{"title":"利用机器学习和子像素混合算法分析雷萨迪耶(托卡特-图尔基耶)膨润土矿床的地球化学特征并绘制地图","authors":"Oktay Canbaz , Muhittin Karaman","doi":"10.1016/j.chemer.2024.126123","DOIUrl":null,"url":null,"abstract":"<div><div><span><span><span>Reşadiye bentonite deposits, which play a significant role in Türkiye's bentonite production, are situated in Central Anatolia. Geochemical, mineralogical, and </span>remote sensing<span> data have been integrated to map the spatial distribution of clay minerals in the bentonite deposits and argillic areas. It is hypothesized that the bentonite samples occurred by the in-situ diagenetic alteration of rhyolite-dacite, trachyte, and andesite/basaltic andesitic composition pyroclastic rocks (ash-flow tuff). Biotite, </span></span>clinoptilolite, </span>calcite<span>, dolomite, K-feldspar, opal-CT, quartz, and clay minerals are detected in most bentonite samples. The clay patterns determined in the bentonite samples in the X-ray diffraction (XRD) diagrams were 12.3–12.6 Å and were interpreted as being rich in Na-smectites. Mineral mapping in these deposits is essential for mining operations since the high-grade bentonite deposits can be affected by the other clay, gang, and ore minerals<span><span> they contain in addition to the smectite. The sample spectra measurements matched montmorillonite and kaolin/smectite spectra. This study tests support vector machine<span> (SVM) and artificial neural network (ANN) machine learning and MTMF subpixel algorithms in </span></span>lithology and mineral mapping in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. It combines the power of subpixel unmixing algorithms to determine the distribution of clay and high-grade bentonites in argillic areas discriminated by machine learning. The results showed that the SVM algorithm can map better than ANN for argillic areas. Additionally, the distribution of high-grade bentonite and kaolin/smectite bearing sites in the study area is discriminated by the mixture-tuned matched filtered (MTMF) spectral classification method. As a result, this study shows that remote sensing studies can be utilized for the exploration and monitoring of high-grade bentonite sites during and/or post-mining operations.</span></span></div></div>","PeriodicalId":55973,"journal":{"name":"Chemie Der Erde-Geochemistry","volume":"84 4","pages":"Article 126123"},"PeriodicalIF":2.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms\",\"authors\":\"Oktay Canbaz , Muhittin Karaman\",\"doi\":\"10.1016/j.chemer.2024.126123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div><span><span><span>Reşadiye bentonite deposits, which play a significant role in Türkiye's bentonite production, are situated in Central Anatolia. Geochemical, mineralogical, and </span>remote sensing<span> data have been integrated to map the spatial distribution of clay minerals in the bentonite deposits and argillic areas. It is hypothesized that the bentonite samples occurred by the in-situ diagenetic alteration of rhyolite-dacite, trachyte, and andesite/basaltic andesitic composition pyroclastic rocks (ash-flow tuff). Biotite, </span></span>clinoptilolite, </span>calcite<span>, dolomite, K-feldspar, opal-CT, quartz, and clay minerals are detected in most bentonite samples. The clay patterns determined in the bentonite samples in the X-ray diffraction (XRD) diagrams were 12.3–12.6 Å and were interpreted as being rich in Na-smectites. Mineral mapping in these deposits is essential for mining operations since the high-grade bentonite deposits can be affected by the other clay, gang, and ore minerals<span><span> they contain in addition to the smectite. The sample spectra measurements matched montmorillonite and kaolin/smectite spectra. This study tests support vector machine<span> (SVM) and artificial neural network (ANN) machine learning and MTMF subpixel algorithms in </span></span>lithology and mineral mapping in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. It combines the power of subpixel unmixing algorithms to determine the distribution of clay and high-grade bentonites in argillic areas discriminated by machine learning. The results showed that the SVM algorithm can map better than ANN for argillic areas. Additionally, the distribution of high-grade bentonite and kaolin/smectite bearing sites in the study area is discriminated by the mixture-tuned matched filtered (MTMF) spectral classification method. As a result, this study shows that remote sensing studies can be utilized for the exploration and monitoring of high-grade bentonite sites during and/or post-mining operations.