Improved One-Way Reflection Waveform Inversion and Strategies for Optimal Offset Selection

Abstract

Conventional reflection waveform inversion solves a two-parameter seismic inverse problem alternately for subsurface reflectivity and acoustic background velocity as the model parameters. It seeks to reconstruct a low-wavenumber velocity model of the subsurface from pure reflection data cyclically, through alternating migration and tomography loops, such that the remodelled data fits the observed data. Low-resolution seismic images with unpreserved amplitudes, full-wave inconsistency in the short-offset data and cycle skipping in the long-offset are perceived as the main reasons for suboptimal tomographic updates and slow convergence in conventional reflection waveform inversion. In the context of one-way reflection waveform inversion, this paper addresses the listed limitations through four main components. First, it augments one-way reflection waveform inversion with a computationally affordable preconditioned least-squares wave equation migration algorithm to ensure high-resolution reflectors with preserved amplitudes. Second, the paper verifies how well the full-wave consistency condition in the short-offset data is satisfied in one-way reflection waveform inversion and suggests muting inconsistent short-offset residual waveforms in the tomography loop to attenuate their adverse imprint. Third, the paper suggests extending the migration offset beyond short offsets to improve both the illumination and the signal-to-noise ratio of the reflectors. Fourth, the paper presents a data-selection algorithm to exclude the damaging effect of the cycle-skipped long-offset data in the tomography loop. The effectiveness of the proposed one-way reflection waveform inversion algorithm is finally validated through three numerical examples, demonstrating its capability to recover high-fidelity tomograms.