Quantum annealer-assisted residual refraction statics estimation on the SEAM Arid model dataset
Diego Rovetta, M. Dukalski, A. Kontakis · 2023
Summary Quantum annealing emerged as a very promising tool for solving combinatorial optimization problems. In particular, residual refraction statics estimation has been identified to be the first potential industrial use case of quantum annealing in geoscience. The currently available quantum annealers have still too small computing capabilities to effectively and independently estimate residual statics on problems of industrial scale. On the other hand, a quantum annealer-assisted hybrid solver, combining classical and quantum information processing could already be a viable tool for this purpose. Here we show the application of such a hybrid solver to the residual refraction statics estimation through stack-power maximization on a significant subset of the SEAM Arid model dataset, comprising of over two thousand gathers. We benchmark our approach against a dedicated deterministic classical solver, showing that in a fixed amount of time per gather, the quantum annealer-assisted solution can in many instances outperform the standard approach.