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Abstract

Summary

This abstract introduces a novel geophysical quantum-annealing application utilizing the Quadratic Unconstrained Binary Optimization (QUBO) algorithm. The algorithm addresses the sparse spike deconvolution (SSD) as a QUBO problem to derive reflectivity from seismic CDP stacks. Initially, we provide a theoretical description of the QUBO algorithm. Subsequently, we present the formulation and mapping of SSD to the QUBO framework. We then apply this approach to a real-world problem, demonstrating various parameters and their impact on the output reflectivity.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.2025643004
2025-10-06
2026-02-07

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References

  1. Booth, M., Reinhardt, S.P., Roy, A., 2017. Partitioning Optimization Problems for Hybrid Classical/Quantum Execution.
     [Google Scholar]
  2. Choong, H.Y., Kumar, S., Gool, L.V., 2023. Quantum Annealing for Single Image Super-Resolution. https://doi.org/10.48550/arXiv.2304.08924
     [Google Scholar]
  3. Oldenburg, D.W., Scheuer, T., Levy, S., 1983. Recovery of the acoustic impedance from reflection seismograms. GEOPHYSICS48, 1318–1337. https://doi.org/10.1190/1.1441413
     [Google Scholar]
  4. Zhang, R., Castagna, J., 2011. Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. GEOPHYSICS76, R147–R158. https://doi.org/10.1190/geo2011-0103.1
     [Google Scholar]
/content/papers/10.3997/2214-4609.2025643004
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Quantum Annealing Assisted Workflow for Sparse Spike Deconvolution
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.2025643004
/content/papers/10.3997/2214-4609.2025643004
/content/papers/10.3997/2214-4609.2025643004
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