Assessment of minimum entropy velocity analysis

The shift to renewable energy has heightened the demand for efficient, cost-effective subsurface imaging, especially for offshore wind farms and underground storage of carbon or hydrogen. These projects frequently depend on short-offset or single-channel seismic data due to logistical and financial constraints, which restrict the resolution of traditional velocity analysis. This work presents a novel method to improve seismic imaging from post-stack data by integrating image focusing into full-waveform inversion (FWI). We include minimum entropy velocity analysis as a cost function within FWI, evaluating subsurface models based on the focusing quality of reverse time migration images using the minimum entropy (ME) norm. Validation on a synthetic dataset shows that, although the ME-norm itself can be ambiguous, its derivative reliably indicates the correct velocity. This insight leads to a new cost function whose gradient, computed via the adjoint-state method, effectively guides model updates through the steepest-descent method. Application to a marine dataset from the Viking Graben (North Sea) demonstrates enhanced image quality, with better reflector alignment and contrast. These results highlight the potential of ME-norm derivatives to drive FWI, thus enabling advanced velocity model building from short-offset or single-fold data with lower computational costs.