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<p>Accurate characterization of the initial in-situ stress field is essential for stability assessment, support design, and surrounding rock control in deep underground engineering, yet field measurements are often highly scattered and three-dimensional inversion is computationally expensive. This study develops a robust and efficient inversion framework for a deeply buried underground powerhouse in the southeastern <i>Tibetan Plateau</i>. First, a three-dimensional borehole stress synthesis method is established by combining particle swarm optimization, the <i>Huber loss</i>, <i>Levenberg–Marquardt iteration</i>, and <i>regularization</i> to denoise multi-source measurements, suppress local outliers, and alleviate ill-conditioning in stress-tensor reconstruction. Second, a <i>surrogate-assisted differential evolution</i> workflow is constructed using a radial basis function network within a prediction–verification–correction <i>active-learning loop</i> to reduce the cost of repeated forward simulations while preserving global optimization capability. Application to the powerhouse shows that the mean relative error decreases from 14.12 to 9.34%, and the deviation variance decreases from 2.98 to 1.33 after optimization. The proposed framework improves both the reliability of inversion input data and the efficiency of field-scale stress reconstruction, providing practical support for stability evaluation and surrounding rock control in deep, geologically complex underground caverns.</p>

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Robust in-situ stress inversion in an underground powerhouse using tensor synthesis and surrogate-assisted differential evolution

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Zhihong Dong,
  • Shijie Hu,
  • Yuan Qian,
  • Xinhui Zhang,
  • Ping Fu,
  • Kaicheng Zhang

摘要

Accurate characterization of the initial in-situ stress field is essential for stability assessment, support design, and surrounding rock control in deep underground engineering, yet field measurements are often highly scattered and three-dimensional inversion is computationally expensive. This study develops a robust and efficient inversion framework for a deeply buried underground powerhouse in the southeastern Tibetan Plateau. First, a three-dimensional borehole stress synthesis method is established by combining particle swarm optimization, the Huber loss, Levenberg–Marquardt iteration, and regularization to denoise multi-source measurements, suppress local outliers, and alleviate ill-conditioning in stress-tensor reconstruction. Second, a surrogate-assisted differential evolution workflow is constructed using a radial basis function network within a prediction–verification–correction active-learning loop to reduce the cost of repeated forward simulations while preserving global optimization capability. Application to the powerhouse shows that the mean relative error decreases from 14.12 to 9.34%, and the deviation variance decreases from 2.98 to 1.33 after optimization. The proposed framework improves both the reliability of inversion input data and the efficiency of field-scale stress reconstruction, providing practical support for stability evaluation and surrounding rock control in deep, geologically complex underground caverns.