We propose an innovative workflow based on the complementary use of Rayleigh waves alongside standard P-wave refraction tomography, which better depicts the shallow part of the near-surface P-wave velocity model. Our surface-wave processing sequence led to an S-wave near-surface velocity field that can be used as a constraint for P-wave tomography and can improve P-wave statics determination. Rayleigh waves are processed in three steps. The first step consists of an accurate frequency-dependent traveltime measurement for each selected source-receiver pair in which the phase difference between two adjacent traces is used to derive the phase velocity. Then, a frequency-dependent surface-wave velocity tomography is performed from the picked traveltimes. Finally, after surface-wave tomography, the frequency-dependent phase velocity volume output by the tomography is inverted to deliver an S-wave near-surface velocity model. This model is used to constrain the first-arrival P-wave tomography. To illustrate our method, we use a 3D narrow-azimuth land data set, acquired along a river valley. Strong lateral velocity variations exist in the shallow part, with slow velocities around the unconsolidated sediments of the riverbed and faster velocities in the consolidated sediments of the surrounding hills. A combined first-arrival tomography using the S-wave velocity model, the initial unconstrained refracted P-wave velocity model, and the original first arrivals is used to obtain a more accurate near-surface P-wave velocity model. This new approach led to a constrained P-wave velocity model from which primary statics are derived and then applied, leading to an improved image with better focusing and continuity of thin layers in the shallowest part.

Estimation of surface consistent residual statics on 3D wide-azimuth P-P or P-Sv data using a Monte-Carlo approach is a challenge. This non-linear method that uses Simulated Annealing is known for its efficiency to compute large magnitude statics but is also characterized by a high computation cost. However, by using advanced methods like High Performance Computing (HPC), the computation cost of this not "embarrassingly parallel" algorithm can be drastically reduced. This paper demonstrates that a nonlinear approach to estimate large magnitude Monte Carlo statics either on P-P or P-Sv data is now possible with a reasonable turnaround. Moreover, this can be done without splitting or chunking the statics computation into several swaths.

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Surface-consistent amplitude corrections are commonly used to correct for the amplitude variations due to nearsurface conditions in land processing. Usually, the corrections for PP and PS datasets are computed separately. There are, however, sound geophysical reasons why a subset of these scalars should be common or shared between PP and PS data. The simultaneous joint estimation of the source and receiver correction scalars for PP and PS datasets that we propose is a way to ensure the consistency of the surface-consistent corrections for both datasets. It also enables a better estimation of the source term which must be identical for PP and PS traces originating from the same shot to be in accordance with the surface-consistent model. When performing surface consistent amplitude corrections of PS datasets, only the radial projection is usually used for the estimation of surface-consistent scalars. As regards the simultaneous joint inversion we also investigated the use of the transverse projection in addition to the radial projection for completeness.