Published as IEEE Geoscience and Remote Sensing Letters, 14, 18-22, (2017)

Multiple reflections noise attenuation using adaptive randomized-order empirical mode decomposition

Wei Chen% latex2html id marker 1373
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\setcounter{footnote}{2}\fnsymbol{footnote}, Jianyong Xie% latex2html id marker 1376
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\setcounter{footnote}{4}\fnsymbol{footnote}, Shaohuan Zu% latex2html id marker 1378
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\setcounter{footnote}{5}\fnsymbol{footnote}, and Shuwei Gan% latex2html id marker 1380
\setcounter{footnote}{3}\fnsymbol{footnote}, and Yangkang Chen% latex2html id marker 1381
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\setcounter{footnote}{1}\fnsymbol{footnote}Key Laboratory of Exploration Technology for Oil and Gas Resources of Ministry of Education, Yangtze University, Wuhan, Hubei, 430100, China
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\setcounter{footnote}{2}\fnsymbol{footnote}Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Wuhan, Hubei 430100, China, Email:
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\setcounter{footnote}{3}\fnsymbol{footnote}State Key Laboratory of Petroleum Resources and Prospecting
China University of Petroleum
Fuxue Road 18th
Beijing, China, 102200
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\setcounter{footnote}{4}\fnsymbol{footnote}Department of Physics, University of Alberta, Edmonton Alberta, T6G 2E1, Canada, Emails:
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\setcounter{footnote}{5}\fnsymbol{footnote}Modeling and Imaging Laboratory
Earth and Planetary Sciences
University of California
Santa Cruz, CA 95064
School of Earth Sciences
Zhejiang University
Hangzhou, Zhejiang Province, China, 310027


We propose a novel approach for removing multiple reflections noise based on an adaptive randomized-order empirical mode decomposition framework. We first flatten the primary reflections in common midpoint (CMP) gather using the automatically picked NMO velocities that correspond to the primary reflections and then randomly permutate all the traces. Next, we removed the spatially distributed random spikes that correspond to the multiple reflections using the EMD based smoothing approach that is implemented in the $f-x$ domain. The trace randomization approach can make the spatially coherent multiple reflections random along the space direction and can decrease the coherency of near-offset multiple reflections. The EMD based smoothing method is superior to median filter and prediction error filter in that it can help preserve the flattened signals better, without the need of exact flattening, and can preserve the amplitude variation much better. In addition, EMD is a fully adaptive algorithm and the parameterization for EMD based smoothing can be very convenient.