The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义。为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5...岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义。为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5种岩石类型共3 700张岩石薄片图像进行岩性识别。在MobileNetV2的倒残差结构中嵌入坐标注意力机制,融合图像中多种矿物的全局特征信息。此外,改进MobileNetV2中的分类器,降低模型的参数量和计算复杂度,从而提高模型的运算速度和效率,并采用带泄露线性整流函数(leaky rectified linear unit, Leaky ReLU)作为激活函数,避免网络训练中的梯度消失问题。实验结果表明,本文提出的改进后的MobileNetV2模型大小仅为2.30 MB,在测试集上的精确率、召回率、F_(1)值分别为91.24%、90.18%、90.70%,具有较高的准确性,相比于SqueezeNet、ShuffleNetV2等同类型的轻量化网络,分类效果最好。展开更多
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) was used to analyze chemical elements—major, trace and rare earth elements (REE) concentrations, augmented with quantitative X-ray diffraction (XRD) analysis and ...Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) was used to analyze chemical elements—major, trace and rare earth elements (REE) concentrations, augmented with quantitative X-ray diffraction (XRD) analysis and thin-section petrography for mineralogical characterization of the Triassic Montney Formation in northeastern British Columbia, Western Canada Sedimentary Basin (WCSB). Results from this study indicate that integration of chemical elements with mineralogy shows affinity to the host lithologies. Evidently, chemical elements are the building blocks for minerals, thus, their significances in the interpretation of geological systems are unambiguous. Herein, major elements concentration such as Al, Fe, K, Mg, Ca, Mn in the samples analyzed from the Montney Formation are interpreted as: 1) indication of dolomitization and diagenesis;2) trace elements—Rb, Th, U, and Cs are related to the organic matter—kerogen in the clay component of the Montney Formation source rock;and 3) transition metals—Sc, V, Co, Cr, Zn show strong affinity with diagenesis in the study interval.展开更多
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)<span style="font-size:12px;font-family:Verdana;"><span style="font-size:12px;font-family:Verdana;"> </span></span><s...Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)<span style="font-size:12px;font-family:Verdana;"><span style="font-size:12px;font-family:Verdana;"> </span></span><span style="font-size:12px;font-family:Verdana;">was used to analyze </span><span style="font-size:10pt;font-family:'}', serif;"><span style="font-size:12px;font-family:Verdana;">chemical elements—</span><span style="font-size:12px;font-family:Verdana;">major, trace and rare earth elements</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">(REE) concentrations, </span></span><span style="font-size:10.0pt;font-family:" color:#222222;"=""><span style="font-size:12px;font-family:Verdana;">augmented with quantitative X-ray diffraction (XRD) analysis and thin-section petrography for</span><span style="font-size:12px;font-family:Verdana;"> </span></span><span style="font-size:10pt;font-family:'}', serif;"><span style="font-size:12px;font-family:Verdana;">mineralogical characterization of the Triassic Montney Formation in northeastern British Columbia, Western Canada Sedimentary</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">Basin (WCSB). Results from this study indicate</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">that integration of chemical elements with mineralogy shows affinity to the host lithologies. Evidently, chemical elements are the building blocks for minerals, thus, their significances</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">in the interpretation of geological systems are unambiguous. Herein, major elements concentration such as Al, Fe, K, Mg, Ca, Mn in the samples analyzed from the Montney Formation are interpreted as: 1) indication of dolomitization and diagenesis;2) trace elements—Rb, Th, U, and Cs are related to the organic matter—kerogen in the clay component of the Montney Formation source rock;and 3) transition metals—Sc, V, Co, Cr, Zn show strong affinity with diagenesis in the study interval.</span></span>展开更多
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义。为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5种岩石类型共3 700张岩石薄片图像进行岩性识别。在MobileNetV2的倒残差结构中嵌入坐标注意力机制,融合图像中多种矿物的全局特征信息。此外,改进MobileNetV2中的分类器,降低模型的参数量和计算复杂度,从而提高模型的运算速度和效率,并采用带泄露线性整流函数(leaky rectified linear unit, Leaky ReLU)作为激活函数,避免网络训练中的梯度消失问题。实验结果表明,本文提出的改进后的MobileNetV2模型大小仅为2.30 MB,在测试集上的精确率、召回率、F_(1)值分别为91.24%、90.18%、90.70%,具有较高的准确性,相比于SqueezeNet、ShuffleNetV2等同类型的轻量化网络,分类效果最好。
文摘Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) was used to analyze chemical elements—major, trace and rare earth elements (REE) concentrations, augmented with quantitative X-ray diffraction (XRD) analysis and thin-section petrography for mineralogical characterization of the Triassic Montney Formation in northeastern British Columbia, Western Canada Sedimentary Basin (WCSB). Results from this study indicate that integration of chemical elements with mineralogy shows affinity to the host lithologies. Evidently, chemical elements are the building blocks for minerals, thus, their significances in the interpretation of geological systems are unambiguous. Herein, major elements concentration such as Al, Fe, K, Mg, Ca, Mn in the samples analyzed from the Montney Formation are interpreted as: 1) indication of dolomitization and diagenesis;2) trace elements—Rb, Th, U, and Cs are related to the organic matter—kerogen in the clay component of the Montney Formation source rock;and 3) transition metals—Sc, V, Co, Cr, Zn show strong affinity with diagenesis in the study interval.
文摘Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)<span style="font-size:12px;font-family:Verdana;"><span style="font-size:12px;font-family:Verdana;"> </span></span><span style="font-size:12px;font-family:Verdana;">was used to analyze </span><span style="font-size:10pt;font-family:'}', serif;"><span style="font-size:12px;font-family:Verdana;">chemical elements—</span><span style="font-size:12px;font-family:Verdana;">major, trace and rare earth elements</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">(REE) concentrations, </span></span><span style="font-size:10.0pt;font-family:" color:#222222;"=""><span style="font-size:12px;font-family:Verdana;">augmented with quantitative X-ray diffraction (XRD) analysis and thin-section petrography for</span><span style="font-size:12px;font-family:Verdana;"> </span></span><span style="font-size:10pt;font-family:'}', serif;"><span style="font-size:12px;font-family:Verdana;">mineralogical characterization of the Triassic Montney Formation in northeastern British Columbia, Western Canada Sedimentary</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">Basin (WCSB). Results from this study indicate</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">that integration of chemical elements with mineralogy shows affinity to the host lithologies. Evidently, chemical elements are the building blocks for minerals, thus, their significances</span><span style="font-size:12px;font-family:Verdana;"> </span><span style="font-size:12px;font-family:Verdana;">in the interpretation of geological systems are unambiguous. Herein, major elements concentration such as Al, Fe, K, Mg, Ca, Mn in the samples analyzed from the Montney Formation are interpreted as: 1) indication of dolomitization and diagenesis;2) trace elements—Rb, Th, U, and Cs are related to the organic matter—kerogen in the clay component of the Montney Formation source rock;and 3) transition metals—Sc, V, Co, Cr, Zn show strong affinity with diagenesis in the study interval.</span></span>
