Improved depth imaging by constrained full-waveform inversion
This work addresses challenges in subsurface imaging for geophysics, but it is incremental as it builds on existing full-waveform inversion methods with a new regularization technique.
The paper tackled the problem of improving depth imaging in seismic inversion by formulating full-wavefield inversion as a constrained optimization problem, resulting in enhanced sharpness and correct repositioning of reflectors in synthetic datasets with noise.
We propose a formulation of full-wavefield inversion (FWI) as a constrained optimization problem, and describe a computationally efficient technique for solving constrained full-wavefield inversion (CFWI). The technique is based on using a total-variation regularization method, with the regularization weighted in favor of constraining deeper subsurface model sections. The method helps to promote "edge-preserving" blocky model inversion where fitting the seismic data alone fails to adequately constrain the model. The method is demonstrated on synthetic datasets with added noise, and is shown to enhance the sharpness of the inverted model and correctly reposition mispositioned reflectors by better constraining the velocity model at depth.