Tomographic inversion of near-surface Q factor by combining surface and cross-hole seismic surveys

The estimation of the quality factor Q plays a fundamental role in enhancing seismic resolution via absorption compensation in the near-surface layer.We present a new geometry that can be used to acquire field data by combining surface and cross-hole surveys to decrease the effect of geophone coupling on Q estimation.In this study,we drilled number of receiver holes around the source hole,each hole has different depth and each geophone is placed geophones into the bottom of each receiver hole to avoid the effect of geophone coupling with the borehole wall on Q estimation in conventional cross-hole seismic surveys.We also propose a novel tomographic inversion of the Q factor without the effect of the source signature,and examine its stability and reliability using synthetic data.We estimate the Q factors of the near-surface layer in two different frequency bands using field data acquired in the Dagang Oilfield.The results show that seismic absorption in the nearsurface layer is much greater than that in the subsurface strata.Thus,it is of critical practical importance to enhance the seismic solution by compensating for near-surface absorption.In addition,we derive different Q factors from two frequency bands,which can be treated,to some extent,as evidence of a frequency-dependent Q.

Wave equation tomographic velocity inversion method based on the Born/Rytov approximation

This paper discusses Born/Rytov approximation tomographic velocity inversion methods constrained by the Fresnel zone. Calculations of the sensitivity kernel function and traveltime residuals are critical in tomographic velocity inversion. Based on the Bom/Rytov approximation of the frequency-domain wave equation, we derive the traveltime sensitivity kemels of the wave equation on the band-limited wave field and simultaneously obtain the traveltime residuals based on the Rytov approximation. In contrast to single-ray tomography, the modified velocity inversion method improves the inversion stability. Tests of the near- surface velocity model and field data prove that the proposed method has higher accuracy and Computational efficiency than ray theory tomography and full waveform inversion methods.

Tomographic velocity inversion for ADCIGs in areas with a rugged surface

Pre-stack depth migration velocity analysis is one of the key techniques influencing image quality. As for areas with a rugged surface and complex subsurface, conventional prestack depth migration velocity analysis corrects the rugged surface to a known datum or designed surface velocity model on which to perform migration and update the velocity. We propose a rugged surface tomographic velocity inversion method based on angle-domain common image gathers by which the velocity field can be updated directly from the rugged surface without static correction for pre-stack data and improve inversion precision and efficiency. First, we introduce a method to acquire angle-domain common image gathers （ADCIGs） in rugged surface areas and then perform rugged surface tornographic velocity inversion. Tests with model and field data prove the method to be correct and effective.

Lithospheric Structure Model of Central Indian Ocean Basin Using Ocean Bottom Seismometer Data

The intense deformation zone in the central Indian Ocean, south of Indian continent is one of the most complex regions in terms of its structure and geodynamics. The deformation zone has been studied and debated in 1990s for its genesis. It was argued that deformation is mainly confined to sedimentary and oceanic crustal layers, while the large wave length geoidal anomalies, on which the deformation region lies, called for deeper sources. The inter connection between deeper and the shallower sources is found missing. The current study focuses on the complexities of this region by analyzing OBS （ocean bottom seismometer） data. The data acquired by five OBS systems along a 300 km long south-north profile in the CIOB （central Indian Ocean basin） have been modeled and the crustal and sub-crustal structure has been determined using 2-D tomographic inversion. Four subsurface layers are identified representing the sediment column, upper crustal layer, lower crustal layer and a sub-crustal layer （upper mantle layer）. A considerable variation in thickness as well as velocity at all interfaces from sedimentary column to upper mantle is observed which indicates that the tectonic forces have affected the entire crust and sub-crustal configuration. The sediments are characterized by higher velocities （2.1 kin/s） due to the increased confining pressure. Modeling results indicated that the velocity in upper crust is in the range of 5.7-6.2 km/s and the velocity of the lower crust varies from 7.0-7.6 km/s. The velocity of the sub-crustal layer is in the range of 7.8-8.4 km/s. This high-velocity layer is interpreted as magmatic under-plating with strong lateral variations. The base of the 7.0 km/s layer at 12-15 km depth is interpreted as the Moho.

Reflection full waveform inversion

Because of the combination of optimization algorithms and full wave equations, full-waveform inversion(FWI) has become the frontier of the study of seismic exploration and is gradually becoming one of the essential tools for obtaining the Earth interior information. However, the application of conventional FWI to pure reflection data in the absence of a highly accurate starting velocity model is difficult. Compared to other types of seismic waves, reflections carry the information of the deep part of the subsurface. Reflection FWI, therefore, is able to improve the accuracy of imaging the Earth interior further. Here, we demonstrate a means of achieving this successfully by interleaving least-squares RTM with a version of reflection FWI in which the tomographic gradient that is required to update the background macro-model is separated from the reflectivity gradient using the Born approximation during forward modeling. This provides a good update to the macro-model. This approach is then followed by conventional FWI to obtain a final high-fidelity high-resolution result from a poor starting model using only reflection data.Further analysis reveals the high-resolution result is achieved due to a deconvolution imaging condition implicitly used by FWI.