Elastic Wave-equation Reflection Traveltime Inversion Using Dynamic Warping and Wave Mode Decomposition
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Earth Science and Engineering Program
KAUST Grant Number2230
Permanent link to this recordhttp://hdl.handle.net/10754/624902
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AbstractElastic full waveform inversion (EFWI) provides high-resolution parameter estimation of the subsurface but requires good initial guess of the true model. The traveltime inversion only minimizes traveltime misfits which are more sensitive and linearly related to the low-wavenumber model perturbation. Therefore, building initial P and S wave velocity models for EFWI by using elastic wave-equation reflections traveltime inversion (WERTI) would be effective and robust, especially for the deeper part. In order to distinguish the reflection travletimes of P or S-waves in elastic media, we decompose the surface multicomponent data into vector P- and S-wave seismogram. We utilize the dynamic image warping to extract the reflected P- or S-wave traveltimes. The P-wave velocity are first inverted using P-wave traveltime followed by the S-wave velocity inversion with S-wave traveltime, during which the wave mode decomposition is applied to the gradients calculation. Synthetic example on the Sigbee2A model proves the validity of our method for recovering the long wavelength components of the model.
CitationWang T, Cheng JB, Guo Q, Wang CL (2017) Elastic Wave-equation Reflection Traveltime Inversion Using Dynamic Warping and Wave Mode Decomposition. 79th EAGE Conference and Exhibition 2017. Available: http://dx.doi.org/10.3997/2214-4609.201701005.
SponsorsThis work is supported by the National Natural Science Foundation of China (NO.41474099, 41674117 & 41630964). This paper is also based upon the work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award NO. 2230. We appreciate the open-source package of DENISE from https://github.com/daniel-koehn/ and Mines Java Toolkit from https://github.com/dhale. We thank the useful advice from Tariq Alkhalifah (KAUST), Zedong Wu (KAUST) and Benxin Chi (Los Alamos).