In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as constru...In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.展开更多
岩石颜色不仅反映沉积环境而且指示特有矿物与元素,是纵向横向沉积演化,地层对比的重要依据和指标之一。目前岩石颜色主要依赖肉眼识别和主观描述,或使用色卡进行对比判读。这些方法受个体差异和环境影响较大,缺少定量计算方法,无法满...岩石颜色不仅反映沉积环境而且指示特有矿物与元素,是纵向横向沉积演化,地层对比的重要依据和指标之一。目前岩石颜色主要依赖肉眼识别和主观描述,或使用色卡进行对比判读。这些方法受个体差异和环境影响较大,缺少定量计算方法,无法满足颜色批量精准识别的需要。因此快速、批量、高效实现颜色的客观识别和数值量化对地质工作研究和应用具有重要意义。该研究基于色度学原理,利用光谱分析技术,结合Python计算机语言编译的岩石颜色定量化识别软件,实现岩石颜色的数值定量化和批量自动化转换,提高颜色的判断精度和识别效率。通过对《Munsell Rock Book》对比发现,CIE RGB颜色系统计算结果与色卡一致性较高,Munsell系统计算结果中色相值(<3个NBS单位)一致性达到86.7%,明度值和纯度值的一致性分别达到92.2%和82.2%,相关性为98.83%和87.50%,均属于较小色差范围。相较于Munsell系统计算结果,31个岩石样品的CIE RGB计算结果与样品颜色的一致性和准确性更高。造成颜色差异的原因复杂多样,不仅与颜色系统之间的转换误差和人为主观对比及判读有关,而且与岩石样品的特殊性和环境等因素密切相关。本次研究为岩石颜色的快速、高效、客观批量化和定量化表征提供了一种可行性方法和思路,具有较好的应用价值。展开更多
文摘In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.
文摘岩石颜色不仅反映沉积环境而且指示特有矿物与元素,是纵向横向沉积演化,地层对比的重要依据和指标之一。目前岩石颜色主要依赖肉眼识别和主观描述,或使用色卡进行对比判读。这些方法受个体差异和环境影响较大,缺少定量计算方法,无法满足颜色批量精准识别的需要。因此快速、批量、高效实现颜色的客观识别和数值量化对地质工作研究和应用具有重要意义。该研究基于色度学原理,利用光谱分析技术,结合Python计算机语言编译的岩石颜色定量化识别软件,实现岩石颜色的数值定量化和批量自动化转换,提高颜色的判断精度和识别效率。通过对《Munsell Rock Book》对比发现,CIE RGB颜色系统计算结果与色卡一致性较高,Munsell系统计算结果中色相值(<3个NBS单位)一致性达到86.7%,明度值和纯度值的一致性分别达到92.2%和82.2%,相关性为98.83%和87.50%,均属于较小色差范围。相较于Munsell系统计算结果,31个岩石样品的CIE RGB计算结果与样品颜色的一致性和准确性更高。造成颜色差异的原因复杂多样,不仅与颜色系统之间的转换误差和人为主观对比及判读有关,而且与岩石样品的特殊性和环境等因素密切相关。本次研究为岩石颜色的快速、高效、客观批量化和定量化表征提供了一种可行性方法和思路,具有较好的应用价值。
