Pre-stack and post-stack seismic inversion using quantum computing

TL;DR

This paper introduces a novel quantum computing approach for seismic inversion, enabling simultaneous estimation of P-wave and S-wave impedances with faster computation and fewer qubits, advancing real-time geophysical analysis.

Contribution

It presents the first application of quantum annealing for inverting both post-stack and pre-stack seismic data in a single step, reducing qubit requirements and increasing speed.

Findings

Quantum annealing closely matches true impedance values in a single epoch.

The method significantly reduces computation time from 20s to 6.3s.

Quantum approach outperforms simulated annealing in accuracy and speed.

Abstract

Quantum computing harnesses the principles of quantum mechanics to solve problems that are intractable for classical computers. Quantum annealing, a specialized approach within quantum computing, is particularly effective for optimization tasks, as it leverages quantum tunneling to escape local minima and efficiently explore complex energy landscapes. In geosciences, many problems are framed as high-dimensional optimization problems, including seismic inversion, which aims to estimate subsurface impedances from seismic data for accurate geological interpretation and resource exploration. This study presents a novel application of quantum computing for seismic inversion, marking the first instance of inverting seismic data to estimate both P-wave and S-wave impedances using a quantum annealer. Building upon our prior work, which demonstrated the estimation of acoustic impedances from post-stack data using a two-step framework, we propose an enhanced workflow capable of inverting both post-stack and pre-stack seismic data in a single step. This advancement significantly reduces the number of qubits per model parameter (from 20 to 5) while improving computational speed (from 20 seconds to 6.3 seconds). The seismic inversion is implemented using the D-Wave Leap hybrid solver, achieving impedance estimation within 4-9 seconds, with the quantum processing unit (QPU) contributing just 0.043-0.085 seconds. Comparative analysis with simulated annealing reveals that quantum annealing produces impedance models closely matching true values in a single epoch, whereas simulated annealing requires 10 epochs for improved accuracy. These findings underscore the transformative potential of quantum computing for real-time, high-precision seismic inversion, marking a crucial step toward fully quantum-driven geophysical solutions.