Quantum computing, along with quantum metrology and quantum communication, are disruptive technologies that promise, in the near future, to impact different sectors of academic research and industry. Among the computational challenges with great interest in science and industry are the inversion problems. These kinds of numerical procedures can be described as the process of determining the cause of an event from measurements of its effects. In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. We compare the obtained results from the quantum computer to those derived from a classical algorithm. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
Quantum computing, along with quantum metrology and quantum communication, are disruptive technologies that promise, in the near future, to impact different sectors of academic research and industry. Among the computational challenges with great interest in science and industry are the inversion problems. These kinds of numerical procedures can be described as the process of determining the cause of an event from measurements of its effects. In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. We compare the obtained results from the quantum computer to those derived from a classical algorithm. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
Quantum computing, along with quantum metrology and quantum communication, are disruptive technologies that promise, in the near future, to impact different sectors of academic research and industry. Among the computational problems with great interest in science and industry are the inversion problems. These kinds of problems can be described as the process of determining the cause of an event from measurements of its effects. In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer to recover a sound wave speed profile, typical in seismic inversion experiments. We compare the obtained results from the quantum computer to those derived from a classical computer. The accuracy achieved by the quantum computer is nearly the same as that of the classical computer.
Quantum computing is increasingly recognized as a transformative technology in geophysics, offering the potential to significantly enhance computational power and efficiency. This advancement holds promise for the simulation and processing of complex geological data at unprecedented speeds. Recent studies have begun exploring the application of quantum computing methods to simplified versions of seismic inversion problems, underscoring the technology’s capability to address real-world inverse problems. The primary aim of this study is to tackle a realistic, scalable, and business-relevant problem by estimating acoustic impedances from seismic trace data using a quantum computer. To the best of our knowledge, this is the first instance of seismic impedances being predicted from seismic data through quantum computing, with a discussion on the advantages of solving the inverse problem on quantum processing units (QPUs). In this paper, we leverage the D-Wave quantum annealer to tackle the poststack seismic inversion problem, employing a novel two-step workflow. In the first step, we utilize the quantum annealer to estimate reflectivities from seismic data. Subsequently, these estimated normal-incidence reflectivities serve as the basis for predicting acoustic impedances with the same quantum technology. To validate the effectiveness of our approach, we present five examples, juxtaposing the impedance predictions from the D-Wave quantum annealer against those obtained through Simulated Annealing—a stochastic global optimizer traditionally employed for seismic inversion. Remarkably, the impedances derived from the quantum annealer matched closely with the true values in just a single epoch, whereas simulated annealing required 10 epochs to achieve a similar accuracy. Furthermore, the QPUs within our hybrid solver took only about 0.08 seconds to estimate these seismic impedances. This is remarkably efficient when compared to the time needed by both the classical component of the hybrid solver and simulated annealing, which each took over 10 seconds. This highlights the potential of QPUs to entirely resolve the seismic inverse problem in under a second, underscoring the transformative impact that quantum computing can have in the field of geophysics.
Abstract 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. A novel application of quantum computing for seismic inversion is presented, marking the first instance of inverting seismic data to estimate both P-wave and S-wave impedances using a quantum annealer. Building upon the 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.
Quantum computing has advanced enough that solving basic inverse problems in exploration seismology may be beneficial to show proof-of-concept results. The problems solved should be simple enough to work with the current early state of quantum computing hardware, which would be comparable to problems solved in the early stages of classical computing. We introduce an iterative algorithm for solving PDE-constrained optimization problems on a quantum annealer. We then use this method to invert for subsurface P-wave velocity—using a hybrid of classical and quantum computing—in a simple seismic experiment. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 9:20 AM Presentation Time: 10:10 AM Location: Poster Station 11 Presentation Type: Poster
This study demonstrates the application of quantum computing based quantum annealing to seismic traveltime inversion, a critical approach for inverting highly accurate velocity models. The seismic inversion problem is first converted into a Quadratic Unconstrained Binary Optimization problem, which the quantum annealer is specifically designed to solve. We then solve the problem via quantum annealing method. The inversion is applied on a synthetic velocity model, presenting a carbon storage scenario at depths of 1000-1300 m. As an application example, we also show the capacity of quantum computing to handle complex, noisy data environments. This work highlights the emerging potential of quantum computing in geophysical applications, providing a foundation for future developments in high-precision seismic imaging.
Abstract One of the promising areas for the future application of quantum computers is problems that are solved using stochastic modeling methods. One of these problems is the problem of synchronous inversion of seismic data in the presence of multimodal information on the elasticity characteristics of geological rocks. This paper analyzes the possibility of solving this problem using quantum annealing. Its reduction to the QUBO task is demonstrated. An assessment is carried out demonstrating the fundamental possibility of solving industrial-scale 1D problems on the latest models of the D-Wave hybrid quantum-classical annealer. In conclusion, an idea is briefly discussed, which, if successfully implemented, may make it possible to solve many 1D problems at once on such an annealer.