Full Waveform Inversion for Imaging Faulted Structures; a Case Study from Japan Trench Forearc Slope

Abstract Full waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model through a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to assure that the observed and simulated waveforms kinematically fit within an error less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM) to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data is acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model which could improve the depth images down to a depth of almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature which could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.