基于卷积神经网络和霍夫变换的地震道自动编辑新方法

CNN-based automated trace editing method using Hough transform

地震道编辑是数据预处理中一个繁琐的过程。这可能需要大量的人力和时间成本,特别是对于大型三维数据集。目前的地震道编辑方法通常耗时较长,如果粗略地去除噪声道,可能会遗漏一些潜在的重要信息。在本章的研究中,我们提出了一种基于机器学习的自动编辑地震道的方法。此外,我们还将霍夫变换技术与卷积神经网络(CNN)相结合,以提高方法的可行性。霍夫变换是一种帮助识别图像中异常直线的特征提取方法,我们将其用来预先检测可能存在的噪声道和坏道的位置。然后,利用设计好的带通滤波器和训练好的CNN模型来识别霍夫变换标记的目标区域内真正的噪声道和坏道。在识别之后,应用自动的处理方式来确定经过处理的地震道是否有用或是仍然应该丢弃。整个方案包括四个主要步骤:数据预处理、霍夫变换检测、CNN网络训练和CNN网络预测。在实际数据应用中,准确率达到了98%,表明所提出的自动道编辑方案在实际应用中是有前景的。

Seismic trace editing is a tedious process in data preprocessing that can incur high time costs,especially when handling large 3D datasets.In addition,existing methods to edit seismic traces may miss vital information when killing noisy traces simply.Thus,in this paper,we propose an automated method to edit seismic traces based on machine learning.The proposed method combines the Hough transform technique and a convolutional neural network(CNN)to improve the feasibility of the scheme.The Hough transform is a feature extraction technique that helps identify anomaly lines in images,and we employ it in the proposed method to ascertain the prospective positions of noisy and bad traces.We then implement a bandpass filter and the trained CNN model to identify the precise noisy traces in the target region indicated by the Hough transform process.Upon identification,automated processing is applied to determine whether the processed traces can be useful or should be discarded.This comprehensive framework includes four main steps,i.e.,data preprocessing,Hough transform detection,network training,and network prediction.Experiments conducted on real-world data yielded 98% accuracy,which indicates the potential efficacy of the proposed automated trace editing method in practical applications.