manually derived data that is primarily guided by scientific ***,it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data ***,no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in *** this paper,we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data *** use gold grade data from a South African tailing storage facility(TSF)and data from both the Landsat-8 and Sentinel remote sensing *** show that various machine learning algorithms can be used given the abundance of training ***,we are able t

关键词： Remote sensing Big geochemical data Machine learning Tailing storage facilities Witwatersrand Basin Dry labs

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Towards a fully data-driven prospectivity mapping methodology:A case study of the Southeastern Churchill Province,Québec and Labrador

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Artificial Intelligence in Geosciences 2021年第1期2卷 128-147页

作者： steven e.zhang Julie e.Bourdeau Glen T.Nwaila David Corrigan SmartMin Limited 39 Kiewiet StreetHelikon Park1759South Africa Geological Survey of Canada 601 Booth StreetOttawaOntarioK1A 0E8Canada School of Geosciences University of the Witwatersrand1 Jan Smuts AveJohannesburg2000South Africa Lynn Street GatineauQuebecJ9H 1B5Canada

Mineral exploration campaigns are financially *** state-of-the-art methods have been developed to mitigate the risk,including predictive modelling of mineral prospectivity using principal component analysis(PCA)and geographic information systems(GIS).The PCA and GIS approach is currently considered acceptable for generating mineral exploration ***,some of its limitations are the dependence on sample stoichiometry(e.g.,the existence of minerals),the necessity of log-ratio transformations when dealing with compositional data,and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity *** this study,we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using *** showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province(Quebec and Labrador),*** region is known for its Rees endowment and the data were gathered for prospectivity mapping.A comparison with the established multivariate hybrid data-and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort,our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate *** results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied *** findings have potentially wider implications on exploration target generation,where project risks(financial,environmental,political,etc.)and geochemical anomalies must be quantified using robust and effective datadriven *** addition,our methodology is more replicable and objective,as manual geoscientific interpretation is not required during the detection of geochemical anomalies.

关键词： ML Mineral prospectivity mapping Principal component analysis Geochemical anomaly Rees

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Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns

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Artificial Intelligence in Geosciences 2022年第1期3卷 86-100页

作者： steven e.zhang Julie e.Bourdeau Glen T.Nwaila Yousef Ghorbani Geological Survey of Canada 601 Booth StreetOttawaOntarioK1A 0E8Canada Wits Mining Institute University of the Witwatersrand1 Jan Smuts Ave.Johannesburg2000South Africa Department of Civil Environmental and Natural Resources EngineeringLuleåUniversity of TechnologySE–97187LuleåSweden

In exploration geochemistry,advances in the detection limit,breadth of elements analyze-able,accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized *** a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data,especially where modern data is considerably different than legacy data,it is an expensive *** risk associated with modernizing such legacy data lies within its uncertainty in return(e.g.,the possibility of new discoveries,in primarily greenfield settings).Without any advanced knowledge of yet unanalyzed elements,the importance of re-analyses remains *** address this uncertainty,we apply machine learning to multivariate geochemical data from different regions in Canada(i.e.,the Churchill Province and the Trans-Hudson Orogen)in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned *** study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data(e.g.,prospectivity mapping).Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.

关键词： Machine learning Re-purposing legacy data Advanced exploration knowledge Canada Geochemical data

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

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Artificial Intelligence in Geosciences 2023年第1期4卷 137-149页

作者： steven e.zhang Glen T.Nwaila Shenelle Agard Julie e.Bourdeau emmanuel John M.Carranza Yousef Ghorbani Wits Mining Institute University of the Witwatersrand1 Jan Smuts Ave.Johannesburg2000South Africa Department of earth Sciences Uppsala UniversitySE 75236UppsalaSweden Department of Geology University of the Free State205 Nelson Mandela Dr.Bloemfontein9301South Africa School of Chemistry University of LincolnJoseph Banks LaboratoriesGreen LaneLincolnLincolnshireLN67DLUnited Kingdom

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 *** 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 ***,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 ***,we were able to infer extraction sequencing(to the resolution of the data),acid mine drainage formation and seasonal *** 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

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Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

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Artificial Intelligence in Geosciences 2021年第1期2卷 60-75页

