Due to the strong electromagnetic interferences and human interference,traditional electromagnetic methods cannot obtain high quality resistivity data of mineral deposits in Chinese mines.The wide field electromagneti...Due to the strong electromagnetic interferences and human interference,traditional electromagnetic methods cannot obtain high quality resistivity data of mineral deposits in Chinese mines.The wide field electromagnetic method(WFEM),in which the pseudo-random signal is taken as the transmitter source,can extract high quality resistivity data in areas with sever interference by only measuring the electric field component.We use the WFEM to extract the resistivity information of the Dongguashan mine in southeast China.Compared with the audio magnetotelluric(AMT)method,and the controlled source audio-frequency magnetotelluric(CSAMT) method,the WFEM can obtain data with higher quality and simpler operations.The inversion results indicate that the WFEM can accurately identify the location of the main ore-body,which can be used for deep mine exploration in areas with strong interference.展开更多
Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard...Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.展开更多
The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and e...The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.展开更多
In order to interpret the vertical electrical sounding data more reliably and effectively in the case of lacking proper priori information, two inverse schemes are proposed to invert combined re- sistivity and induced...In order to interpret the vertical electrical sounding data more reliably and effectively in the case of lacking proper priori information, two inverse schemes are proposed to invert combined re- sistivity and induced polarization data by using particle swarm optimization technique. Based on the computational formula of induced polarization, the inversion for chargeability/polarizability data can be transformed into inverting equivalent resistivity data. Then, the inversion for combined data can be decomposed into two procedures： inverting resistivity data and inverting equivalent resistivity data. A sequential inversion scheme is presented to run the two procedures sequentially. Contrast to the se- quential scheme, a simultaneous one is proposed to invert resistivity and induced polarization data si- multaneously. Both the sequential and simultaneous schemes are performed via centered-progressive particle swarm optimization algorithm for more exploratory purpose. Numerical experiments show that both the designed inversion algorithms can invert resistivity and induced polarization data suc- cessfully with fast convergence and high accuracy, even performed in a large search space. The inverse results are comparable to the results from generalized linear method. As an approximate importance sampler, the particle swarm optimization based algorithm can provide posterior analysis conveniently. We employ the posterior probability distributions of inverted model parameters to evaluate the per- formance and uncertainty of inversion. The posterior analysis and further field data testing show that the proposed inversion algorithms perform good sampling of the equivalence region and make sure that the global optimum can locate in the high probability areas.展开更多
基金Project(2018YFC0807802)supported by the National Key R&D Program of ChinaProject(41874081)supported by the National Natural Science Foundation of China
文摘Due to the strong electromagnetic interferences and human interference,traditional electromagnetic methods cannot obtain high quality resistivity data of mineral deposits in Chinese mines.The wide field electromagnetic method(WFEM),in which the pseudo-random signal is taken as the transmitter source,can extract high quality resistivity data in areas with sever interference by only measuring the electric field component.We use the WFEM to extract the resistivity information of the Dongguashan mine in southeast China.Compared with the audio magnetotelluric(AMT)method,and the controlled source audio-frequency magnetotelluric(CSAMT) method,the WFEM can obtain data with higher quality and simpler operations.The inversion results indicate that the WFEM can accurately identify the location of the main ore-body,which can be used for deep mine exploration in areas with strong interference.
基金Projects(41874145,72088101)supported by the National Natural Science Foundation of China。
文摘Prticle swarm optimization(PSO)is adopted to invert the self-potential anomalies of simple geometry.Taking the vertical semi-infinite cylinder model as an example,the model parameters are first inverted using standard particle swarm optimization(SPSO),and then the searching behavior of the particle swarm is discussed and the change of the particles’distribution during the iteration process is studied.The existence of different particle behaviors enables the particle swarm to explore the searching space more comprehensively,thus PSO achieves remarkable results in the inversion of SP anomalies.Finally,six improved PSOs aiming at improving the inversion accuracy and the convergence speed by changing the update of particle positions,inertia weights and learning factors are introduced for the inversion of the cylinder model,and the effectiveness of these algorithms is verified by numerical experiments.The inversion results show that these improved PSOs successfully give the model parameters which are very close to the theoretical value,and simultaneously provide guidance when determining which strategy is suitable for the inversion of the regular polarized bodies and similar geophysical problems.
基金Project(41574123)supported by the National Natural Science Foundation of ChinaProject(2015zzts250)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2013FY110800)supported by the National Basic Research Scientific Program of China
文摘The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.
基金supported by the National Natural Science Foundation of China(No.41574123)
文摘In order to interpret the vertical electrical sounding data more reliably and effectively in the case of lacking proper priori information, two inverse schemes are proposed to invert combined re- sistivity and induced polarization data by using particle swarm optimization technique. Based on the computational formula of induced polarization, the inversion for chargeability/polarizability data can be transformed into inverting equivalent resistivity data. Then, the inversion for combined data can be decomposed into two procedures： inverting resistivity data and inverting equivalent resistivity data. A sequential inversion scheme is presented to run the two procedures sequentially. Contrast to the se- quential scheme, a simultaneous one is proposed to invert resistivity and induced polarization data si- multaneously. Both the sequential and simultaneous schemes are performed via centered-progressive particle swarm optimization algorithm for more exploratory purpose. Numerical experiments show that both the designed inversion algorithms can invert resistivity and induced polarization data suc- cessfully with fast convergence and high accuracy, even performed in a large search space. The inverse results are comparable to the results from generalized linear method. As an approximate importance sampler, the particle swarm optimization based algorithm can provide posterior analysis conveniently. We employ the posterior probability distributions of inverted model parameters to evaluate the per- formance and uncertainty of inversion. The posterior analysis and further field data testing show that the proposed inversion algorithms perform good sampling of the equivalence region and make sure that the global optimum can locate in the high probability areas.
