| 基于小波域的FastICA算法的非常规油气藏地震资料去噪 |
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| 引用本文： | 王顾希,郭 思,郭 科,彭 宇.基于小波域的FastICA算法的非常规油气藏地震资料去噪[J].地质通报,2015,34(7):1391-1399. |
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| 作者姓名： | 王顾希 郭 思 郭 科 彭 宇 |
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| 作者单位： | 1.成都理工大学,四川 成都 610059；2.中国测试技术研究院，四川 成都 610021；3.川庆钻探工程有限公司地质勘探开发研究院，四川 成都 610000 |
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| 基金项目： | 国家自然科学基金项目(批准号：41272363)、2013高等学校博士学科点专项（编号：20135122110010） |
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| 摘 要： | 近年来,随着鄂尔多斯盆地苏里格气田开发规模的不断扩大,全数字三维地震得到广泛应用,数字检波器的高灵敏性使得地震资料噪声极其发育。因此,全数字三维地震资料叠前去噪是资料处理的关键。针对全数字三维地震资料的特点,结合盲信号分离技术的最新发展,特别是神经网络技术的最新发展应用到研究中来,建立适合于理论地震记录的盲信号分离的算法模型,对已知反射地震数据实施盲分离技术,将地震信号变换到小波域中,并用Fast ICA算法进行盲分离去噪,然后将去噪后的信号从小波域变换到时间域信号。试验结果表明,该方法得到的去噪效果较时间域内直接去噪效果好。
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| 关 键 词： | 地震资料 三维地震 非常规油气藏 小波变换 随机噪声 阈值函数 |
The application of FastICA algorithm based on wavelet domain to seismic data denoising of unconventional oil and gas reservoirs |
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| WANG Guxi,GUO Si,GUO Ke,PENG Yu.The application of FastICA algorithm based on wavelet domain to seismic data denoising of unconventional oil and gas reservoirs[J].Geologcal Bulletin OF China,2015,34(7):1391-1399. |
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| Authors: | WANG Guxi GUO Si GUO Ke PENG Yu |
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| Institution: | 1. Chengdu University of Technology, Chengdu 610059, Sichuan, China; 2. China Institute of Measurement and Testing Technology, Chengdu 610021, Sichuan, China; 3. CNPC Chuanqing Drilling Engineering Co., Ltd., Chengdu 610000, Sichuan, China |
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| Abstract: | In recent years, with the continuous expansion and development of Sulige gas field in Ordos Basin, the digital 3D seismic data have been widely used, and digital geophone with high sensitivity that causes noises in seismic data is extremely developed. Therefore, the digital 3D seismic data prestack denoising is the key to data processing. In this paper, based on the 3D digital seismic data, in combination with the new development of the blind signal separation technology, especially the latest development and application of the neural network technology, the authors studied and established the blind signal separation algorithm model suitable for theoretical seismograms, performed blind source separation technology for known seismic reflection data, transformed the seismic signal into wavelet domain, used FastICA algorithm to conduct blind separation denoising, and then transformed the denoised signal from the wavelet domain to the time domain. The experimental results show that the denoising result of this method is better than the direct denoising result in the time domain. |
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| Keywords: | seismic data three-dimensional earthquake unconventional oil and gas reservoirs wavelet transform random noise threshold function |
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