作者： steven e.zhang Glen T.Nwaila Julie e.Bourdeau Lewis D.Ashwal SmartMin Limited 39 Kiewiet StreetHelikon Park1759South Africa School of Geosciences University of the Witwatersrand1 Jan Smuts AveJohannesburg2000South Africa Geological Survey of Canada 601 Booth StreetOttawaOntarioK1A 0E8Canada

In this study,we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province(Gondwana Supercontinent).Wedemonstrate that a variety of trace elements,including most of the lanthanides,chalcophile,lithophile,and siderophile elements,can be predicted with excellent *** finding reveals that there are reliable,high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic *** the major and minor elements are used as predictors,prediction performance can be used as a direct proxy for geochemical *** such,our proposed method is suitable for prospective exploration by identifying anomalous trace element *** to multivariate compositional data analysis methods,the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical *** we do not use multivariate compositional data analysis techniques(*** component analysis and combined use of major,minor and trace elements data),we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature ***,we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the *** approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping,or be used as a pre-processor to reduce the detection of false geochemical anomalies,particularly where the data is of variable quality.

关键词： Machine learning Predictive modelling Compositional data Prospectivity mapping Anomaly detection Karoo igneous province

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

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Artificial Intelligence in Geosciences 2022年第1期3卷 71-85页

作者： Glen T.Nwaila steven e.zhang Julie e.Bourdeau Yousef Ghorbani emmanuel John M.Carranza Wits Mining Institute University of the Witwatersrand1 Jan Smuts Ave.Johannesburg2000South Africa Geological Survey of Canada 601 Booth StreetOttawaOntarioK1A 0E8Canada Department of Civil Environmental and Natural Resources EngineeringLuleåUniversity of TechnologySE–97187LuleåSweden Department of Geology University of the Free State205 Nelson Mandela DrBloemfontein9301South Africa

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 *** 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 *** sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low *** sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral *** 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 *** 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 *** 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 *** 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 *** summarise the anomalies in the area,principal component analysis was used on the reconstruction errors across all modelled *** method en

关键词： Anomaly detection Iron deposit Lansat-8 Remote sensing Machine learning exploration Prospecting

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Smurfs have“fused”into the asymmetric division of stem cells

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Protein & Cell 2011年第1期2卷 2-4页

作者： steven Y.Cheng Ying e.zhang Department of Developmental Genetics Center for Regenerative MedicineNanjing Medical University140 Hanzhong RoadNanjing 210029China Laboratory of Cellular and Molecular Biology National Cancer InstituteNational Institutes of Health37 Convent DriveBethesdaMD 20892USA

The asymmetric cell division is the way in which a stem cell divides into one daughter stem cell and one differentiated daughter *** process is one of the key principles of developmental biology that ensures the perpetual supply of stem cells while allowing a particular cell lineage to be *** Drosophila oogenesis,the fate of the daughter stem cell produced from the asymmetric division of germline stem cells(GSCs)is specified by Decapentaplegic(Dpp),but the other daughter cell has almost equal access to the Dpp *** Dpp signaling is inactivated in the differentiated daughter cell has been a deep mystery until a recent study,led by Dahua Chen of the Institute of Zoology,China(Xia et al.,2010),showed that the Dpp receptor,Thick vein(Tkv),is degraded specifically in the differentiated daughter cell by an HeCT-domain ubiquitin e3 ligase,Smurf,in conjunction with a serine-threonine kinase Fused(Fu).This finding is as much revealing as it is surprising,because up until now Fu is considered a core member of the Hedgehog signaling pathway.

关键词： daughter asymmetric fused

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Chasing Sequencing Perfection:Marching Toward Higher Accuracy and Lower Costs

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Genomics, Proteomics & Bioinformatics 2024年第2期22卷 1-12页

作者： Hangxing Jia Shengjun Tan Yong e.zhang CAS Key Laboratory of Zoological Systematics and evolution&State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of ZoologyChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China CAS Center for excellence in Animal evolution and Genetics Chinese Academy of SciencesKunming 650223China

Next-generation sequencing(NGS),represented by Illumina platforms,has been an essential cornerstone of basic and applied ***,the sequencing error rate of 1 per 1000 bp(10^(−3))represents a serious hurdle for research areas focusing on rare mutations,such as somatic mosaicism or microbe *** examining the high-fidelity sequencing methods developed in the past decade,we summarized three major factors underlying errors and the corresponding 12 strategies mitigating these *** then proposed a novel framework to classify 11 preexisting representative methods according to the corresponding combinatory strategies and identified three trends that emerged during methodological *** further extended this analysis to eight long-read sequencing methods,emphasizing error reduction ***,we suggest two promising future directions that could achieve comparable or even higher accuracy with lower costs in both NGS and long-read sequencing.

