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Estimation of seismic anisotropy with azimuth from sonic data by full waveform inversionGold Open Access

Authors: S. Fuse, H. Mikada and J. Takekawa
Event name: The 22nd International Symposium on Recent Advances in Exploration Geophysics (RAEG 2018)
Session: Geoexploration
Publication date: 24 May 2018
DOI: 10.3997/2352-8265.20140224
Language: English
Info: Extended abstract, PDF ( 785.56Kb )

Summary:
Seismic anisotropy is defined as the directional dependency of seismic phase velocities caused by the anisotropy in the elastic properties of medium. In the past 30 years of research on seismic anisotropy, it has become well known that subsurface materials are more anisotropic than the foreseen. An estimation of anisotropic properties becomes more important for processing seismic data and planning hydraulic fracturing. However, algebraic problems for estimating the anisotropic parameters from seismic data still remain due to the ill-posedness and the instability in the inverse mapping. It is, however, difficult to estimate directly all of 21 independent parameters in the general elastic medium in the 3D Cartesian coordinate system, and a method to deal with seismic anisotropy for complex anisotropic materials has been waited for. In this study, we conduct numerical simulation for transversely isotropic medium (TI) which has 5 independent stiffness elements in 3-dimensional logging model. We propose a new parameterization strategy that minimizes the number of parameters so as to alleviate the ill-posedness and the instability: 7 parameters to express a general anisotropic medium. Instead of the full rank stiffness matrix for general triclinic materials, we assume a transversely isotropic material with horizontal axis of rotation (HTI) as an elastic medium with orientation and dip angles of the axis that becomes an analogue to general elastic medium. We attempt to estimate these parameters by full waveform strategy because azimuthal anisotropy influences the waveform. Since one of the crucial problems of FWI is the predicted model would be possible to converge to local minimum as the number of parameters increases, the small number of unknowns in the proposed strategy could play a key role to deal with complex anisotropy. As a result, all elements come close to true values by full waveform inversion process. Our results suggest that the proposed parameterization strategy and FWI have an advantage over the conventional methods in terms of accuracy and stability.

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