6d2dbcf3953b062b262b3fc5feac94f7 RemSen_a2021v13n16p3258.pdf 69e67fd6a86fe71652a7ccc39fa60c0efaca612e RemSen_a2021v13n16p3258.pdf 2d393fb5d0659db7c5f5bf0bfa3c61786c266acba620f3f5cb00184e12cc1705 RemSen_a2021v13n16p3258.pdf Title: Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions Subject: This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces. Keywords: hyperspectral imaging; machine learning; spectral geology Author: Agustin Lobo, Emma Garcia, Gisela Barroso, David Martí, Jose-Luis Fernandez-Turiel and Jordi Ibáñez-Insa Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Aug 18 11:59:48 2021 CEST ModDate: Wed Aug 18 12:05:04 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2521492 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- QQBFAR+VnURWPalladioL Type 1 Custom yes yes no 10 0 NBJEFI+URWPalladioL-Ital Type 1 Custom yes yes no 15 0 QMHUMW+URWPalladioL-Bold Type 1 Custom yes yes no 20 0 FVFCEB+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 SSNIUI+VnURWPalladioL-Bold Type 1 Custom yes yes no 28 0 ZKJPJD+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 36 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 58 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 62 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 66 0 VORBJF+CMSY10 Type 1 Builtin yes yes no 81 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 88 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 91 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 94 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 104 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 107 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 110 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 123 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 126 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 129 0 MKDWFJ+EURM10 Type 1 Builtin yes yes no 136 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 143 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 153 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 156 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 159 0 DDLCDL+Arial CID TrueType Identity-H yes yes yes 162 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 174 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 177 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 180 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 187 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 190 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 193 0 DDLIGI+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 210 0 DDKLOL+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 213 0 DDKLON+PalatinoLinotype TrueType WinAnsi yes yes no 216 0 DDKLPP+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 219 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2023-12-01 02:35:52 CET RepresentationInformation: RemSen_a2021v13n16p3258.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-11-30 16:41:02 CET Size: 2521492 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 436 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Geological Settings Destination: section.2 Item: Title: Materials and Methods Destination: section.3 Children: Item: Title: Laboratory Imaging Spectrometry Destination: subsection.3.1 Item: Title: Ground-Based Panoramic Hyperspectral Imaging of Simulated Mine Face Destination: subsection.3.2 Item: Title: Classification Processing Destination: subsection.3.3 Children: Item: Title: Linear Discriminant Analysis (LDA) Destination: subsubsection.3.3.1 Item: Title: Support Vector Machines (SVM) Destination: subsubsection.3.3.2 Item: Title: Random Forest Destination: subsubsection.3.3.3 Item: Title: Validation Destination: subsubsection.3.3.4 Item: Title: Results Destination: section.4 Children: Item: Title: Laboratory Imaging Spectrometry Destination: subsection.4.1 Item: Title: Ground-Based Panoramic Hyperspectral Imaging of Simulated Mine Face Destination: subsection.4.2 Item: Title: Discussion Destination: section.5 Item: Title: Conclusions Destination: section.6 Item: Title: References Destination: section.6 Info: Title: Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin Tungsten Deposits: Simulation under Indoor Conditions Author: Agustin Lobo, Emma Garcia, Gisela Barroso, David Martí, Jose-Luis Fernandez-Turiel and Jordi Ibáñez-Insa Subject: This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450 950 nm and 950 1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten tin mine faces. 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