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Seismological Research Letters

Research Article| May 22, 2019 Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning Yangkang Chen; Yangkang Chen aSchool of Earth Sciences, Zhejiang University, Number 866, Yuhangtang Road, Xihu District, Hangzhou 310027, Zhejiang Province, China, yangkang.chen@zju.edu.cn Search for other works by this author on: GSW Google Scholar Mi Zhang; Mi Zhang bState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 18 Fuxue Road, Beijing 102200, China, cupmi@sina.com Search for other works by this author on: GSW Google Scholar Min Bai; Min Bai aSchool of Earth Sciences, Zhejiang University, Number 866, Yuhangtang Road, Xihu District, Hangzhou 310027, Zhejiang Province, China, yangkang.chen@zju.edu.cn Search for other works by this author on: GSW Google Scholar Wei Chen Wei Chen Corresponding Author cKey Laboratory of Exploration Technology for Oil, and Gas Resources of Ministry of Education, Yangtze University, Number 111, Daxue Road, Caidian District, Wuhan 430100, China, chenwei2014@yangtzeu.edu.cndAlso at Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Number 111, Daxue Road, Caidian District, Wuhan 430100, China. Search for other works by this author on: GSW Google Scholar Author and Article Information Yangkang Chen aSchool of Earth Sciences, Zhejiang University, Number 866, Yuhangtang Road, Xihu District, Hangzhou 310027, Zhejiang Province, China, yangkang.chen@zju.edu.cn Mi Zhang bState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, 18 Fuxue Road, Beijing 102200, China, cupmi@sina.com Min Bai aSchool of Earth Sciences, Zhejiang University, Number 866, Yuhangtang Road, Xihu District, Hangzhou 310027, Zhejiang Province, China, yangkang.chen@zju.edu.cn Wei Chen Corresponding Author cKey Laboratory of Exploration Technology for Oil, and Gas Resources of Ministry of Education, Yangtze University, Number 111, Daxue Road, Caidian District, Wuhan 430100, China, chenwei2014@yangtzeu.edu.cndAlso at Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Number 111, Daxue Road, Caidian District, Wuhan 430100, China. Publisher: Seismological Society of America First Online: 22 May 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (4): 1552–1564. https://doi.org/10.1785/0220190028 Article history First Online: 22 May 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Yangkang Chen, Mi Zhang, Min Bai, Wei Chen; Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning. Seismological Research Letters 2019;; 90 (4): 1552–1564. doi: https://doi.org/10.1785/0220190028 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon can be characterized as a low signal‐to‐noise ratio (SNR). The low SNR of the seismic data may lower the quality of many subsequent seismological analyses, such as inversion and imaging. Thus, the removal of unwanted seismic noise has significant importance. In this article, we intend to improve the SNR of many seismological datasets by developing new denoising framework that is based on an unsupervised machine‐learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic‐wave components. To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low‐amplitude neurons in the hidden layer, we introduce a sparsity constraint to the autoencoder neural network. The sparse autoencoder method introduced in this article is effective in attenuating the seismic noise. More importantly, it is capable of preserving subtle features of the data, while removing the spatially incoherent random noise. We apply the proposed denoising framework to a reflection seismic image, depth‐domain receiver function gather, and an earthquake stack dataset. The purpose of this study is to demonstrate the framework’s potential in real‐world applications. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning