摘要
在地质勘探与地震信号处理中,岩性分类是一个最基本的问题。然而,由于实际的岩性分类涉及到各种复杂的因素与环节,使得传统的统计和机器学习方法难于得到满意的分类准确率,无法在实际应用中进行有效的岩性识别。为了有效地解决这一问题,本文依据测井曲线数据提出了一种基于深度卷积自编码器的神经网络模型及其相应的参数学习算法,来实现有效的岩性分类与识别,并采用游程平滑算法对分类结果中孤立点进行剔除,进一步改善岩性分类的效果。实验结果表明,即使在少量的测井曲线标注样本条件下,本文所提出的深度学习模型也能够显著地提高了岩性分类的准确率,能够达到实际应用的要求。
Lithologic classification and recognition is the most basic problem for geological exploration and seismic signal processing. However, it is rather complicated with a variety of factors so that statistical and conventional machine learning models cannot achieve a satisfactory accuracy of lithologic classification, and therefore the obtained classifiers cannot be effectively applied to the lithologic recognition in practical applications. In order to overcome this difficulty, this paper proposes a deep convolutional auto-encoder neural network as well as its parameter learning algorithm for lithologic classification. By adopting the run length smoothing algorithm(RLSA) to remove the isolated points in the classification result of the network on the input data series, our proposed deep learning model can make the lithologic classification more effectively. It is demonstrated by the experiments that the accuracy of lithologic classification of our proposed model is remarkably increased to a practically applicable level, even if there is only a small number of tag lithologic samples in well logging.
作者
张树义
王波
马尽文
ZHANG Shuyi;WANG Bo;MA Jinwen(School of Mathematical Sciences and LMAM,Peking University,Beijing 100871,China)
出处
《信号处理》
CSCD
北大核心
2023年第1期11-19,共9页
Journal of Signal Processing
基金
国家重点研发计划课题(2018AAA0100205)资助。
关键词
岩性分类
深度学习
卷积自编码器
游程平滑算法
lithologic classification
deep learning
convolutional auto-encoder
run length smoothing algorithm
