Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR
Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR
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DOI：
10.3390/rs15235546
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Sichun Long;Maoqi Liu;Chaohui Xiong;Tao Li;Wenhao Wu;Hongjun Ding;Liya Zhang;Chuanguang Zhu-Chuanguang-Z
中科院分区：
文献类型：
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作者：
Sichun Long;Maoqi Liu;Chaohui Xiong;Tao Li;Wenhao Wu;Hongjun Ding;Liya Zhang;Chuanguang Zhu-Chuanguang-Z
目前对采矿区地表变形预测的研究主要依赖于以参数为中心的数值模型，这表明了适用性和参数可靠性方面的限制。虽然多时相干涉合成孔径雷达（MT-干涉合成孔径雷达）技术获取了丰富的数据，但这些数据中蕴含的信息尚未被充分挖掘。因此，本文提出了一种新的方法来解释矿区地表变形集成学习与MT-InSAR技术。在MT-InSAR监测结果的基础上，通过融合基于距离的特征和空间自相关理论，获得了集成学习数据集的目标变量。在随后的阶段，空间分层抽样以及互信息方法被部署到选择数据集的功能。利用湖南资兴煤矿MT-InSAR监测数据，采用格兰杰因果关系检验和Johansen协整分析方法，对研究区断层滑动与煤层开采之间的关系进行了严格分析，从而获得了训练Bagging模型所需的数据集。随后，利用Bagging技术，采用决策树，支持向量回归和多层感知器作为基础估计器构建集成模型。将树结构Parzen估计（TPE）优化算法应用于Bagging模型，得到了矿区断层滑动预测的优化模型。与基线模型相比，性能提高了25.88%，证实了本研究中概述的数据预处理方法的有效性。这一结果也证明了集成学习与MT-InSAR技术相结合预测矿区地表变形的创新性和可行性。该研究首次将TPE优化集成模型与MT-InSAR技术相结合，为矿区地表变形预测提供了新的视角，并为进一步揭示MT-InSAR监测数据中隐藏的信息提供了有价值的见解。
The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.