An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking

IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Bowen Deng, Guangui Zou, Suping Peng, Jiasheng She, Chengyang Han, Yanhai Liu
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引用次数: 0

Abstract

This article introduces a novel method for geological fault interpretation utilizing a 2D convolutional neural network approach with a focus on coalbed horizons, dealing with the problem as an image classification task. At the beginning, linear annotations, reflecting geological fault features, are applied to multiple seismic sections. By considering texture differences between fault and non-fault areas, we construct samples that represent these distinct zones for training deep neural networks. Initially, fault annotations are transformed into single dots to facilitate pixel-based processing. To depict a specific dot's geological structure, we employ a matrix clipped around the point, determined by a combination of range and step parameters. Convolutional layers generate filters equivalent to seismic data transformation, streamlining the need for analysis and selection of seismic attributes. The article discusses enhancing the efficiency of 2D convolutional neural network–based fault interpretation by optimizing sample selection, data extraction and model construction procedures. Through the incorporation of data from two mining areas (totalling 27.09 km2) in sample creation, the overall accuracy exceeds 0.99. Recognition extends seamlessly to unlabelled sections, showcasing the innovative technical route and methodology of fault interpretation with linear annotation and pixel-based thinking. This study presents a method that integrates planar and raster thinking, transitioning from vision-oriented geological structure annotation to algorithm-oriented pixel location. The proposed 2D convolutional neural network–based matrix-oriented fault/non-fault binary classification demonstrates feasibility and reproducibility, offering a new automated approach for fault detection in coalbeds through convolutional neural network algorithms.

基于线性标注和像素思维的二维卷积神经网络地震数据断层解释方法
本文介绍了一种利用二维卷积神经网络方法进行地质断层解释的新方法,重点关注煤层地层,将该问题作为图像分类任务来处理。首先,将反映地质断层特征的线性注释应用于多个地震剖面。通过考虑断层区域和非断层区域之间的纹理差异,我们构建了代表这些不同区域的样本,用于训练深度神经网络。最初,断层注释被转换成单个点,以方便基于像素的处理。为了描述特定点的地质结构,我们采用了一个围绕点剪切的矩阵,该矩阵由范围和步长参数组合决定。卷积层生成的滤波器相当于地震数据转换,从而简化了地震属性的分析和选择。文章讨论了通过优化样本选择、数据提取和模型构建程序,提高基于二维卷积神经网络的断层解释效率。通过将两个矿区(总面积 27.09 平方公里)的数据纳入样本创建,总体准确率超过了 0.99。识别范围无缝延伸至未标注的地段,展示了以线性标注和像素思维进行断层解释的创新技术路线和方法。本研究提出了一种融合平面和栅格思维的方法,从以视觉为导向的地质结构标注过渡到以算法为导向的像素定位。所提出的基于矩阵的二维卷积神经网络断层/非断层二元分类法证明了其可行性和可重复性,为通过卷积神经网络算法进行煤层断层探测提供了一种新的自动化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Geophysical Prospecting
Geophysical Prospecting 地学-地球化学与地球物理
CiteScore
4.90
自引率
11.50%
发文量
118
审稿时长
4.5 months
期刊介绍: Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.
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