Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network

2021 ◽  
Vol 26 (1) ◽  
pp. 71-77
Author(s):  
Weiqiang Liu ◽  
Rujun Chen ◽  
Liangyong Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parameter Estimation ◽  
Model Parameters ◽  
Near Surface ◽  
Complex Resistivity ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Artificial Neural ◽  
Cole Model

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.

Resistivity-depth imaging of airborne transient electromagnetic method based on an artificial neural network

2020 ◽  
Author(s):  
Jifeng Zhang ◽  
Bing Feng ◽  
Dong Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Electromagnetic Response ◽  
Model Parameters ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Artificial Neural

<p>An artificial neural network, which is an important part of artificial intelligence, has been widely used to many fields such as information processing, automation and economy, and geophysical data processing as one of the efficient tools. However, the application in geophysical electromagnetic method is still relatively few. In this paper, BP neural network was combined with airborne transient electromagnetic method for imaging subsurface geological structures.</p><p>We developed an artificial neural network code to map the distribution of geologic conductivity in the subsurface for the airborne transient electromagnetic method. It avoids complex derivation of electromagnetic field formula and only requires input and transfer functions to obtain the quasi-resistivity image section. First, training sample set, which is airborne transient electromagnetic response of homogeneous half-space models with the different resistivity, is formed and network model parameters include the flight altitude and the time constant, which were taken as input variables of the network, and pseudo-resistivity are taken as output variables. Then, a double hidden layer BP neural network is established in accordance with the mapping relationship between quasi-resistivity and airborne transient electromagnetic response. By analyzing mean square error curve, the training termination criterion of BP neural network is presented. Next, the trained BP neural network is used to interpret the airborne transient electromagnetic responses of various typical layered geo-electric models, and it is compared with those of the all-time apparent resistivity algorithm. After a lot of tests, reasonable BP neural network parameters were selected, and the mapping from airborne TEM quasi-resistivity was realized. The results show that the resistivity imaging from BP neural network approach is much closer to the true resistivity of model, and the response to anomalous bodies is better than that of all-time apparent resistivity numerical method. Finally, this imaging technique was use to process the field data acquired by the airborne transient method from Huayangchuan area. Quasi-resistivity depth section calculated by BP neural network and all-time apparent resistivity is in good agreement with the actual geological situation, which further verifies the effectiveness and practicability of this algorithm.</p>

Deformation Prediction and Inversion of Shuibuya Project Based on Artificial Neural Network and Genetic Algorithm

2012 ◽  
Vol 170-173 ◽  
pp. 2115-2118 ◽  
Author(s):  
Ling Cao ◽  
Zhen Biao Zhan ◽  
Yong Han
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Permanent Deformation ◽  
Model Parameters ◽  
Rock Fill ◽  
Artificial Neural ◽  
One Year ◽  
And Inversion ◽  
Concrete Face

he application of artificial neural network and genetic algorithm is made into the Shuibuya concrete face rock-fill dam project. At the beginning of design phase, genetic algorithm was used to predict the deformation of the dam; One year after the completion of construction, the rheological constitutive model parameters of Shuibuya concrete face rock-fill dam (CFRD) was inverted based on the monitoring data. And the permanent deformation of the dam was computed with the help of artificial neural network and genetic algorithm. The study result not only can accurately grasp the characteristic of Shuibuya CFRD, but also is propitious for the advancement of the computation theory about superhigh project.

scholarly journals Generalization Enhancement of Artificial Neural Network For Turbulence Closure By Feature Selection

2021 ◽  
Author(s):  
Linyang Zhu ◽  
Weiwei Zhang ◽  
Guohua Tu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Validation Dataset ◽  
Model Parameters ◽  
Feature Subset ◽  
Universal Method ◽  
Feature Importance ◽  
Artificial Neural

Abstract Feature selection targets for selecting relevant and useful features, and is a vital challenge in turbulence modeling by machine learning methods. In this paper, a new posterior feature selection method based on validation dataset is proposed, which is an efficient and universal method for complex systems including turbulence. Different from the priori feature importance ranking of the filter method and the exhaustive search for feature subset of the wrapper method, the proposed method ranks the features according to the model performance on the validation dataset, and generates the feature subsets in the order of feature importance. Using the features from the proposed method, a black-box model is built by artificial neural network (ANN) to reproduce the behavior of Spalart-Allmaras (S-A) turbulence model for high Reynolds number (Re) airfoil flows in aeronautical engineering. The results show that compared with the model without feature selection, the generalization ability of the model after feature selection is significantly improved. To some extent, it is also demonstrated that although the feature importance can be reflected by the model parameters during training process, artificial feature selection is still very necessary.

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