During transient electromagnetic method （TEM） exploration of a copper mine, we detected the late-channel TEM signal reversal phenomenon （a voltage change from positive to negative） caused by the influence of the i...During transient electromagnetic method （TEM） exploration of a copper mine, we detected the late-channel TEM signal reversal phenomenon （a voltage change from positive to negative） caused by the influence of the induced polarization （IP） effect, which affects the depth and precision of the TEM detection. The conventional inversion method is inefficient because it is difficult to process the data. In this paper, the Cole-Cole model is adopted to analyze the effect of Dc resistivity, chargeability, time constant, and frequency exponent on the TEM response in an homogeneous half space model. Singular Value Decomposition （SVD） is used to invert the measured TEM data, and the Dc resistivity, chargeability, time constant and frequency exponent were extracted from the measured TEM data in the mine area. The extracted parameters are used for interpreting the detection result as a supplement. This reveals why the TEM data acquired in the area has a low resolution. It was found that the DC resistivity and time constant do not significantly change the results, however, the chargeability and frequency exponent have a significant effect. Because of these influences, the SVD method is more accurate than the conventional method in the apparent resistivity profile. The area of the copper mine is confined accurately based on the SVD inverted data. The conclusion has been verified by drill and is identical to the practical geological situation.展开更多
为解决压电波动法在检测混凝土结构时面临的源信号信噪比低、背景噪声大与非平稳性问题,提出了一种K奇异值分解(K-means singular value decomposition,K-SVD)更新字典的压电信号滤波方法,并对混凝土结构损伤进行了识别。首先,对开裂状...为解决压电波动法在检测混凝土结构时面临的源信号信噪比低、背景噪声大与非平稳性问题,提出了一种K奇异值分解(K-means singular value decomposition,K-SVD)更新字典的压电信号滤波方法,并对混凝土结构损伤进行了识别。首先,对开裂状态与完整状态下混凝土结构的压电信号进行采集,并将采集的信号进行分类处理;其次,对上述采集的压电信号进行滤波处理,并对K-SVD字典学习滤波结果与未滤波结果进行对比分析,评价K-SVD字典学习滤波方法的适用性;最后,利用残差卷积神经网络(residual network,ResNet)对滤波后的压电信号进行分类识别。结果表明:利用基于K-SVD字典学习与ResNet模型,能够稳定地识别混凝土结构内部损伤的压电信号;训练集与测试集的损伤信号识别准确率分别为93.25%与92.38%,无损信号的识别准确率分别为95.41%与94.67%,相较于未滤波的采集信号,其准确率提升了10个百分点以上;利用K-SVD字典学习与ResNet对混凝土结构损伤进行有效识别,实现了对混凝土结构内部损伤区域的定位。研究结果可为混凝土结构健康监测的数据处理提供一种新的思路。展开更多
基金supported by the National Technology R&D Program in the 11th Five year Plan of China(No.2007BAQ00168-1-1)the National Natural Science Foundation of China(No. 41103052/D0309)the Shanxi Province Excellent Graduate Innovation Program(No. 20113038)
文摘During transient electromagnetic method （TEM） exploration of a copper mine, we detected the late-channel TEM signal reversal phenomenon （a voltage change from positive to negative） caused by the influence of the induced polarization （IP） effect, which affects the depth and precision of the TEM detection. The conventional inversion method is inefficient because it is difficult to process the data. In this paper, the Cole-Cole model is adopted to analyze the effect of Dc resistivity, chargeability, time constant, and frequency exponent on the TEM response in an homogeneous half space model. Singular Value Decomposition （SVD） is used to invert the measured TEM data, and the Dc resistivity, chargeability, time constant and frequency exponent were extracted from the measured TEM data in the mine area. The extracted parameters are used for interpreting the detection result as a supplement. This reveals why the TEM data acquired in the area has a low resolution. It was found that the DC resistivity and time constant do not significantly change the results, however, the chargeability and frequency exponent have a significant effect. Because of these influences, the SVD method is more accurate than the conventional method in the apparent resistivity profile. The area of the copper mine is confined accurately based on the SVD inverted data. The conclusion has been verified by drill and is identical to the practical geological situation.
文摘为解决压电波动法在检测混凝土结构时面临的源信号信噪比低、背景噪声大与非平稳性问题,提出了一种K奇异值分解(K-means singular value decomposition,K-SVD)更新字典的压电信号滤波方法,并对混凝土结构损伤进行了识别。首先,对开裂状态与完整状态下混凝土结构的压电信号进行采集,并将采集的信号进行分类处理;其次,对上述采集的压电信号进行滤波处理,并对K-SVD字典学习滤波结果与未滤波结果进行对比分析,评价K-SVD字典学习滤波方法的适用性;最后,利用残差卷积神经网络(residual network,ResNet)对滤波后的压电信号进行分类识别。结果表明:利用基于K-SVD字典学习与ResNet模型,能够稳定地识别混凝土结构内部损伤的压电信号;训练集与测试集的损伤信号识别准确率分别为93.25%与92.38%,无损信号的识别准确率分别为95.41%与94.67%,相较于未滤波的采集信号,其准确率提升了10个百分点以上;利用K-SVD字典学习与ResNet对混凝土结构损伤进行有效识别,实现了对混凝土结构内部损伤区域的定位。研究结果可为混凝土结构健康监测的数据处理提供一种新的思路。
