{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T14:10:29Z","timestamp":1789827029969,"version":"4.0.1"},"reference-count":42,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"6","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11971258"],"award-info":[{"award-number":["11971258"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12271291"],"award-info":[{"award-number":["12271291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2021YFA1001300"],"award-info":[{"award-number":["2021YFA1001300"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFA0719200"],"award-info":[{"award-number":["2021YFA0719200"]}]},{"name":"Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Water & Digital Governance Key Project","award":["SKL-IOW-2021TC2101"],"award-info":[{"award-number":["SKL-IOW-2021TC2101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM J. Sci. Comput."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>Abstract.<\/jats:p>\n                  <jats:p>Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface parameter reconstruction. Due to the existence of local minimum traps, the success of the inversion process usually requires a good initial model. Our study primarily focuses on understanding the impact of source wavelets on the landscape of the corresponding optimization problem. We thus introduce a decomposition scheme that divides the inverse problem into two parts. The first step transforms the measured data into data associated with the desired source wavelet. Here, we consider inversions with known and unknown sources to mimic real scenarios. The second subproblem is the conventional FWI, which is much less dependent on an accurate initial model since the previous step improves the misfit landscape. A regularized deconvolution method and a convolutional neural network are employed to solve the source transformation problem. Numerical experiments on the benchmark models demonstrate that our approach improves the gradient\u2019s quality in the subsequent FWI and provides a better inversion performance.<\/jats:p>","DOI":"10.1137\/22m1540612","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T04:47:07Z","timestamp":1700023627000},"page":"B753-B775","source":"Crossref","is-referenced-by-count":3,"title":["Robust Full Waveform Inversion: A Source Wavelet Manipulation Perspective"],"prefix":"10.1137","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1201-1212","authenticated-orcid":true,"given":"Chenglong","family":"Bao","sequence":"first","affiliation":[{"name":"Yau Mathematical Sciences Center, Tsinghua University, and Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2204-7235","authenticated-orcid":true,"given":"Lingyun","family":"Qiu","sequence":"additional","affiliation":[{"name":"Corresponding author. Yau Mathematical Sciences Center, Tsinghua University, and Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9561-0340","authenticated-orcid":true,"given":"Rongqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Yau Mathematical Sciences Center, Tsinghua University, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"F. Billette and S. Brandsberg-Dahl, The 2004 BP velocity benchmark, in 67th EAGE Conference & Exhibition, European Association of Geoscientists & Engineers, 2005, pp. cp\u20131.","DOI":"10.3997\/2214-4609-pdb.1.B035"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1190\/1.3379323"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/0916069"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/83.661187"},{"key":"ref7","first-page":"357","author":"Chen L. C.","year":"2014","journal-title":"Comput. Sci."},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/28\/4\/045001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.4310\/CMS.2014.v12.n5.a7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1190\/1.2967551"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1190\/1.3112572"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"G. Huang and W. W. Symes, Full waveform inversion via matched source extension, in 2015 SEG Annual Meeting, OnePetro, 2015.","DOI":"10.1190\/segam2015-5872566.1"},{"key":"ref14","unstructured":"D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, preprint, arXiv:1412.6980, 2014."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1190\/1.1635054"},{"key":"ref16","unstructured":"I. Loshchilov and F. Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts, preprint, arXiv:1608.03983, 2016."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-12-1165-2019"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11770-014-0453-1"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1190\/tle35121060.1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/32\/11\/115008"},{"key":"ref21","unstructured":"A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, Wavenet: A Generative Model for Raw Audio, preprint, arXiv:1609.03499, 2016."},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jappgeo.2020.103979"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-246X.2006.02978.x"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1190\/1.1444597"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/ac1511"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1051\/0004-6361\/202038552"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"D. Ren, K. Zhang, Q. Wang, Q. Hu, and W. Zuo, Neural blind deconvolution using deep priors, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3341\u20133350.","DOI":"10.1109\/CVPR42600.2020.00340"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23029-5"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2478.2007.00617.x"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1093\/gji\/ggaa196"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"D. Sun, K. Jiao, D. Vigh, and R. Coates, Source wavelet estimation in full waveform inversion, in 76th EAGE Conference and Exhibition 2014, Vol. 2014, EAGE Publications BV, 2014, pp. 1\u20135.","DOI":"10.3997\/2214-4609.20140708"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1190\/geo2019-0195.1"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1190\/segam2020-3424509.1"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1190\/1.1441754"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1190\/1.3238367"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"R. Wang, C. Bao, and L. Qiu, Seismic waveform inversion with source manipulation, in 82nd EAGE Annual Conference & Exhibition, Vol. 2021, EAGE Publications BV, 2021, pp. 1\u20135.","DOI":"10.3997\/2214-4609.202113161"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1190\/segam2016-13879674.1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1190\/geo2013-0294.1"},{"key":"ref39","first-page":"1910","volume":"25","author":"Yan R.","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref40","unstructured":"F. Yu and V. Koltun, Multi-scale Context Aggregation by Dilated Convolutions, preprint, arXiv:1511.07122, 2015."},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"J. Zhang, J. Pan, W.S. Lai, R. W. Lau, and M.H. Yang, Learning fully convolutional networks for iterative non-blind deconvolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3817\u20133825.","DOI":"10.1109\/CVPR.2017.737"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s00024-018-2025-4"}],"container-title":["SIAM Journal on Scientific Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/22M1540612","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T17:20:35Z","timestamp":1787332835000},"score":1,"resource":{"primary":{"URL":"https:\/\/epubs.siam.org\/doi\/10.1137\/22M1540612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12,31]]}},"alternative-id":["10.1137\/22M1540612"],"URL":"https:\/\/doi.org\/10.1137\/22m1540612","relation":{},"ISSN":["1064-8275","1095-7197"],"issn-type":[{"value":"1064-8275","type":"print"},{"value":"1095-7197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]}}}