GPS observation network is deployed in the central part of Ningxia, which is the juncture of the Alxa block, Ordos block and Qinghai-Xizang (Tibet) block. Using the data of five phases of repeated survey sine 1996, th...GPS observation network is deployed in the central part of Ningxia, which is the juncture of the Alxa block, Ordos block and Qinghai-Xizang (Tibet) block. Using the data of five phases of repeated survey sine 1996, the current state of crustal movement in the central part of Ningxia is analyzed. From the result, we can know the following. (1) In the period from Dec. 1996 to May 1999, the central part of Ningxia had the phenomenon of left-lateral movement about the west margin of Ordos (measuring station P2) and the Lingwu fault on the east of the Yinchuan basin displayed the mode of left-lateral reverse strike slip movement. In that region, the direction of the principal stress field was NNE-SSW (with an azimuth of 29.8?; the central part to the south of the measuring station P2 displaced eastward; the vertical deformation was obviously greater than the horizontal deformation in order of magnitude; the Yinchuan basin and Qinghai-Xizang (Tibet) block were in a state of rising; the measuring station P1 in the hinterland of Ordos showed a trend of subsiding year by year; and there may be a hidden fault to exist between the measuring points P3 and P4. (2) About one year before the occurrence of moderately strong earthquakes in the vicinity of the measuring region, deformation anomalies and abnormal changes of principal stress direction can be observed by the GPS measuring stations in that region; before moderately strong earthquakes near the measuring region and before strong earthquakes in adjacent regions, the simulated GPS deformation vector field ofthat region can betoken the approximate position of the coming earthquake. These results can be regarded as the eigenvalues of earthquake prediction for consideration.展开更多
Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accura...Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.展开更多
该文选用宁夏枸杞作为试验材料,对枸杞样品经特定浓度的氰戊菊酯、氯氰菊酯、氯氟氰菊酯处理后,进行图像采集与光谱数据分析,并通过多种算法进行样本集划分、光谱预处理、特征波长提取及判别模型构建,最终建立针对菊酯类农药残留的定量...该文选用宁夏枸杞作为试验材料,对枸杞样品经特定浓度的氰戊菊酯、氯氰菊酯、氯氟氰菊酯处理后,进行图像采集与光谱数据分析,并通过多种算法进行样本集划分、光谱预处理、特征波长提取及判别模型构建,最终建立针对菊酯类农药残留的定量检测模型。结果表明,氯氟氰菊酯通过肯纳德‐斯通算法(Kennard‐Stone,KS)‐基线校准(Baseline)‐遗传偏最小二乘算法(genetic algorithm and partial least squares,GAPLS)‐卷积神经网络(convolutional neural network,CNN)建立的定量预测模型性能最优,校正集和预测集的相关系数分别为0.677、0.571,校正集和预测集的均方根误差分别为0.058、0.065;氰戊菊酯通过随机取样(random sampling,RS)‐原始光谱‐GAPLS‐CNN建立的定量预测模型效果最佳,校正集和预测集的相关系数分别为0.983、0.981,校正集和预测集的均方根误差分别为0.070、0.078;氯氰菊酯通过联合X‐Y距离(sample set partitioning based on joint X‐Y distances,SPXY)‐标准正态变量变换(standard normal variate,SNV)‐GAPLS‐CNN建立的定量预测模型效果最佳,校正集和预测集的相关系数分别为0.952、0.937,校正集和预测集的均方根误差分别为0.089、0.107。展开更多
基金the Program of the Science and Technology Commission of Ningxia Hui Autonomous Region and Joint Foundation of Seismological Science(197043).
文摘GPS observation network is deployed in the central part of Ningxia, which is the juncture of the Alxa block, Ordos block and Qinghai-Xizang (Tibet) block. Using the data of five phases of repeated survey sine 1996, the current state of crustal movement in the central part of Ningxia is analyzed. From the result, we can know the following. (1) In the period from Dec. 1996 to May 1999, the central part of Ningxia had the phenomenon of left-lateral movement about the west margin of Ordos (measuring station P2) and the Lingwu fault on the east of the Yinchuan basin displayed the mode of left-lateral reverse strike slip movement. In that region, the direction of the principal stress field was NNE-SSW (with an azimuth of 29.8?; the central part to the south of the measuring station P2 displaced eastward; the vertical deformation was obviously greater than the horizontal deformation in order of magnitude; the Yinchuan basin and Qinghai-Xizang (Tibet) block were in a state of rising; the measuring station P1 in the hinterland of Ordos showed a trend of subsiding year by year; and there may be a hidden fault to exist between the measuring points P3 and P4. (2) About one year before the occurrence of moderately strong earthquakes in the vicinity of the measuring region, deformation anomalies and abnormal changes of principal stress direction can be observed by the GPS measuring stations in that region; before moderately strong earthquakes near the measuring region and before strong earthquakes in adjacent regions, the simulated GPS deformation vector field ofthat region can betoken the approximate position of the coming earthquake. These results can be regarded as the eigenvalues of earthquake prediction for consideration.
基金Under the auspices of the National Natural Science Foundation of China(No.41571217)the National Key Research and Development Program of China(No.2016YFD0300801)
文摘Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.
文摘该文选用宁夏枸杞作为试验材料,对枸杞样品经特定浓度的氰戊菊酯、氯氰菊酯、氯氟氰菊酯处理后,进行图像采集与光谱数据分析,并通过多种算法进行样本集划分、光谱预处理、特征波长提取及判别模型构建,最终建立针对菊酯类农药残留的定量检测模型。结果表明,氯氟氰菊酯通过肯纳德‐斯通算法(Kennard‐Stone,KS)‐基线校准(Baseline)‐遗传偏最小二乘算法(genetic algorithm and partial least squares,GAPLS)‐卷积神经网络(convolutional neural network,CNN)建立的定量预测模型性能最优,校正集和预测集的相关系数分别为0.677、0.571,校正集和预测集的均方根误差分别为0.058、0.065;氰戊菊酯通过随机取样(random sampling,RS)‐原始光谱‐GAPLS‐CNN建立的定量预测模型效果最佳,校正集和预测集的相关系数分别为0.983、0.981,校正集和预测集的均方根误差分别为0.070、0.078;氯氰菊酯通过联合X‐Y距离(sample set partitioning based on joint X‐Y distances,SPXY)‐标准正态变量变换(standard normal variate,SNV)‐GAPLS‐CNN建立的定量预测模型效果最佳,校正集和预测集的相关系数分别为0.952、0.937,校正集和预测集的均方根误差分别为0.089、0.107。
