Matches in SemOpenAlex for { <https://semopenalex.org/work/W2945294778> ?p ?o ?g. }
- W2945294778 abstract "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." @default.
- W2945294778 created "2019-05-29" @default.
- W2945294778 creator A5018916500 @default.
- W2945294778 creator A5019972279 @default.
- W2945294778 creator A5037374524 @default.
- W2945294778 creator A5069092681 @default.
- W2945294778 date "2019-05-22" @default.
- W2945294778 modified "2023-10-18" @default.
- W2945294778 title "Improving the Signal‐to‐Noise Ratio of Seismological Datasets by Unsupervised Machine Learning" @default.
- W2945294778 cites W1559584481 @default.
- W2945294778 cites W1822012234 @default.
- W2945294778 cites W1965555277 @default.
- W2945294778 cites W1968079322 @default.
- W2945294778 cites W1991265582 @default.
- W2945294778 cites W2021734622 @default.
- W2945294778 cites W2022057731 @default.
- W2945294778 cites W2024551209 @default.
- W2945294778 cites W2025727209 @default.
- W2945294778 cites W2033245860 @default.
- W2945294778 cites W2037105264 @default.
- W2945294778 cites W2055167779 @default.
- W2945294778 cites W2075366799 @default.
- W2945294778 cites W2077294437 @default.
- W2945294778 cites W2118682956 @default.
- W2945294778 cites W2124161683 @default.
- W2945294778 cites W2132454757 @default.
- W2945294778 cites W2150233229 @default.
- W2945294778 cites W2344237992 @default.
- W2945294778 cites W2412205031 @default.
- W2945294778 cites W2416739439 @default.
- W2945294778 cites W2579302569 @default.
- W2945294778 cites W2602807606 @default.
- W2945294778 cites W2623614429 @default.
- W2945294778 cites W2741984561 @default.
- W2945294778 cites W2766140804 @default.
- W2945294778 cites W2793015978 @default.
- W2945294778 cites W2802174336 @default.
- W2945294778 cites W2888525203 @default.
- W2945294778 cites W2896556550 @default.
- W2945294778 cites W2903512683 @default.
- W2945294778 cites W2908856853 @default.
- W2945294778 cites W2913447624 @default.
- W2945294778 cites W2914025801 @default.
- W2945294778 cites W4242972815 @default.
- W2945294778 cites W4244300305 @default.
- W2945294778 doi "https://doi.org/10.1785/0220190028" @default.
- W2945294778 hasPublicationYear "2019" @default.
- W2945294778 type Work @default.
- W2945294778 sameAs 2945294778 @default.
- W2945294778 citedByCount "42" @default.
- W2945294778 countsByYear W29452947782019 @default.
- W2945294778 countsByYear W29452947782020 @default.
- W2945294778 countsByYear W29452947782021 @default.
- W2945294778 countsByYear W29452947782022 @default.
- W2945294778 countsByYear W29452947782023 @default.
- W2945294778 crossrefType "journal-article" @default.
- W2945294778 hasAuthorship W2945294778A5018916500 @default.
- W2945294778 hasAuthorship W2945294778A5019972279 @default.
- W2945294778 hasAuthorship W2945294778A5037374524 @default.
- W2945294778 hasAuthorship W2945294778A5069092681 @default.
- W2945294778 hasConcept C127313418 @default.
- W2945294778 hasConcept C151730666 @default.
- W2945294778 hasConcept C154945302 @default.
- W2945294778 hasConcept C161191863 @default.
- W2945294778 hasConcept C16674752 @default.
- W2945294778 hasConcept C166957645 @default.
- W2945294778 hasConcept C175181221 @default.
- W2945294778 hasConcept C17744445 @default.
- W2945294778 hasConcept C191935318 @default.
- W2945294778 hasConcept C199539241 @default.
- W2945294778 hasConcept C205649164 @default.
- W2945294778 hasConcept C2776085556 @default.
- W2945294778 hasConcept C2777045944 @default.
- W2945294778 hasConcept C2778304055 @default.
- W2945294778 hasConcept C41008148 @default.
- W2945294778 hasConcept C521751864 @default.
- W2945294778 hasConcept C548895740 @default.
- W2945294778 hasConceptScore W2945294778C127313418 @default.
- W2945294778 hasConceptScore W2945294778C151730666 @default.
- W2945294778 hasConceptScore W2945294778C154945302 @default.
- W2945294778 hasConceptScore W2945294778C161191863 @default.
- W2945294778 hasConceptScore W2945294778C16674752 @default.
- W2945294778 hasConceptScore W2945294778C166957645 @default.
- W2945294778 hasConceptScore W2945294778C175181221 @default.
- W2945294778 hasConceptScore W2945294778C17744445 @default.
- W2945294778 hasConceptScore W2945294778C191935318 @default.
- W2945294778 hasConceptScore W2945294778C199539241 @default.
- W2945294778 hasConceptScore W2945294778C205649164 @default.
- W2945294778 hasConceptScore W2945294778C2776085556 @default.
- W2945294778 hasConceptScore W2945294778C2777045944 @default.
- W2945294778 hasConceptScore W2945294778C2778304055 @default.
- W2945294778 hasConceptScore W2945294778C41008148 @default.
- W2945294778 hasConceptScore W2945294778C521751864 @default.
- W2945294778 hasConceptScore W2945294778C548895740 @default.
- W2945294778 hasLocation W29452947781 @default.
- W2945294778 hasOpenAccess W2945294778 @default.
- W2945294778 hasPrimaryLocation W29452947781 @default.
- W2945294778 hasRelatedWork W114747801 @default.
- W2945294778 hasRelatedWork W2046706951 @default.
- W2945294778 hasRelatedWork W2136623835 @default.