Estimation of acoustic macro models using a genetic full-waveform inversion: Applications to the Marmousi model

We present a stochastic full-waveform inversion that uses genetic algorithms (GA FWI) to estimate acoustic macro-models of the P-wave velocity field. Stochastic methods such as GA severely suffer the curse of dimensionality, meaning that they require unaffordable computer resources for inverse problems with many unknowns and expensive forward modeling. To mitigate this issue, we propose a two-grid technique, that is, a coarse grid to represent the subsurface for the GA inversion and a finer grid for the forward modeling. We applied this procedure to invert synthetic acoustic data of the Marmousi model. We show three different tests. The first two tests use as prior information a velocity model derived from standard stacking velocity analysis and differ only for the parameterization of the coarse grid. Their comparison shows that a smart parameterization of the coarse grid may significantly improve the final result. The third test uses a linearly increasing 1D velocity model as prior information, a layer-stripping procedure, and a large number of model evaluations. All the three tests return velocity models that fairly reproduce the long-wavelength structures of the Marmousi. First-break cycle skipping related to the seismograms of the final GA-FWI models is significantly reduced compared to the one computed on the models used as prior information. Descent-based FWIs starting from final GA-FWI models yield velocity models with low and comparable model misfits and with an improved reconstruction of the structural details. The quality of the models obtained by GA FWI + descent-based FWI is benchmarked against the models obtained by descent-based FWI started from a smoothed version of the Marmousi and started directly from the prior information models. The results are promising and demonstrate the ability of the two-grid GA FWI to yield velocity models suitable as input to descent-based FWI

Horizon picking for multidimensional data: an integrated approach

3-D seismic surveys generate 5-D data volume. In order to estimate the horizons for interpretation and further processing, the traveltime picking needs to be performed on n-D subsets of this 5-D data volume (n≤5). Horizon picking (HP) is complicated by the irregular sampling, faults, discontinuities, and low signal-to-noise ratio areas. The automatic HP techniques described here are aimed to support the interpreters in the estimation of the events by preserving their depth continuity. The HP is carried out directly on the full n-D dataset and not just iterated on 2-D sliced subsets, this avoids misties among different 2-D slices. The additional advantages are that the proposed method can perform the HP of multiple and irregularly sampled horizons, in addition it can handle discontinuous events by keeping the association with the target in depth. The interpreter is only asked to initialize the HP by providing some seed picks on the target horizon(s), then the algorithm lets the estimate of the horizons grow along all the dimensions simultaneously

Multi-Arrival Kirchhoff Migration - Wavefronts Separation and Equalization

reserved5In the case of a complex subsurface single-arrival Kirchhoff migration results can be inadequate. In this work we address the issue of implementing true amplitude multi-arrival Kirchhoff migration with equalization in the angles domain starting from a single arrival algorithm. We develop a method to implement multi-arrival Kirchhoff migration with equalization in the angle domain. We solve wavefront separation by applying a continuity condition and we show that the hit-count panel used for equalization in the multiarrival case is the summation of the single-arrival hit-count. Results on synthetic data prove that multi-arrival migration improves imaging in the case of complex velocity model.M. Giboli; V. Lipari; G. Drufuca; C. Andreoletti; N. BienatiGiboli, Matteo; Lipari, Vincenzo; Drufuca, Giuseppe; C., Andreoletti; N., Bienat

Two-grid genetic algorithm full-waveform inversion

Full-waveform inversion (FWI) tries to estimate velocity models of the subsurface with improved accuracy and resolution compared to conventional methods. To be successful, it needs input data that is rich in low frequencies and possibly characterized by long source-to-receiver offsets. The correct solution of the inverse problem by means of local methods is facilitated if the starting model lies in the “valley” of the cost-function global minimum. We explore the possibility of relaxing this requirement by using genetic algorithms, a stochastic optimization method, as the driver of the FWI (GA FWI). However, stochastic methods are affected by the “curse of dimensionality,” meaning that they require huge and sometimes even unaffordable computer resources for inverse problems with many unknowns and costly forward modeling. Therefore, we need to adopt proper stratagems in the inversion and limit our goal to the estimation of a velocity macromodel that is of a model with only the long-wavelength velocity structures, which could eventually act as the starting model for a local, higher-resolution gradient-based inversion. To this end, in the GA FWI we parametrize the subsurface with two grids: (1) a coarse grid with widely spaced nodes, that is unknowns, for the inversion, and (2) a fine grid with shorter spacing for the modeling. As a side result, we can also have an estimate of the uncertainty at the solution nodes of the grid. The approach we discuss is 2D acoustic in the time domain, with finite difference forward modeling. The examples we show refer to the Marmousi model and to a marine field data set