Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network
文献类型：期刊论文
| 作者 | Liu, Weiqiang2; Chen, Rujun1; Yang, Liangyong3 |
| 刊名 | JOURNAL OF ENVIRONMENTAL AND ENGINEERING GEOPHYSICS
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 26期号:1页码:71-77 |
| ISSN号 | 1083-1363 |
| DOI | 10.32389/JEEG20-054 |
| 英文摘要 | In near surface electrical exploration, it is often necessary to estimate the Cole-Cole model parameters according to the measured multi-frequency complex resistivity spectrum of ore and rock samples in advance. Parameter estimation is a nonlinear optimization problem, and the common method is least square fitting. The disadvantage of this method is that it relies on initial value and the result is unstable when data is confronted with noise interference. To further improve the accuracy of parameter estimation, this paper applied artificial neural network (ANN) method to the Cole-Cole model estimation. Firstly, a large number of forward models are generated as samples to train the neural network and when the data fitting error is lower than the error threshold, the training ends. The trained neural network is directly used to efficiently estimate the parameters of vast amounts of new data. The efficiency of the artificial neural network is analyzed by using simulated and measured spectral induced polarization data. The results show that artificial neural network method has a faster computing speed and higher accuracy in Cole-Cole model parameter estimation. |
| 资助项目 | Science Foundation of China University of Petroleum-Beijing[2462020YXZZ005] ; Science Foundation of China University of Petroleum-Beijing[2462020YJRC010] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000636767000008 |
| 出版者 | ENVIRONMENTAL ENGINEERING GEOPHYSICAL SOC |
| 资助机构 | Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing ; Science Foundation of China University of Petroleum-Beijing |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/101097]  |
| 专题 | 中国科学院地质与地球物理研究所 |
| 通讯作者 | Liu, Weiqiang |
| 作者单位 | 1.Cent South Univ, Sch Geosci & Infophys, Changsha 410012, Peoples R China 2.China Univ Petr Beijing CUP, Coll Geophys, Beijing 102249, Peoples R China 3.Chinese Acad Sci IGGCAS, Inst Geol & Geophys, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Weiqiang,Chen, Rujun,Yang, Liangyong. Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network[J]. JOURNAL OF ENVIRONMENTAL AND ENGINEERING GEOPHYSICS,2021,26(1):71-77. |
| APA | Liu, Weiqiang,Chen, Rujun,&Yang, Liangyong.(2021).Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network.JOURNAL OF ENVIRONMENTAL AND ENGINEERING GEOPHYSICS,26(1),71-77. |
| MLA | Liu, Weiqiang,et al."Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network".JOURNAL OF ENVIRONMENTAL AND ENGINEERING GEOPHYSICS 26.1(2021):71-77. |
入库方式： OAI收割
来源：地质与地球物理研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。