Quantum Annealing for Seismic Imaging

Quantum Annealing for Seismic Imaging: Exploring Quantum Annealing Possibilities For Residual Statics Estimation using the D-Wave Advantage System and Hybrid Solver

van der Linde, Stan (TU Delft Electrical Engineering, Mathematics and Computer Science)

Möller, M. (mentor)
Phillipson, Frank (graduation committee)
Dukalski, Marcin (graduation committee)
Neumann, Niels (graduation committee)
Rovetta, Diego (graduation committee)

Delft University of Technology

Applied Mathematics | Computational Science and Engineering

2021-11-11

Recent developments in quantum annealing have shown promising results in logistics, life sciences, machine learning and more. However, in the field of geophysical sciences the applications have been limited. A quantum annealing application was developed for residual statics estimation. Residual statics estimation is a highly non-linear problem in geophysical subsurface imaging. The problem is run on a quantum annealer as well as on a hybrid solver. Quantum annealers solve binary quadratic models, which are a specific type of NP-Hard problem. The hybrid solver uses quantum annealers together with classical computers to solve optimisation problems. Two binary quadratic models were developed for the purpose of residual statics estimation, one based on the one-hot encoding and the other on standard binary encoding. These two models were theoretically analysed and subsequently implemented and tested. Tests with synthetic data were done using the quantum annealer. The solutions found with quantum annealing were often only a few bit-flips away from the global optimum. Therefore, the results were further improved by post-processing of the data with the steepest descent method. It was found that the one-hot encoding with steepest descent performed superior to the other methods. As a final test the hybrid solver was used to solve a business-sized and realistically difficult problem. The hybrid solver outperformed a current industry standard cross-correlation solver.

Quantum Annealing
Quantum Computing
Quantum Algorithms
D-Wave
Subsurface Imaging
Geophysics
Mathematics
QUBO
Binary Quadratic Model
HUBO
One-Hot Encoding
Binary Encoding
Combinatorial Optimization
Residual Statics Estimation
Simulated Annealing
Arid Model
Degree Reduction
Global Optimization
Steepest Descent

http://resolver.tudelft.nl/uuid:ea5b2af6-62e3-4a60-91a1-a59e01266f5b

Student theses

master thesis

© 2021 Stan van der Linde