气候变化领域集成服务门户(CCPortal): Deep Learning for Characterizing Paleokarst Collapse Features in 3-D Seismic Images

CCPortal

DOI	10.1029/2020JB019685
	Deep Learning for Characterizing Paleokarst Collapse Features in 3-D Seismic Images
	Wu X.; Yan S.; Qi J.; Zeng H.
发表日期	2020
ISSN	21699313
卷号	125 期号:9
英文摘要	Paleokarst systems are found extensively in carbonate-prone basins worldwide. They can form large reservoirs and provide efficient pathways for hydrocarbon migration, but they can also create serious engineering geohazards. The full delineation of potentially buried paleokarst systems plays an important role for reservoir characterization, oil and gas production, and other engineering tasks. We propose a supervised convolutional neural network (CNN) to automatically and accurately characterize paleokarst and associated collapse features from 3-D seismic images. To avoid time-consuming manual labeling for training the CNN, we propose an efficient workflow to automatically generate numerous 3-D training image pairs including synthetic seismic images and the corresponding label images of the collapsed paleokarst features simulated in the seismic images. With this workflow, we are able to simulate realistic and diverse geologic structures and collapsed paleokarst features in the training images from which the CNN can effectively learn to recognize the collapsed paleokarst features in real field seismic images. Two field examples from the Fort Worth Basin demonstrate that our CNN-based method is superior to conventional automatic methods in delineating paleokarst collapse features from seismic images. From the CNN-based paleokarst characterization, we can further automatically extract 3-D collapsed paleokarst systems and quantitatively measure their geometric parameters. Our CNN-based method is highly efficient and takes only seconds to classify collapsed paleokarst features a 3-D seismic image with 320 × 1, 024 × 1, 024 samples (approximately 268 km2) by using one graphics processing unit. ©2020. American Geophysical Union. All Rights Reserved.
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187613
作者单位	School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China; The University of Oklahoma, NormanOK, United States; Bureau of Economic Geology, University of Texas at Austin, Austin, TX, United States
推荐引用方式 GB/T 7714	Wu X.,Yan S.,Qi J.,et al. Deep Learning for Characterizing Paleokarst Collapse Features in 3-D Seismic Images[J],2020,125(9).
APA	Wu X.,Yan S.,Qi J.,&Zeng H..(2020).Deep Learning for Characterizing Paleokarst Collapse Features in 3-D Seismic Images.Journal of Geophysical Research: Solid Earth,125(9).
MLA	Wu X.,et al."Deep Learning for Characterizing Paleokarst Collapse Features in 3-D Seismic Images".Journal of Geophysical Research: Solid Earth 125.9(2020).