</span></span></div></div>\",\"PeriodicalId\":55973,\"journal\":{\"name\":\"Chemie Der Erde-Geochemistry\",\"volume\":\"84 4\",\"pages\":\"Article 126123\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemie Der Erde-Geochemistry\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0009281924000473\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemie Der Erde-Geochemistry","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009281924000473","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
摘要
re adiye膨润土矿床位于安纳托利亚中部,在 rkiye膨润土生产中起着重要作用。综合地球化学、矿物学和遥感数据,绘制了膨润土矿床和泥质区粘土矿物的空间分布图。推测膨润土样品是由流纹岩-英安岩、粗面岩和安山岩/玄武安山岩组成的火山碎屑岩(灰流凝灰岩)原位成岩蚀变而成。在大多数膨润土样品中检测到黑云母、斜发沸石、方解石、白云石、钾长石、蛋白石ct、石英和粘土矿物。膨润土样品在x射线衍射(XRD)图上的粘土图案为12.3 ~ 12.6 Å,被解释为富含钠蒙脱石。这些矿床的矿物测绘对采矿作业至关重要,因为高品位膨润土矿床除了蒙脱石外,还会受到其他粘土、钢和矿石矿物的影响。样品光谱测量结果与蒙脱土和高岭土/蒙脱石光谱相匹配。本研究测试了支持向量机(SVM)和人工神经网络(ANN)机器学习和MTMF亚像素算法在先进星载热发射与反射辐射计(ASTER)卫星数据中的岩性和矿物成图。它结合了亚像素分解算法的力量,通过机器学习来确定粘土和高级膨润土在泥质区域的分布。结果表明,支持向量机算法比人工神经网络能更好地映射出弹性区域。此外,采用混合调谐匹配滤波(MTMF)光谱分类方法对研究区内高岭土和高岭土/蒙脱石含矿点的分布进行了判别。因此,本研究表明,遥感研究可用于在采矿作业期间和/或采矿后勘探和监测高品位膨润土场址。
Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms
Reşadiye bentonite deposits, which play a significant role in Türkiye's bentonite production, are situated in Central Anatolia. Geochemical, mineralogical, and remote sensing data have been integrated to map the spatial distribution of clay minerals in the bentonite deposits and argillic areas. It is hypothesized that the bentonite samples occurred by the in-situ diagenetic alteration of rhyolite-dacite, trachyte, and andesite/basaltic andesitic composition pyroclastic rocks (ash-flow tuff). Biotite, clinoptilolite, calcite, dolomite, K-feldspar, opal-CT, quartz, and clay minerals are detected in most bentonite samples. The clay patterns determined in the bentonite samples in the X-ray diffraction (XRD) diagrams were 12.3–12.6 Å and were interpreted as being rich in Na-smectites. Mineral mapping in these deposits is essential for mining operations since the high-grade bentonite deposits can be affected by the other clay, gang, and ore minerals they contain in addition to the smectite. The sample spectra measurements matched montmorillonite and kaolin/smectite spectra. This study tests support vector machine (SVM) and artificial neural network (ANN) machine learning and MTMF subpixel algorithms in lithology and mineral mapping in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. It combines the power of subpixel unmixing algorithms to determine the distribution of clay and high-grade bentonites in argillic areas discriminated by machine learning. The results showed that the SVM algorithm can map better than ANN for argillic areas. Additionally, the distribution of high-grade bentonite and kaolin/smectite bearing sites in the study area is discriminated by the mixture-tuned matched filtered (MTMF) spectral classification method. As a result, this study shows that remote sensing studies can be utilized for the exploration and monitoring of high-grade bentonite sites during and/or post-mining operations.
期刊介绍:
GEOCHEMISTRY was founded as Chemie der Erde 1914 in Jena, and, hence, is one of the oldest journals for geochemistry-related topics.
GEOCHEMISTRY (formerly Chemie der Erde / Geochemistry) publishes original research papers, short communications, reviews of selected topics, and high-class invited review articles addressed at broad geosciences audience. Publications dealing with interdisciplinary questions are particularly welcome. Young scientists are especially encouraged to submit their work. Contributions will be published exclusively in English. The journal, through very personalized consultation and its worldwide distribution, offers entry into the world of international scientific communication, and promotes interdisciplinary discussion on chemical problems in a broad spectrum of geosciences.
The following topics are covered by the expertise of the members of the editorial board (see below):
-cosmochemistry, meteoritics-
igneous, metamorphic, and sedimentary petrology-
volcanology-
low & high temperature geochemistry-
experimental - theoretical - field related studies-
mineralogy - crystallography-
environmental geosciences-
archaeometry