卷积神经网络
油页岩
人工神经网络
人工智能
地质学
计算机科学
样品(材料)
分割
图像分割
工作流程
模式识别(心理学)
机器学习
数据库
物理
古生物学
热力学
作者
David Tang,Kyle Spikes
出处
期刊:Seg Technical Program Expanded Abstracts
[Society of Exploration Geophysicists]
日期:2017-08-17
被引量:19
标识
DOI:10.1190/segam2017-17738502.1
摘要
The segmentation procedure in a digital rock physics workflow is often very challenging and time consuming. Here, we present an alternative method for quickly segmenting digital rock physics images that utilizes machine learning. Elemental SEM images of a core sample serve as inputs into a neural network. The network then outputs the probability of a pixel belonging to a certain class. Segmentation is implemented by choosing the class with the highest probability. This process allows for the uncertainty to be quantified in mineral phase identification. After training the algorithm, the rest of the image and subsequent images can be quickly segmented. We demonstrate the segmentation process on a shale sample with six different phases. Presentation Date: Wednesday, September 27, 2017 Start Time: 3:05 PM Location: Exhibit Hall C, E-P Station 4 Presentation Type: EPOSTER
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