An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

The estimation of the subsurface acoustic impedance (AI) model is an important step seismic data processing for oil and gas exploration. full waveform inversion (FWI) a powerful way to invert parameters with surface acquired data. Nevertheless, strong nonlinear relationship between will cause nonconvergence unstable problems in practice. To divide into some more linear steps, 2D AI imaging method proposed estimate broadband based on reflectivity. Firstly, novel scheme Gaussian beam migration (GBM) produce point spread function (PSF) conventional image subsurface. Then, reflectivity can be obtained by implementing deconvolution respect calculated PSF. Assuming that low-wavenumber part deduced background velocity, we implemented merging as high-wavenumber produced result. developed GBM computational hotspot includes only two one demigraton (Born modeling) processes. Hence, efficient than using least-squares migrations (LSMs) require multiple iterations (every iteration Born modeling process) minimize objective residuals. Numerical examples both synthetic field have demonstrated validity application potential method.