地震相分类的深度学习框架

IF 1.1 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Harpreet Kaur, Nam Pham, Sergey Fomel, Zhicheng Geng, Luke Decker, Ben Gremillion, Michael Jervis, Raymond Abma, Shuang Gao
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引用次数: 2

摘要

我们提出了一种基于深度神经网络的地震相分类框架。我们基于DeepLabv3+和生成对抗网络的架构实现了两种不同的神经网络进行分割,并比较了地震反射数据与岩性相的映射结果。DeepLabv3+预测在预测相之间具有更清晰的边界,而生成对抗网络输出具有更好的预测相连续性。我们使用贝叶斯框架将不确定性分析合并到工作流中。提出的方法包括对来自多个网络的预测相进行联合分析,同时考虑预测中的不确定性,通过减少人工干预的需要,加速了解释过程,也减少了解释器可能带来的个人偏见。通过对现场数据实例的测试,我们确定了所提出算法的有效性,我们发现所提出的工作流能够准确地分类相。这可能使低井密度地区的沉积环境图的开发成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning framework for seismic facies classification
We have proposed a deep neural network-based framework for seismic facies classification. We implement two different neural networks based on the architectures of DeepLabv3+ and generative adversarial network for segmentation and compare the mapping results from seismic reflection data to lithologic facies. DeepLabv3+ predictions have sharper boundaries between the predicted facies whereas generative adversarial network output has a better continuity of predicted facies. We incorporate uncertainty analysis into the workflow using a Bayesian framework. The proposed approach consisting of joint analysis of predicted facies from multiple networks along with uncertainty in prediction accelerates the interpretation process by reducing the need for human intervention and also lessens individual biases that an interpreter may bring. We determine the effectiveness of the proposed algorithm by testing on field data examples, and we find that the proposed workflow classifies facies accurately. This may potentially enable the development of depositional environment maps in areas of low well density.
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来源期刊
CiteScore
2.50
自引率
8.30%
发文量
126
期刊介绍: ***Jointly published by the American Association of Petroleum Geologists (AAPG) and the Society of Exploration Geophysicists (SEG)*** Interpretation is a new, peer-reviewed journal for advancing the practice of subsurface interpretation.
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