Numerical calculation of All-Time Apparent Resistivity for Central Loop Transient Electromagnetic Method

2003 ◽  
Vol 46 (5) ◽  
pp. 998-1010 ◽  
Author(s):  
Denghai BAI ◽  
Maxwell A Meju ◽  
Jian LU ◽  
Lifeng WANG ◽  
Zhaohai HE
Keyword(s):  
Numerical Calculation ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Central Loop

The Development of Near-source Electromagnetic Methods in China

2018 ◽  
Vol 23 (1) ◽  
pp. 115-124 ◽  
Author(s):  
Guo-qiang Xue
Keyword(s):  
High Efficiency ◽  
Academic Research ◽  
Electromagnetic Method ◽  
Detection Accuracy ◽  
Wide Field ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Detection Technology ◽  
Deep Depth ◽  
Central Loop

Near-source electromagnetic technology has been developed and applied in the exploration of petroleum, metallic ore, coal, and engineering geology due to its high efficiency, high detection accuracy, and deep depth of investigation. In this paper, research and applications of the frequency-domain electromagnetic sounding method (FDEM), wide-field electromagnetic method (WFEM), modified central-loop transient electromagnetic method (TEM), and short-offset grounded-wire TEM (SOTEM) with obvious near-source characteristics, were reviewed and analyzed. From the 1960s to 1990s, the FDEM method and equipment were extensively developed in China. These methods have played important roles in the exploration of coal resources. Based on controlled source audio-frequency magnetotelluric (CSAMT) and FDEM methods, a new method has been developed by deriving a new expression to calculate apparent resistivity. This method, which is referred to as WFEM, has been studied, applied, and received great attention in China. To increase work efficiency and reduce the influence of local transverse anisotropy on the detection processes, a modified central-loop TEM detection technology based on the central loop transient electromagnetic method was developed in China. The advantages of SOTEM in near-source surveys with high resolution and increased depth detection stimulated academic research interest to further develop grounded-wire TEM techniques. [Figure: see text]

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.

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>

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.

scholarly journals Research on Airborne Electromagnetic Whole-area Apparent Resistivity Imaging Algorithm in the Detection of Goaf in Rail Transit

2021 ◽  
Vol 2083 (4) ◽  
pp. 042072
Author(s):  
Yajuan Jia ◽  
Jianbo Zheng ◽  
Hongfang Zhou
Keyword(s):  
Magnetic Field ◽  
Field Strength ◽  
Magnetic Field Strength ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Transient Electromagnetic Method ◽  
Resistivity Imaging ◽  
Transient Electromagnetic ◽  
Air System ◽  
The Magnetic Field

Abstract Depth apparent resistivity imaging is an important process of data processing and analysis in the aviation transient electromagnetic method. It can provide reference value of conductor depth, vertical extension, and other information, and can accurately provide the measurement of each aviation transient electromagnetic measurement system. The structural section of the apparent conductivity of the one-dimensional layered medium on the line. As an advanced geophysical exploration technology, the aerial transient electromagnetic method has been applied significantly in the exploration of polymetallic minerals abroad in recent years. In this paper, based on the theory of ground-to-air transient electromagnetic method with multiple radiation sources, a corresponding multi-component global apparent resistivity definition method is established. The advantages of using the magnetic field strength to define the global apparent resistivity of the multi-radiation field source ground-air system are analysed. For each component of the magnetic field strength, respective global apparent resistivity algorithms are proposed to realize the multi-component, full-time, and full-space visual resistivity. The resistivity is calculated, and the influence of the offset on the global apparent resistivity is analysed. By adjusting the relative position of the source and the current direction and other parameters, the multi-radiation source transient electromagnetic ground-air system can not only strengthen the signal strength of different components, weaken random interference, but also better distinguish the location of underground anomalies

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