Adaptive reflection waveform inversion: Faster, tighter, deeper, smarter

Abstract

We demonstrate that an appropriate combination of adaptive waveform inversion (AWI) (Guasch et al., 2019), kinematic reflection waveform inversion (RWI) (Warner et al., 2018), and quantum particle-swarm global optimization (qPSO) (Debens et al., 2015), is able to generate accurate wellresolved velocity models from unprocessed raw field data. We begin from simple one-dimensional starting models, we use minimal human intervention, and we recover velocity models, that are both kinematically accurate and highly resolved in space, to depths that lie well below the deepest penetration of refracted arrivals. Using this approach, refractions, reflections and multiples all contribute to the quality of the final velocity model. We demonstrate the efficacy of this approach using a realistic blind synthetic dataset in 2D, and using the corresponding reflection-dominated narrow-azimuth 3D field dataset that served, in part, as the motivation and archetype for the synthetic. For the field data, we demonstrate a close match to a blind well that penetrates below the refracted arrivals. This approach can build final velocity models in a small fraction of the time required for conventional depth velocity-model building. We show three types of inversion: (1) simple vanilla FWI which evolves towards local minima, leading to mis-convergence when starting far from the true answer, (2) AWI which increase the region of convexity that surrounds the global minimum, and (3) AWI-RWI which targets residual timing errors and that has explicit sensitivity to reflection moveout and so is able to recover deep macro-model velocity reflection updates.