Full Zone Apparent Resistivity Definition of Semi-airborne Transient Electromagnetic Method

2013 ◽  
Author(s):  
Ying-ying Zhang ◽  
Xiu Li ◽  
Zhi-peng Qi
Keyword(s):  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic ◽  
Definition Of

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.

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

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

scholarly journals Quantitative Calculation of Aquifer Water Quantity Using TEM Data

2017 ◽  
Vol 21 (1) ◽  
pp. 51
Author(s):  
Lanying Huang ◽  
Shengdong Liu ◽  
Bo Wang ◽  
Fubao Zhou
Keyword(s):  
Coal Mine ◽  
Mine Water ◽  
Apparent Resistivity ◽  
Electromagnetic Method ◽  
Water Quantity ◽  
Water Volume ◽  
Water Yield ◽  
Projection Area ◽  
Transient Electromagnetic Method ◽  
Transient Electromagnetic

Mine water is a menace to coal mining. Mine water quantity is generally detected by drilling in the coal mine roadway, and the disadvantage is high workload and low efficiency. Therefore, transient electromagnetic method (TEM) was proposed, and TEM data was processed to detect the water yield property of a targeted layer in coal mine. Through a mine-oriented 3D Transient Electromagnetic Method observing system, the signal of induced voltage is obtainable. Transient Electromagnetic Method 3D data volume can be calculated through the calculation of all-time resistivity and time-depth conversion. After an appropriate apparent resistivity value is set, the spatial distribution range of an aquifer can be determined. Then, with water-filling coefficient of the aquifer, its water quantity can be estimated. The water yield property detection results in the No.4 coal seam goaf of the No.80101 workface in Jude Mine of Shanxi, China, demonstrates that the apparent resistivity of this goaf is less than 3 Ω.m, and the projection area of low-resistivity anomaly zone is 22,383 m2. By using the formula Q=KMS, we can estimate that the water volume is 33,574 m3. Three boreholes have been constructed for the later dredging and drainage project, which results in a total water yield of 33,089 m3. The error percentage of the predicted water quantity is less than 1.5%. It can thus be concluded that it is feasible to predict aquifer water content with TEM data. Cálculo cuantitativo del volumen de aguas subterráneas a través del Método Electromagnético Transitorio (TEM)ResumenEl agua al interior de las minas es una amenaza para la minería de carbón. La cantidad del agua en las minas se detecta al perforar en las vías del socavón, con la desventaja de la carga de trabajo y la baja eficacia que significan. Por esto, el presente trabajo propone el Método Electromagnético Transitorio (TEM, del inglés Transient Electromagnetic Method) y utiliza la información generada para detectar las propiedades de producción de agua en una capa específica de una mina de carbón. A través de un sistema de observación tridimensional TEM orientado a minas se puede obtener la señal del voltaje inducido. La capacidad de la información del sistema TEM tridimensional se puede calcular al obtener la resistividad y la conversión tiempo-profundidad. Tan pronto se establece el valor apropiado de la resistividad aparente se puede determinar el rango de distribución de un acuífero. Luego, con el coeficiente de saturación del acuífero se puede calcular la cantidad de agua. Los resultados de la detección de las propiedades de producción de agua en la mina abandonada número cuatro de frente de trabajo número 80101 de la mina Jude, en Shanxi, China, demostró que la resistividad aparente de esta excavación es de 3 Ω.m, y el área de proyección de la zona anómala de baja resistividad es 22.383 m2. Al usar la fórmula Q=KMS, se estimó que el volumen de agua es de 33,574 m3. Tres pozos se han construido para el próximo proyecto de dragado y drenaje, cuyos resultados de producción de agua es de 33,089 m3. El porcentaje de error de la predicción de la cantidad de agua es menor al 1.5 %. Se concluye, por ende, que es factible producir el contenido de agua de un acuífero con la información del TEM.

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