Fast 3D migration-velocity analysis by wavefield extrapolation using the prestack exploding-reflector model

We introduce an integrated wave-equation technique for migration velocity analysis (MVA) that consists of three steps: (1) forming the extended data, (2) approximating the correct transmitted wavefield, and (3) using wavefield tomography to update the velocity model. In the first step, the crosscorrelation imaging condition is relaxed to produce other nonzero-lag common image gathers (CIG) that, combined, form a common image cube (CIC). Slicing the CIC at different crosscorrelation lags forms a series of CIGs. Flattened events will occur in the CIGs at a lag other than the zero-lag when an incorrect velocity model is used in the migration. In the second step, for each event on the CIG, we pick the focusing depth and crosscorrelation lag at which it is flattest. We then model a Green’s function by seeding a source at the focusing depth using one-way wave equation modeling, then shift the modeled wavefield with the focusing crosscorrelation lag. This process is repeated for the other primary events at different lateral and vertical positions. The result is a set of modeled data whose wavefield approximates the wavefield that would have been generated if the correct velocity model had been used to simulate these gathers. We then apply wavefield tomography on these data-driven modeled data to update the velocity model. Our inversion scheme is based on wave-equation traveltime tomography that can update the velocity model in the presence of large velocity errors and a complex environment. Tests on synthetic and real 2D seismic data confirm the method’s effectiveness in building velocity models in complex structural areas that have large lateral velocity variations.

Migration velocity analysis using traveltime wavefield tomography

Al‐Saleh

Jiao

2012

Moveout-based wave-equation migration velocity analysis

Zhang

Biondi

2013

Self Cite

We propose a new method to perform wave-equation migration velocity analysis using angle-domain common image gathers. Instead of maximizing the imagestack-power objective function directly with respect to the slowness, we link the objective function to the slowness indirectly through an intermediate moveout parameter. Since this approach is robust against the cycle-skipping problem, it produces more reasonable gradients. Also the proposed method does not require explicit picking of the moveout parameters. Our data examples shows the great potential of this method.

Seismic differential semblance-oriented migration velocity analysis — Status and the way forward

Chauris,

Farshad

2023

Differential Semblance Optimization (DSO) is a seismic waveform inversion approach. It has been proposed to remove the limitation of conventional full waveform inversion as a velocity model building tool when only reflected waves are available. It has connections with Migration Velocity Analysis (MVA) techniques defined in the image domain to build macro velocity models, with a splitting between the macro-model and the small-scale heterogeneities. Among the various DSO approaches, DSO-MVA has unique characteristics for complex velocity model building, while ensuring the convergence to the global minimum in a local optimization approach: the wavefields are computed with the two-way wave equation operators, focusing panels are built in a survey-sinking mode and the quality of the macro velocity model is evaluated on common image gathers in the subsurface offset domain through a differential annihilator. We review here the significant aspects that have been better understood over the last fifteen years and discuss the way forward. A first identified issue has been the imprint of the small-scale heterogeneities on the macro-model update that was revealed with the use of wave-equation based Green's functions and an ad-hoc solution was later proposed by slightly modifying the definition of the objective function. A second important aspect has been the development of efficient approaches to compute the required quantitative migration based on approximate inverses to replace the previously used migrations. The issue of correctly interpreting the amplitudes has also moved into one of an improved model parameterization, i.e. from constant-density acoustics media to full acoustic media as a path towards fully visco-elastic media. Finally, we discuss the way forward with the challenges of extending DSO-MVA beyond the single scattering approximation, for processing diving waves and multiples, but also the challenge of the numerical implementation, that remains critical even in 2D, for an applicability on large-scale real data.