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The TGS360Pro system utilizes seismic wave technology to perform advanced geological forecasting for tunnels, generating multidimensional data parameters including relative stress changes, water content, elastic modulus, and danger levels. However, the complex interrelations among these parameters often come with significant redundant information and sometimes exhibit mutually exclusive relationships, noticeably increasing the difficulty of data processing and geological interpretation. To address this issue, this study effectively integrated multiple related features, significantly improving the accuracy of data analysis and the generalization ability of the predictive model through techniques such as deviation maximization and multi-criteria decision analysis. It revealed potential hidden patterns within the data, thereby enhancing the model’s prediction accuracy and improving the accuracy of geological hazard identification. Through practical application in an underground plant project of a hydropower station, this study has demonstrated the efficiency and reliability of this method in identifying geological phenomena and predicting geological conditions.

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Multi-criteria Decision Analysis of TGS360Pro Advance Geological Forecasting Results Based on Deviation Maximization

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Mingcai Zhang,
  • Guanghong Ju,
  • Dazhou Zhang,
  • Zonggang Chen,
  • Dong Li,
  • Song Han

摘要

The TGS360Pro system utilizes seismic wave technology to perform advanced geological forecasting for tunnels, generating multidimensional data parameters including relative stress changes, water content, elastic modulus, and danger levels. However, the complex interrelations among these parameters often come with significant redundant information and sometimes exhibit mutually exclusive relationships, noticeably increasing the difficulty of data processing and geological interpretation. To address this issue, this study effectively integrated multiple related features, significantly improving the accuracy of data analysis and the generalization ability of the predictive model through techniques such as deviation maximization and multi-criteria decision analysis. It revealed potential hidden patterns within the data, thereby enhancing the model’s prediction accuracy and improving the accuracy of geological hazard identification. Through practical application in an underground plant project of a hydropower station, this study has demonstrated the efficiency and reliability of this method in identifying geological phenomena and predicting geological conditions.