Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model
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摘要: 根据矿区开采沉降规律对后续沉降进行预测是评估矿山开采风险、调整开采规划的关键。对使用Logistic时间函数模型进行矿区沉降预测中的可用条件进行了分析和模拟实验,并提出了一种基于小基线集合成孔径雷达干涉测量(SBAS-InSAR)技术和Logistic模型的矿区沉降动态预测方法。首先,通过SBAS-InSAR获得矿区的时序沉降数据; 然后,以时序沉降数据作为拟合数据,采用信赖域算法逐像元计算其Logistic模型参数,根据Logistic模型可用条件,确定出可以对后续沉降进行预测的像元范围; 最后,根据Logistic模型对可预测范围内的后续沉降进行预测。以内蒙古鄂尔多斯市某矿区为研究区对上述预测方法进行了实验,采用对应日期的InSAR监测结果对预测结果进行了验证,结果表明: 36d后和108d后预测结果的均方根误差分别为0.0101m和0.0236m,预测误差小于0.03 m的比例分别达到98.9%和89.3%,表明该动态预测模型预测精度较高。
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关键词:
- SBAS-InSAR /
- Logistic模型 /
- 煤矿开采沉降 /
- 动态预测
Abstract: Predicting the subsequent subsidence in mining areas according to the law of mining subsidence is the key to assessing mining risks and adjusting mining planning. This study determined the available conditions of the logistic model for mining subsidence prediction through analysis and simulation experiments and proposed a method for the dynamic prediction of mining subsidence based on small baseline subset (SBAS)-interferometric synthetic aperture radar (InSAR) technology and the logistic model. Firstly, the time-series subsidence data of a mining area was obtained using the SBAS-InSAR technology. Then, taking the time series subsidence data as the data for fitting, the parameters of the logistic model were calculated pixel by pixel by using the trust region algorithm. Then, the pixel range in which the subsequent subsidence can be predicted was determined according to the available conditions of the logistic model. Finally, according to the Logistic model, the subsequent subsidence within the predictable range was predicted. This method was applied to a certain mining area in Erdos City, Inner Mongolia for tests, and the prediction results were verified using the InSAR monitoring results of corresponding dates. The predicted results after 36 days and 108 days of ming had the root mean square error (RMSE) of 0.010 1 m and 0.023 6 m, respectively, and their proportion with prediction errors of less than 0.03 m reached 98.9% and 89.3%, respectively. These results indicate that the method for dynamic prediction proposed in this study has high prediction accuracy.-
Key words:
- SBAS-InSAR /
- Logistic model /
- coal mining subsidence /
- dynamic prediction
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