关键词： Sequencing error High-fidelity sequencing Consensus sequencing Single-molecule sequencing Rare mutation

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The power of“controllers”:Transposon-mediated duplicated genes evolve towards neofunctionalization

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Journal of Genetics and Genomics 2023年第7期50卷 462-472页

作者： Huijing Ma Mengxia Wang Yong e.zhang Shengjun Tan Key Laboratory of Zoological Systematics and evolution&State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of ZoologyChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China CAS Center for excellence in Animal evolution and Genetics Chinese Academy of SciencesKunmingYunnan 650223China Chinese Institute for Brain Research Beijing 102206China

Since the discovery of the first transposon by *** McClintock,the prevalence and diversity of transposable elements(Tes)have been gradually *** fundamental genetic components,Tes drive organismal evolution not only by contributing functional sequences(e.g.,regulatory elements or“controllers”as phrased by ***)but also by shuffling genomic *** the latter respect,Te-mediated gene duplications have contributed to the origination of new genes and attracted extensive *** response to the development of this field,we herein attempt to provide an overview of Temediated duplication by focusing on common rules emerging across duplications generated by different Te ***,despite the huge divergence of transposition machinery across Tes,we identify three common features of various Te-mediated duplication mechanisms,including end bypass,template switching,and recurrent *** three features lead to one common functional outcome,namely,Te-mediated duplicates tend to be subjected to exon shuffling and ***,the intrinsic properties of the mutational mechanism constrain the evolutionary trajectories of these *** finally discuss the future of this field including an in-depth characterization of both the duplication mechanisms and functions of Te-mediated duplicates.

关键词： Transposable elements Gene duplication New gene origination end bypass Template switching exon shuffling Neofunctionalization Mutational constraint

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A unified intrinsic functional expansion theory for solitary waves

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Acta Mechanica Sinica 2005年第1期21卷 1-15页

作者： Theodore Yaotsu Wu John Kao Jin e.zhang California Institute of Technology Pasadena.CA 91125U.S.A.FellowInstitute of MechanicsChinese Academy of SciencesBeijing 100080China Warner Brothers GlendaleCA 91203.U.S.A. The university of Hong Kong.Pokfulam Road.Hong Kong SAR.China

A new theory is developed here for evaluating solitary waves on water, with results of high accuracy uniformly valid for waves of all heights, from the highest wave with a corner crest of 120 down to very low ones of diminishing height. Solutions are sought for the euler model by employing a unified expansion of the logarithmic hodograph in terms of a set of intrinsic component functions analytically determined to represent all the intrinsic properties of the wave entity from the wave crest to its outskirts. The unknown coefficients in the expansion are determined by minimization of the mean-square error of the solution, with the minimization optimized so as to take as few terms as needed to attain results as high in accuracy as attainable. In this regard, Stokess formula, F2= tan , relating the wave speed (the Froude number F) and the logarithmic decrement of its wave field in the outskirt, is generalized to establish a new criterion requiring (for minimizing solution error) the functional expansion to contain a finite power series in M terms of Stokess basic term (singular in ), such that 2M is just somewhat beyond unity, i.e. 2M1. This fundamental criterion is fully validated by solutions for waves of various amplitude-to-water depth ratio =a/h, especially about 0.01, at which M=10 by the criterion. In this pursuit, the class of dwarf solitary waves, defined for waves with 0.01, is discovered as a group of problems more challenging than even the highest wave. For the highest wave, a new solution is determined here to give the maximum height hst=0.8331990, and speed Fhst=1.290890, accurate to the last significant figure, which seems to be a new record.

关键词： Solitary waves on water Unified intrinsic functional expansion theory exact solutions High-accuracy computation of waves of arbitrary height Mass and energy transfer

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