基于深度神经网络的岩性分割

J. Lin, E. Haber
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引用次数: 0

摘要

本文将岩性分割任务置于计算机视觉的框架中,避免了传统方法在岩性分割任务中的困难。首先,我们建立了一个包含配对拓扑、卫星和岩性图像的岩性数据集;其次,介绍了HyperNet和UNet两种热神经网络,并将其应用于岩性分割任务中。实验结果表明,HyperNet和UNet在岩性分割中都是有效的,具有广阔的应用前景。神经网络预测精度比随机猜测提高3倍,大大减轻了专业岩性地质工作者的工作量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lithology segmentation using deep neural network
Summary This paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision. First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task. The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation. % Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.
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