David L. Hinkley

Fast full-wavefield seismic inversion using encoded sources

Full-wavefield seismic inversion (FWI) estimates a subsurface elastic model by iteratively minimizing the difference between observed and simulated data. This process is extremely computationally intensive, with a cost comparable to at least hundreds of prestack reverse-time depth migrations. When FWI is applied using explicit time-domain or frequency-domain iterative-solver-based methods, the seismic simulations are performed for each seismic-source configuration individually. Therefore, the cost of FWI is proportional to the number of sources. We have found that the cost of FWI for fixed-spread data can be significantly reduced by applying it to data formed by encoding and summing data from individual sources. The encoding step forms a single gather from many input source gathers. This gather represents data that would have been acquired from a spatially distributed set of sources operating simultaneously with different source signatures. The computational cost of FWI using encoded simultaneous-source gathers is reduced by a factor roughly equal to the number of sources. Further, this efficiency is gained without significantly reducing the accuracy of the final inverted model. The efficiency gain depends on subsurface complexity and seismic-acquisition parameters. There is potential for even larger improvements of processing speed.

Fast Full Wave Seismic Inversion Using Source Encoding

Full Wavefield Seismic Inversion (FWI) estimates a subsurface elastic model by iteratively minimizing the difference between observed and simulated data. This process is extremely compute intensive, with a cost on the order of at least hundreds of prestack reverse time migrations. For time-domain and Krylov-based frequency-domain FWI, the cost of FWI is proportional to the number of seismic sources inverted. We have found that the cost of FWI can be significantly reduced by applying it to data processed by encoding and summing individual source gathers, and by changing the encoding functions between iterations. The encoding step forms a single gather from many input source gathers. This gather represents data that would have been acquired from a spatially distributed set of sources operating simultaneously with different source signatures. We demonstrate, using synthetic data, significant cost reduction by applying FWI to encoded simultaneous-source data.

A practical procedure for application of 3D SRME to conventional marine data

Complex 3D multiples are a pervasive problem affecting data sets from a variety of geographic areas. 3D surface-related multiple elimination (SRME) is a powerful method capable of attenuating a large class of even the most complex 3D multiples. While SRME does not make any assumptions about the subsurface and is a data-driven procedure, its 3D implementation requires dense sampling of the wavefield on the surface, amounting to the deployment of a shot and a receiver at every surface location. Typical marine acquisition geometries deliver much sparser surface coverage, which results in severe shot- and receiver-domain aliasing in the crossline direction.