Enhancing Low-Wavenumber Information in Reflection Waveform Inversion by the Energy Norm Born Scattering

Abstract

Full waveform inversion (FWI) plays a central role in the field of exploration geophysics due to its potential in recovering the properties of the subsurface at a high resolution. A starting model with ample long-wavelength components is essential for the success of most FWI algorithms. Reflection waveform inversion (RWI) is one popular way to invert for the long-wavelength velocity components from the short offset seismic data by decomposing the gradient of FWI into migration and tomographic terms. However, the transmitted part of Born scattering in conventional RWI still produces high-wavenumber artifacts, which would hinder its convergence. Thus, in this letter, an efficient non-transmission energy norm Born scattering is used in RWI to overcome the drawbacks of conventional RWI. Finally, we use numerical examples to show that the energy norm Born scattering can provide clean reflection energy from the reflector and enhance the low-wavenumber information in the RWI gradient.

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

“…We can also extract specific features from the data to retrieve a good initial velocity model, like seismic envelope inversion [6], [7], [8], phase-based waveform inversion [9], [10]. Reflection-waveform inversion (RWI) is able to retrieve a smooth initial velocity model for FWI [11], [12], [13]. RWI requires the calculation of a trueamplitude image at each iteration by a least-squares reverse time migration (LSRTM), which is computationally expensive.…”