Resistivity-depth Imaging with the Airborne Transient Electromagnetic Method Based on an Artificial Neural Network

2020 ◽  
Vol 25 (3) ◽  
pp. 355-368
Author(s):  
Bing Feng ◽  
Ji-feng Zhang ◽  
Dong Li ◽  
Yang Bai
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

We developed an artificial neural network to map the distribution of geologic conductivity in the earth subsurface using the airborne transient electromagnetic method. The artificial neural network avoids the need for complex derivations of electromagnetic field formulas and requires only input and transfer functions to obtain a quasi-resistivity image. First, training sample set from the airborne transient electromagnetic response of homogeneous half-space models with different resistivities was formed, and network model parameters, including the flight altitude, time constant, and response amplitude, were determined. Then, a double-hidden-layer back-propagation (BP) neural network was established based on the mapping relationship between quasi-resistivity and airborne transient electromagnetic response. By analyzing the mean square error curve, the training termination criterion of the BP neural network was determined. Next, the trained BP neural network was used to interpret the airborne transient electromagnetic responses of various typical layered geo-electric models, and the results were compared with that from the all-time apparent resistivity algorithm. The comparison indicated that the resistivity imaging from the BP neural network approach was much closer to the true resistivity of the model, and the response to anomalous bodies was better than that from an all-time apparent resistivity. Finally, this imaging technique was used to process field data acquired by employing the airborne transient method from the HuaYin survey area. Quasi-resistivity depth sections calculated with the BP neural network and the actual geological situation were in good.

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>

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

The Application of TEM on Detecting Mine Goaf

2014 ◽  
Vol 1073-1076 ◽  
pp. 2153-2157
Author(s):  
Yong Jun Li ◽  
Xiao Ming Li ◽  
Ting Chen
Keyword(s):  
Water Content ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Transient Electromagnetic Method ◽  
Electrical Characteristics ◽  
Geophysical Prospecting ◽  
Secondary Field ◽  
Low Resistivity ◽  
Transient Electromagnetic

Transient electromagnetic method is one of the geophysical prospecting methods to detect mine goaf. The paper analyzes the unique electrical characteristics of the stratum containing goaf. TEM inverts the apparent resistivity and delineates the mine goaf and determines water content by observing the pure secondary field. The method is sensitive to geologic bodies of low resistivity and has higher resolution. The paper takes some one mine in Shanxi as example to prove the practicability and effectiveness of TEM in production. It has certain reference significance in detecting mine goaf.

scholarly journals TEM apparent resistivity imaging for grounding grid detection using artificial neural network

2019 ◽  
Vol 13 (17) ◽  
pp. 3932-3940 ◽  
Author(s):  
Shanqiang Qin ◽  
Yao Wang ◽  
Heng-Ming Tai ◽  
Haowen Wang ◽  
Xian Liao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Apparent Resistivity ◽  
Resistivity Imaging ◽  
Grounding Grid ◽  
Artificial Neural

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 Subsurface object recognition in a soil using UWB irradiation by Bow-Tie antenna and artificial neural network

2018 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fdtd Method ◽  
Antenna System ◽  
Modern World ◽  
Field Energy ◽  
Important Place ◽  
Transient Electromagnetic ◽  
Artificial Neural ◽  
Bow Tie

Background: Subsurface radiolocation problems have an important place in the modern world, such as in geology, building, and humanitarian demining. A complex problem that impedes the widespread use of subsurface radars is the processing and interpretation of the parameters of the reflected electromagnetic field. Objectives: The main purpose of this work is to solve the problem of recognition of objects buried in a soil by bow-tie antenna and artificial neural network (ANN). Materials and methods: The problem of recognition an ideally conducting cylindrical object that is situated below the earth's surface is solved by an ANN. The air-ground interface is irradiated by a bow-tie antenna, which is excited by means of a nanosecond impulse current. The irradiation by nearly point-like source in contrast to plane transient electromagnetic wave incidence considered in our previous works is characterized by the significant decrease of field energy reached a hidden object, reflected, and received by antenna. Moreover, the descent of the field energy becomes more sensible proportionally to the distance from the object to the radar. The complications can call into question the possibility the application of the approach on the base of ANN. The electromagnetic problem is solved numerically by using the FDTD method. The time dependences of amplitudes of differently polarized electric field components, which were obtained in four points above the earth's surface were used as the initial data. The points form the shape of a square. The ANN was trained by the obtained data to determine the position of the object beneath the ground. Results: ANN recognition quality was tested by test data with the addition of Gaussian noise and data obtained when the receiving system is moved relative to the object by shift of the value that was absent in training set. Conclusion: Such type of antenna system in combination with the ANN shows good results for determining the distance to the object even in the presence of noise.

Prediction of Mine Inrush Water Based on BP Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Mine Water ◽  
Influential Factors ◽  
Water Inflow ◽  
Impact Factors ◽  
Artificial Neural ◽  
Inrush Water ◽  
The Impact

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

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