Research on the application of the Quantum-behaved Particle Swarm Optimization algorithm in the inverse estimation of in-situ stress based on fault-slip fractures

ZHOU Weiwei; FENG Yongcun; LI Xiaorong; LIU Jie; SU Feiyu; HU Han

doi:10.12090/j.issn.1006-6616.2025095

Volume 31 Issue 6

Dec. 2025

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Article Navigation > Journal of Geomechanics > 2025 > 31(6): 1238-1254

ZHOU W W，FENG Y C，LI X R，et al.，2025. Research on the application of the Quantum-behaved Particle Swarm Optimization algorithm in the inverse estimation of in-situ stress based on fault-slip fractures[J]. Journal of Geomechanics，31（6）：1238−1254 doi: 10.12090/j.issn.1006-6616.2025095

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Research on the application of the Quantum-behaved Particle Swarm Optimization algorithm in the inverse estimation of in-situ stress based on fault-slip fractures

doi: 10.12090/j.issn.1006-6616.2025095

ZHOU Weiwei^1
,,
FENG Yongcun^{2
,
,},
LI Xiaorong^2
,,
LIU Jie^1
,,
SU Feiyu^1
,,
HU Han^1
,

1.
College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
2.
College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China

Funds: This research is financially supported by the National Science and Technology Major Project (Grant No. 2025ZD1011100).

More Information

Received: 2025-07-30
Revised: 2025-10-09
Accepted: 2025-10-27

Available Online: 2025-12-12

Published: 2025-12-28

Abstract

Abstract

Objective To improve the computational efficiency and accuracy of stress tensor inversion from fault-slip data, and to address the limitations of conventional grid search methods—namely, high computational cost and susceptibility to local optima—an inversion approach based on intelligent optimization algorithms was investigated. Methods A novel fault-slip data inversion method based on the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is proposed, in which the stress tensor is parameterized by four variables: three Euler angles (α, β, γ) representing the orientations of the principal stress axes and a stress ratio (Φ). A misfit function is constructed based on the angular deviation between the shear stress direction and the observed slip vector. To enhance convergence performance, an elite-guided learning strategy was adopted, incorporating a reward-penalty feedback mechanism and a tensor distance metric to quantify stress similarity. Multiple synthetic stress models were tested using a simulated fault-slip dataset, and the inversion performance of QPSO was compared with the conventional grid search method in terms of efficiency and accuracy. Results The proposed QPSO-based inversion method achieves a non-convergence rate below 8% and reduces computational time to approximately 1/27 of what is required by the grid search approach. The method converges rapidly in high-dimensional, multimodal parameter spaces and accurately identifies normal, reverse, and strike-slip stress regimes. The well-defined clustering of the inversion results indicates strong stability and physical consistency. Conclusion The QPSO-based method exhibits significant advantages in stress tensor inversion from fault-slip data, including high computational efficiency, strong adaptability, and fast convergence. Significance It provides effective technical support for regional in-situ stress field reconstruction and focal mechanism analysis, and offers the enlightenment and reference value of theoretical methods in geomechanical applications.
- stress tensor inversion,
- fault-slip data,
- in-situ stress,
- QPSO inversion method,
- tensor distance

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