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Automated analysis of petrographic thin section images using advanced machine learning techniques

机译:采用先进机器学习技术自动分析岩体薄剖视图

摘要

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated analysis of petrographic thin section images. In one aspect, a method includes determining a first image of a petrographic thin section of a rock sample, and determining a feature vector for each pixel of the first image. Multiple different regions of the petrographic thin section are determined by clustering the pixels of the first image based on the feature vectors, wherein one of the regions corresponds to grains in the petrographic thin section. The method further includes determining a second image of the petrographic thin section, including combining images of the petrographic thin section acquired with plane-polarized light and cross-polarized light. Multiple grains are segmented from the second image of the petrographic thin section based on the multiple different regions from the first image, and characteristics of the segmented grains are determined.
机译:方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于自动分析岩晶薄截面图像。在一个方面,一种方法包括确定岩石样本的岩图像薄​​部分的第一图像,并确定第一图像的每个像素的特征向量。通过基于特征向量聚类第一图像的像素来确定岩体薄部分的多个不同区域,其中一个区域对应于岩晶薄部分中的颗粒。该方法还包括确定岩晶薄部分的第二图像,包括用平面偏振光和交叉偏振光获取的岩体薄部分的组合图像。基于来自第一图像的多个不同区域,从岩图像薄截面的第二图像分割多个晶粒,并且确定分段晶粒的特性。

著录项

  • 公开/公告号US11010883B2

    专利类型

  • 公开/公告日2021-05-18

    原文格式PDF

  • 申请/专利权人 SAUDI ARABIAN OIL COMPANY;

    申请/专利号US201815955072

  • 申请日2018-04-17

  • 分类号G06T7;G06T7/90;G06T7/11;G06T7/62;G06K9/62;

  • 国家 US

  • 入库时间 2022-08-24 18:43:12

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