dc.contributor.author Sun, Bingbing dc.contributor.author Alkhalifah, Tariq Ali dc.date.accessioned 2020-02-13T06:53:18Z dc.date.available 2020-02-13T06:53:18Z dc.date.issued 2020-02-11 dc.date.submitted 2019-07-06 dc.identifier.citation Sun, B., & Alkhalifah, T. A. (2020). Joint Minimization of the Mean and Information Entropy of the Matching Filter Distribution for a Robust Misfit Function in Full-Waveform Inversion. IEEE Transactions on Geoscience and Remote Sensing, 58(7), 4704–4720. doi:10.1109/tgrs.2020.2966115 dc.identifier.doi 10.1109/TGRS.2020.2966115 dc.identifier.uri http://hdl.handle.net/10754/661499 dc.description.abstract A full-waveform inversion (FWI) is a highly nonlinear inversion methodology. FWI tends to converge to a local minimum rather than a global one. We refer to this phenomenon as cycle skipping'' in FWI. A cost-effective solution for resolving this issue is to design a more convex misfit function for the optimization problem. A global comparison based on using a matching filter (MF) admits more robust misfit functions. In this case, we would compute an MF first by deconvolving the predicted data from the measured ones. When the velocity model is accurate, the predicted data resemble the measured ones, and the resulting MF would be an approximated Dirac delta function. If the velocity produces data that are different from the observed ones, a misfit function can be formulated by penalizing the energy away from the zero-lag time (the center). Here, we develop a general mechanism for an evolution of the MF to our objective in FWI. Specifically from the statistics point of view, rather than using a penalty, we propose a novel misfit by minimization of the mean and information entropy of the MF distribution. We show that the resulting misfit function can mitigate the cycle skipping'' as well as reduce the mean and variance of the resulting MF distribution. We use a modified Marmousi example to demonstrate the features of the proposed misfit. We also evaluate the robustness of the proposed method using inaccurate (rotation in phase) source wavelets and measured data with different levels of Gaussian random noise. dc.description.sponsorship The work was supported financially by the King Abdullah University of Science and Technology (KAUST) in general. dc.description.sponsorship The authors would like to thank the members of SWAG for useful discussions. dc.publisher Institute of Electrical and Electronics Engineers (IEEE) dc.relation.url https://ieeexplore.ieee.org/document/8994166/ dc.relation.url https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8994166 dc.rights Archived with thanks to IEEE Transactions on Geoscience and Remote Sensing dc.subject Full-waveform inversion (FWI) dc.subject information entropy dc.subject matching filter (MF) dc.subject nonlinear inversion. dc.title Joint Minimization of the Mean and Information Entropy of the Matching Filter Distribution for a Robust Misfit Function in Full-Waveform Inversion dc.type Article dc.contributor.department Earth Science and Engineering Program dc.contributor.department Physical Science and Engineering (PSE) Division dc.contributor.department Seismic Wave Analysis Group dc.identifier.journal IEEE Transactions on Geoscience and Remote Sensing dc.eprint.version Post-print kaust.person Sun, Bingbing kaust.person Alkhalifah, Tariq Ali dc.date.accepted 2019-12-31 refterms.dateFOA 2020-02-13T08:11:58Z dc.date.published-online 2020-02-11 dc.date.published-print 2020-07
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