International audienceSimultaneous shooting becomes attractive in seismic data acquisition, especially when a survey has to be acquired in harsh meteorological environment or under strict environmental regulations. Despite the evident time-saving advantage, the simultaneous-source method has a considerable draw-back: the sources interfere with each other creating cross-talk in the data, which leads to significant increase of the processing complexity. Whether a preliminary deblending step (i.e. separation of signals originating from different sources) is necessary or not, remains an open question. In any case, a lot of processing sequences for simultaneous-source data start with deblending. In this paper we propose such method based on identifying coherent features in the data and classifying them according to their source of origin. Following the principle of matrioshka dolls, nested Orthogonal Matching Pursuits (OMP) are used for signal decomposition. Our parametric dictionaries are adapted to seismic data and are progressively constructed during decomposition. The method shows encouraging results on synthetic 2D datasets and is scalable to large datasets of industrial size

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Seismic characterization of source rocks (SRs) became widely used in exploration risk assessment, partly driven by the evaluation of petroleum systems conditioned by the presence of organic-rich SRs. Generally, rock physics combined with seismic amplitude variation with offset (AVO) analysis and inversion of seismic data are used to detect SR presence and to assess SR lateral and vertical variations measured in total organic carbon (TOC) content (in weight percent [wt%]). Despite its great potential, this method suffers from a range of pitfalls and uncertainties. In this study, based on several data sets, we highlight the variability of seismic responses of SRs. From well data, rock property studies of SRs show that the relation between acoustic impedance, which is the product of density and P-wave velocity, and TOC turns out not to be representative in SRs with TOC contents less than approximately 4–5 wt%. In the screening phase of rock-physics data, SR also reveals a large range of Poisson's ratio values, which relates to P-wave and S-wave velocities and has a direct impact on AVO. Moreover, from real seismic data, AVO analysis gave support for this complex behavior, highlighting AVO class I, III, and IV anomalies. Therefore, the expectation that the top of SR intervals would feature a “clear dimming with offset” (AVO class IV) should not be generalized for SR identification, especially in frontier areas lacking nearby well calibration, as suggested by the results of this study.

International audienceSimultaneous-source (or blended) seismic data acquisition allows reducing acquisition time, which is beneficial in harsh meteorological environment or when strict environmental regulations are applied. The only draw-back of blended acquisition is the interference (or cross-talk) between signals originating from different seismic sources firing at the same time. Recent advances in processing and imaging allow acceptable handling of the cross-talk, however, specific processing methods adapted for blended data still need to be improved. Whether the deblending step (or separation of signals originating from different sources) is necessary remains an open question, but it is still included in the beginning of most of the simultaneous-source processing sequences. In this paper, we propose a deblending method based on the decomposition of the blended signal into a set of locally coherent features, or seismic events. The information on the source contained in each seismic event is further used for separation. The decomposition is performed using the Orthogonal Matching Pursuit signal decomposition algorithm with a specific parametric dictionary adapted for seismic events and allowing sparse representation of the data. The method shows promising results on synthetic sets of 2D seismic data and is scalable to large datasets of industrial size