基于深度学习的地震数据自动层位识别

Harshit Gupta, Siddhant Pradhan, Rahul Gogia, Seshan Srirangarajan, J. Phirani, Sayan Ranu
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引用次数: 0

摘要

地震图像中的层是具有重要地质意义的表面,可用于了解地质结构和地层模型。然而,地震数据中的水平跟踪是一项耗时且具有挑战性的任务。考虑到油气行业决策的时间限制,为地质学家节省地震解释任务的时间至关重要。我们利用深度卷积神经网络(CNN)直接从地震图像中跟踪层位。提出了一种新的地震层位自动跟踪方法,减少了解释时间,提高了地质工作者的解释精度。我们展示了所提出的CNN模型在不同训练数据集大小和不同平衡类的方法下的性能比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Based Automatic Horizon Identification from Seismic Data
Horizons in a seismic image are geologically signficant surfaces that can be used for understanding geological structures and stratigraphy models. However, horizon tracking in seismic data is a time consuming and challenging task. Saving geologist's time from this seismic interpretation task is essential given the time constraints for the decision making in the oil & gas industry. We take advantage of the deep convolutional neural networks (CNN) to track the horizons directly from the seismic images. We propose a novel automatic seismic horizon tracking method that can reduce the time needed for interpretation, as well as increase the accuracy for the geologists. We show the performance comparison of the proposed CNN model for different training data set sizes and different methods of balancing the classes.
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