{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:04:41Z","timestamp":1760151881683,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42130805","42074151","42004106","YDZJ202101ZYTS020","QT202116"],"award-info":[{"award-number":["42130805","42074151","42004106","YDZJ202101ZYTS020","QT202116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jilin Province","award":["42130805","42074151","42004106","YDZJ202101ZYTS020","QT202116"],"award-info":[{"award-number":["42130805","42074151","42004106","YDZJ202101ZYTS020","QT202116"]}]},{"name":"Young Science and Technology Talents of Jilin Province","award":["42130805","42074151","42004106","YDZJ202101ZYTS020","QT202116"],"award-info":[{"award-number":["42130805","42074151","42004106","YDZJ202101ZYTS020","QT202116"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In conventional passive seismic exploration, it is often necessary to make a long-period seismic record. On the one hand, the passive seismic records with long period allowed us to screen several good passive seismic records with long period for seismic interferometry reconstruction and perform piecewise stacking on them. On the other hand, a sufficiently long recording time can help us avoid noise interference generated by nonpassive sources during the recording process, such as animal activities, construction operations, industrial electrical interference, etc. Compared with the passive seismic records with short period, the passive seismic records with long period can obtain higher signal-to-noise ratio after seismic interferometry reconstruction. However, they also cause huge consumptions of manpower, material resources, and time. Based on this, this paper proposes a seismic interferometry reconstruction method using passive signals of short-period recordings. Based on deep learning technology, the effective information is extracted and enhanced, the strong coherent noise after reconstruction is suppressed and weakened, the SNR of reconstructed recording is improved, and the effective information is mined. It can effectively reduce the time of passive seismic recording required for acquisition and improve acquisition efficiency. In addition, it also has a certain monitoring effect on real-time changes in underground structures.<\/jats:p>","DOI":"10.3390\/rs14215318","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"5318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Weak Signal Enhancement for Passive Seismic Data Reconstruction Based on Deep Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Binghui","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Liguo","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Pan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Yuchen","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4305-1","DOI":"10.1029\/2006GL028735","article-title":"Retrieval of reflections from seismic background-noise measurements","volume":"34","author":"Draganov","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"A51","DOI":"10.1190\/1.2976118","article-title":"Passive seismic interferometry by multidimensional deconvolution","volume":"73","author":"Wapenaar","year":"2008","journal-title":"Geophysics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"SA61","DOI":"10.1190\/1.3460431","article-title":"Estimation of primaries by sparse inversion from passive seismic data","volume":"75","author":"Verschuur","year":"2010","journal-title":"Geophysics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1093\/gji\/ggu168","article-title":"Receiver-pair seismic interferometry applied to body-wave USArray data","volume":"198","author":"Ruigrok","year":"2014","journal-title":"Geophys. J. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"254301","DOI":"10.1103\/PhysRevLett.93.254301","article-title":"Retrieving the elastodynamic Green's function of an arbitrary inhomogeneous medium by cross correlation","volume":"93","author":"Wapenaar","year":"2004","journal-title":"Phys. Rev. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","first-page":"261","article-title":"PhaseNet: A deep-neural-network-based seismic arrival-time picking method","volume":"216","author":"Zhu","year":"2019","journal-title":"Geophys. J. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5120","DOI":"10.1029\/2017JB015251","article-title":"Wave arrival picking and first-motion polarity determination with deep learning","volume":"123","author":"Ross","year":"2018","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"WA13","DOI":"10.1190\/geo2019-0173.1","article-title":"A convolutional neural network approach to deblending seismic data","volume":"85","author":"Sun","year":"2020","journal-title":"Geophysics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"N17","DOI":"10.1190\/geo2019-0569.1","article-title":"Extracting horizon surfaces from 3D seismic data using deep learning","volume":"85","author":"Tschannen","year":"2020","journal-title":"Geophysics"},{"key":"ref_13","first-page":"2795","article-title":"Application of deep neural networks for multiples atte-nuation","volume":"64","author":"Song","year":"2021","journal-title":"Chin. J. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Petrov, S., Mukerji, T., Zhang, X., and Yan, X. (2022). Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning. Remote Sens., 15.","DOI":"10.3390\/en15031064"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"19304-1","DOI":"10.1029\/2006GL027747","article-title":"Green's function retrieval by cross-correlation in case of one-sided illumination","volume":"33","author":"Wapenaar","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1111\/j.1365-246X.2011.05007.x","article-title":"Seismic interferometry by crosscorrelation and by multidimensional deconvolution: A systematic comparison","volume":"2011 185","author":"Wapenaar","year":"2011","journal-title":"Geophys. J. Int."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"H1","DOI":"10.1190\/geo2010-0039.1","article-title":"Finite-difference modeling experiments for seismic interferometry","volume":"76","author":"Thorbecke","year":"2011","journal-title":"Geophysics"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:01:51Z","timestamp":1760144511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":17,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215318"],"URL":"https:\/\/doi.org\/10.3390\/rs14215318","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,10,24]]}}}