相
模式识别(心理学)
地质学
聚类分析
自编码
地震道
人工智能
卷积神经网络
计算机科学
特征提取
深度学习
小波
古生物学
构造盆地
作者
Feng Qian,Miao Yin,Xiao-Yang Liu,Yaojun Wang,Chao Lu,Guangmin Hu
出处
期刊:Geophysics
[Society of Exploration Geophysicists]
日期:2018-05-01
卷期号:83 (3): A39-A43
被引量:118
标识
DOI:10.1190/geo2017-0524.1
摘要
One of the most important goals of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment. Prestack seismic data carry rich information that can help us get higher resolution and more accurate facies maps. Therefore, it is promising to use prestack seismic data for the seismic facies recognition task. However, because each identified object changes from the poststack trace vectors to a prestack trace matrix, effective feature extraction becomes more challenging. We have developed a novel data-driven offset-temporal feature extraction approach using the deep convolutional autoencoder (DCAE). As an unsupervised deep learning method, DCAE learns nonlinear, discriminant, and invariant features from unlabeled data. Then, seismic facies analysis can be accomplished through the use of conventional classification or clustering techniques (e.g., K-means or self-organizing maps). Using a physical model and field prestack seismic surveys, we comprehensively determine the effectiveness of our scheme. Our results indicate that DCAE provides a much higher resolution than the conventional methods and offers the potential to significantly highlight stratigraphic and depositional information.
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