An Efficient and Stable High-Resolution Seismic Imaging Method: Point-Spread Function Deconvolution

By fitting observed data with predicted seismograms, least-squares migration (LSM) computes a generalized inverse for a subsurface reflectivity model, which can improve image resolution and reduce artifacts caused by incomplete acquisition. However, the large computational cost of LSM required for simulations and migrations limits its wide applications for large-scale imaging problems. Using point-spread function (PSF) deconvolution, we present an efficient and stable high-resolution imaging method. The PSFs are first computed on a coarse grid using local ray-based Gaussian beam Born modeling and migration. Then, we interpolate the PSFs onto a fine-image grid and apply a high-dimensional Gaussian function to attenuate artifacts far away from the PSF centers. With 2D/3D partition of unity, we decompose the traditional adjoint migration results into local images with the same window size as the PSFs. Then, these local images are deconvolved by the PSFs in the wavenumber domain to reduce the effects of the band-limited source function and compensate for irregular subsurface illumination. The final assembled image is obtained by applying the inverse of the partitions for the deconvolved local images. Numerical examples for both synthetic and field data demonstrate that the proposed PSF deconvolution can significantly improve image resolution and amplitudes for deep structures, while not being sensitive to velocity errors as the data-domain LSM.