Airborne electromagnetic transient method enjoys the advantages of high-efficiency and the high resolution of electromagnetic anomalies,especially suitable for mining detection around goaf areas and deep exploration o...Airborne electromagnetic transient method enjoys the advantages of high-efficiency and the high resolution of electromagnetic anomalies,especially suitable for mining detection around goaf areas and deep exploration of minerals.In this paper,we calculated the full-wave airborne transient electromagnetic data,according to the result of numerical research,the advantage of switch-off time response in electromagnetic detection was proofed via experiments.Firstly,based on the full-wave airborne transient electromagnetic system developed by Jilin University(JLU-ATEMI),we proposed a method to compute the full-waveform electromagnetic(EM)data of 3D model using the FDTD approach and convolution algorithm,and verify the calculation by the response of homogenous half-space.Then,through comparison of switch-off-time response and off-time response,we studied the effect of ramp time on anomaly detection.Finally,we arranged two experimental electromagnetic detection,the results indicated that the switch-off-time response can reveal the shallow target more effectively,and the full-waveform airborne electromagnetic system is an effective technique for shallow target detection.展开更多
Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter wit...Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.展开更多
To evaluate the performance of real time kinematic (RTK) network algorithms without applying actual measurements, a new method called geometric precision evaluation methodology (GPEM) based on covariance analysis was ...To evaluate the performance of real time kinematic (RTK) network algorithms without applying actual measurements, a new method called geometric precision evaluation methodology (GPEM) based on covariance analysis was presented. Three types of multiple reference station interpolation algorithms, including partial derivation algorithm (PDA), linear interpolation algorithms (LIA) and least squares condition (LSC) were discussed and analyzed. The geometric dilution of precision (GDOP) was defined to describe the influence of the network geometry on the interpolation precision, and the different GDOP expressions of above-mentioned algorithms were deduced. In order to compare geometric precision characteristics among different multiple reference station network algorithms, a simulation was conducted, and the GDOP contours of these algorithms were enumerated. Finally, to confirm the validation of GPEM, an experiment was conducted using data from Unite State Continuously Operating Reference Stations (US-CORS), and the precision performances were calculated according to the real test data and GPEM, respectively. The results show that GPEM generates very accurate estimation of the performance compared to the real data test.展开更多
基金Project(41674109) supported by the National Natural Science Foundation of China
文摘Airborne electromagnetic transient method enjoys the advantages of high-efficiency and the high resolution of electromagnetic anomalies,especially suitable for mining detection around goaf areas and deep exploration of minerals.In this paper,we calculated the full-wave airborne transient electromagnetic data,according to the result of numerical research,the advantage of switch-off time response in electromagnetic detection was proofed via experiments.Firstly,based on the full-wave airborne transient electromagnetic system developed by Jilin University(JLU-ATEMI),we proposed a method to compute the full-waveform electromagnetic(EM)data of 3D model using the FDTD approach and convolution algorithm,and verify the calculation by the response of homogenous half-space.Then,through comparison of switch-off-time response and off-time response,we studied the effect of ramp time on anomaly detection.Finally,we arranged two experimental electromagnetic detection,the results indicated that the switch-off-time response can reveal the shallow target more effectively,and the full-waveform airborne electromagnetic system is an effective technique for shallow target detection.
基金Project(20090162120084)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(08JJ4014)supported by the Natural Science Foundation of Hunan Province,China
文摘Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.
基金Project(61273055) supported by the National Natural Science Foundation of ChinaProject(CX2010B012) supported by Hunan Provincial Innovation Foundation for Postgraduate Students, ChinaProject(B100302) supported by Innovation Foundation for Postgraduate Students of National University of Defense Technology, China
文摘To evaluate the performance of real time kinematic (RTK) network algorithms without applying actual measurements, a new method called geometric precision evaluation methodology (GPEM) based on covariance analysis was presented. Three types of multiple reference station interpolation algorithms, including partial derivation algorithm (PDA), linear interpolation algorithms (LIA) and least squares condition (LSC) were discussed and analyzed. The geometric dilution of precision (GDOP) was defined to describe the influence of the network geometry on the interpolation precision, and the different GDOP expressions of above-mentioned algorithms were deduced. In order to compare geometric precision characteristics among different multiple reference station network algorithms, a simulation was conducted, and the GDOP contours of these algorithms were enumerated. Finally, to confirm the validation of GPEM, an experiment was conducted using data from Unite State Continuously Operating Reference Stations (US-CORS), and the precision performances were calculated according to the real test data and GPEM, respectively. The results show that GPEM generates very accurate estimation of the performance compared to the real data test.
