数据采集与处理2019，Vol.34Issue(2)：288-296,9.DOI:10.16337/j.1004-9037.2019.02.011
基于盲源分离的ICA去噪技术在裂缝预测中的应用
Application of ICA Denoising Based on Blind Source Separation in Fracture Prediction
摘要
Abstract
The random interfering noise contained in seismic record, if not removed properly, will inevitably pose a threat on the fracture development zone prediction accuracy because of greatly disturbing the key edge detection algorithm used in predicting step. Therefore, it is necessary to remove noise from seismic data and improve the quality of original seismic data. In this study, the independent component analysis (ICA) denoising technique, a blind source separation method, is used to decompose the seismic data into different levels of background and target reflection response of reservoir, and effectively make a distinction between effective signal and the random noise, which makes the processing result better than the conventional denoising algorithm. The processing ensures that the signal information basically does not suffer any losses and proffers a better lateral consistency in waveform characteristics. The field results show that, by applying the denoising method to the seismic data before edge detection, a robust fracture prediction result of fracture development zone distribution is achieved corresponding to the regional characteristics of fracture development, and it is in accordance with the drilling results of fracture development characteristics. This study improves the reliability of the fracture prediction of igneous rock zones.关键词
盲源分离/独立分量分析/去噪/裂缝预测/边缘检测Key words
blind sourceseparation/independent component analysis/denoising/fracture prediction/edge detection分类
天文与地球科学引用本文复制引用
王有涛,桂志先..基于盲源分离的ICA去噪技术在裂缝预测中的应用[J].数据采集与处理,2019,34(2):288-296,9.基金项目
国家科技重大专项(2016ZX05002-002)资助项目 （2016ZX05002-002）
