Simultaneous multifrequency inversion of full-waveform seismic data

Hu, Wenyi; Abubakar, Aria; Habashy, Tarek M.

doi:10.1190/1.3073002

Cited by 138 publications

(69 citation statements)

References 49 publications

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“…Note that the FD operator H b needs to be inverted only once since the background medium is not changing throughout the inversion process. In the 2D case our frequency-domain finite difference (FDFD) code (see Hu et al [19]) employs a LU decomposition technique, hence, the cost of calculating L b operating on a function is relatively cheap since the LU decomposition is done only once and then stored to be used in each step of the inversion process. In order to set up the FD-CSI method we define the data error to be…”

Section: The Fd-csi Methodsmentioning

confidence: 99%

A finite-difference contrast source inversion method

et al. 2008

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We present a contrast source inversion (CSI) algorithm using a finite-difference (FD) approach as its backbone for reconstructing the unknown material properties of inhomogeneous objects embedded in a known inhomogeneous background medium. Unlike the CSI method using the integral equation (IE) approach, the FD-CSI method can readily employ an arbitrary inhomogeneous medium as its background. The ability to use an inhomogeneous background medium has made this algorithm very suitable to be used in through-wall imaging and time-lapse inversion applications. Similar to the IE-CSI algorithm the unknown contrast sources and contrast function are updated alternately to reconstruct the unknown objects without requiring the solution of the full forward problem at each iteration step in the optimization process. The FD solver is formulated in the frequency domain and it is equipped with a perfectly matched layer (PML) absorbing boundary condition. The FD operator used in the FD-CSI method is only dependent on the background medium and the frequency of operation, thus it does not change throughout the inversion process. Therefore, at least for the two-dimensional (2D) configurations, where the size of the stiffness matrix is manageable, the FD stiffness matrix can be inverted using a non-iterative inversion matrix approach such as a Gauss elimination method for the sparse matrix. In this case, an LU decomposition needs to be done only once and can then be reused for multiple source positions and in successive iterations of the inversion. Numerical experiments show that this FD-CSI algorithm has an excellent performance for inverting inhomogeneous objects embedded in an inhomogeneous background medium.

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Section: The Fd-csi Methodsmentioning

confidence: 99%

A finite-difference contrast source inversion method

et al. 2008

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“…Including more details in the baseline model improves the recovery of the amplitude changes. In practice, when we need to make a judgment on the accuracy of the baseline model is, seismic measures (e.g., gather flatness and data fitting) and prior information (well logs and interpretations) should be used as constraints (Hu et al, 2009;Asnaashari et al, 2013). Within a wide range of convergence levels for baseline inversion (Figure 11), DDWI is robust and capable of delivering reliable results.…”

Section: Baseline Model Dependencementioning

confidence: 99%

Double-difference waveform inversion: Feasibility and robustness study with pressure data

et al. 2015

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Time-lapse seismic data are widely used to monitor reservoir changes. Qualitative comparisons between baseline and monitor data sets or image volumes provide information about fluid and pressure effects within the reservoir during production. However, to perform real quantitative analysis of such reservoir changes, quantitative estimates of the elastic parameters are required as input parameters to rock-physics-based reservoir models. Full-waveform inversion has been proposed as a potential tool for retrieving subsurface properties, such as P-and S-wave velocities and density by fitting simulated waveforms to seismic data. An extension of this method to time-lapse applications seems straightforward, but, in fact, it requires more tailored processes such as double-difference waveform inversion (DDWI). We used realistic 2D synthetic pressure data examples to compare the performance of DDWI with that of two other inversion schemes: one using the same starting model for both inversions and the other starting the monitor inversion with the final baseline inversion model. The data simulation and inversion were based on acoustic theory. Although P-wave velocity changes were reliably recovered by each inversion method, DDWI was found to deliver the best results when perfectly repeated surveys were used. However, differencing the baseline and monitor data sets, as required by DDWI, could be found to be sensitive to the presence of survey nonrepeatability. To investigate the feasibility of using DDWI in practice, the dependence of DDWI on the quality of the baseline models and its robustness to survey nonrepeatability were studied with numerical tests. Various types of nonrepeatability were considered separately in the synthetic tests, including random noise, acquisition geometry mismatch, source wavelet discrepancy, and overburden velocity changes. A study of the correlation between the levels and types of nonrepeatability and the resulting contamination of the inversion results found that, for pressure data, DDWI was capable of inverting reliably for P-wave velocity changes under realistic survey nonrepeatability conditions.

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“…For more details about our inversion algorithm implementation, please see Hu et al (2009Hu et al ( , 2011.…”

Section: Formulationmentioning

confidence: 99%

“…This can be done through preconditioning (Operto et al (2006)) or regularization in the inversion process. The application of regularization scheme to FWI has been studied in works by Hu et al (2009);Ramírez and Lewis (2010); Guitton (2011); Asnaashari et al (2013). Usually in acoustic and/or elastic FWI, the sensitivity of the mass density to the measurement is weak, compared to the sensitivity of the P-wave velocity to the measurement data.…”

Section: Introductionmentioning

confidence: 99%

Structural similarity regularization scheme for multiparameter seismic full waveform inversion

Liang

Abubakar

et al. 2013

SEG Technical Program Expanded Abstracts 2013

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SUMMARYWe introduce a new regularization scheme for multiparameter seismic full-waveform inversion (FWI). Using this scheme, we can constrain spatial variations of parameters which are having a weak sensitivity with the one that having a good sensitivity to the measurement, assuming that these parameters have similarities in their structures. In seismic FWI, we apply this scheme when inverting the P-wave velocity and mass density simultaneously. Results from numerical tests show that this scheme may significantly improve the reconstruction of the mass density. Since we obtain an improved mass-density distribution, the inverted P-wave velocity is also enhanced. Hence, we also obtain a better data fit. As numerical examples we show inversions of both vertical seismic profiling (VSP) and surface seismic measurements.

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Simultaneous multifrequency inversion of full-waveform seismic data

Cited by 138 publications

References 49 publications

A finite-difference contrast source inversion method

A finite-difference contrast source inversion method

Double-difference waveform inversion: Feasibility and robustness study with pressure data

Structural similarity regularization scheme for multiparameter seismic full waveform inversion

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