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For seismic exploration (i.e. reservoir geophysics), retrieval of body waves appears promising, especially at low frequencies (below 3 Hz) where seismic vibrators reach a limit. These low frequencies are of interest for velocity model building (Baeten et al. 2013) and for broadband seismic inversion (Kneller et al. 2014). Here we demonstrate an example of body wave retrieval using ambient noise correlation in the context of seismic monitoring. The result of the ambient noise correlation is correctly arranged to produce noise-induced shot point gathers at each sensor positions. The noise-induced migrated image is compared and then combined with the image obtained by migrating the conventional active shot point gathers generated by vibroseismic sources (Cotton et al, 2014). In so doing, we considerably broaden the image spectrum toward the low frequencies.

Combined with recent receiver deghosting strategies, the use of multi-level sources can provide further uplift to the ever broadening bandwidth of seismic data. While multi-level sources help mitigate source notches in the output spectrum, the resulting emitted wavelet still exhibits residual ghosts, directivity, and bubble energy which must be handled in processing. We highlight the compatibility of Ziolkowski’s notional source method with multi-level source acquisition. We continue by showing how the directional signatures may be used for 3D directional designature and deghosting on shallow water towed streamer data. The results show a significant improvement in the level of ringing relating to source wavelet directivity effects.

Recently, a lot of efforts has been done in seismology and geophysics to track and monitor sub-surface property variations such as velocity, fluid pressure or saturation. In 4D monitoring, the two main observables are velocity and amplitude variations. For a given reflection, variations are classically monitored using wavelet travel time and amplitude. Today, combined travel time and amplitude variations measures are possible. In this paper, we present an array processing method to measure new observables linked with the velocity variations in 4D monitoring such as the slowness at both the source and the receiver sides.

The use of broadband data in reservoir characterization has proven advantages. However, the whole workflow should be reviewed and renewed in order to get all the advantages of broadband data.

Reliable low frequency content and phase alignment are critical for broadband seismic inversion and the prediction of reservoir properties. However, currently there is a lack of tools for ultra-low frequency (less than 5 Hz) quality and phase assessment. A focusing metric in the impedance domain is proposed to assess the ultra-low frequency phase alignment. The method has been applied on a real broadband dataset for the assessment of the residual phase correction process and the conclusion is validated by using well information.

Parameterization lies at the center of anisotropic full-waveform inversion (FWI) with multiparameter updates. This is because FWI aims to update the long and short wavelengths of the perturbations. Recently, there has been an intensive effort to determine the optimal parameterization, centering the fundamental discussion mainly on the analysis of radiation patterns for each one of these parameterizations, and aiming to determine which is best suited for multiparameter inversion. We have developed a new parameterization in the scope of FWI, based on the concept of kinematically equivalent media, as originally proposed in other areas of seismic data analysis. The radiation pattern reveals that this parameterization combines some of the characteristics of parameterizations with one velocity and two Thomsen's parameters and parameterizations using two velocities and one Thomsen's parameter. The study of perturbation of traveltime with perturbation of model parameters shows that the new parameterization is less ambiguous when relating these quantities in comparison with other more commonly used parameterizations.

The aim of this study is to discuss the challenges for interbed multiple attenuation in the Asia-Pacific region. Many possible multiple generators such as seafloor, carbonate layer or volcanic flow are found in the data. Without any prior subsurface knowledge, the ISS method is used in our study to estimate all the interbed multiples . In order to obtain a high resolution multiple model, some data preparation can be required such as inverting near offset data for shallow water or broadband processing for better separation of thin layers. If the survey has strong crossline dip, 3D prediction is necessary to take in account the out-of-plane components. Through case studies from offshore Australia and offshore Vietnam, we demonstrate strategies for effective interbed multiple attenuation using the ISS method.

By adapting reverse time migration (RTM) and demigration as the migration and modeling operators to maximize the crosscorrelation between the simulated and the acquired seismic data, we introduced a new practical least-squares RTM (LSRTM) scheme and derived a steepest descent method in seeking the optimal image. Through synthetic and real data experiments, we determined that the proposed LSRTM provided high-quality images with balanced amplitudes, improved focusing, and enhanced resolution. The method was also capable of removing free surface ghosts caused by towed streamer acquisition, filling the structures and reducing crosstalk noise associated with simultaneous shooting.

Pre-stack elastic inversion requires a reliable wavelet model. This wavelet model must take into account the angle dependency of the wavelet, as well as the time dependency. This is even more important for broadband data, as the wavelets exhibit more variations, due to the fact that the maximum frequency is very high for small time and angles, but much smaller for large time and angle. We show how a continuously varying wavelet model can be estimated through a Bayesian inversion. This wavelet model can be used to pre-process the gathers and provide a zero-phase wavelet model for pre-stack elatic inversion. Synthetic and real data examples are shown to support this.