Evaluating Transient Electromagnetic Data in a High Noise Environment

2012 ◽  
Author(s):  
Daniel Friedman ◽  
Becky Hollingshaus ◽  
Benjamin Lee ◽  
Collin Wilson ◽  
Paul Bedrosian ◽  
...  
Keyword(s):  
High Noise ◽  
Noise Environment ◽  
Electromagnetic Data ◽  
Transient Electromagnetic

Micro-motion Targets Resolution in a High Noise Environment

2010 ◽  
Vol 32 (11) ◽  
pp. 2630-2635
Author(s):  
Yong-sheng Guan ◽  
Qun-sheng Zuo ◽  
Hong-wei Liu
Keyword(s):  
High Noise ◽  
Noise Environment ◽  
Micro Motion

Micro-motion targets resolution in a high noise environment

2009 ◽  
Author(s):  
Yongsheng Guan ◽  
Hongwei Liu ◽  
Feng Chen
Keyword(s):  
High Noise ◽  
Noise Environment ◽  
Micro Motion

scholarly journals An Express Algorithm for Transient Electromagnetic Data Interpretation

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 354 ◽  
Author(s):  
Roman Kaminskyj ◽  
Nataliya Shakhovska ◽  
Gregus Michal ◽  
Borys Ladanivskyy ◽  
Lidia Savkiv
Keyword(s):  
Experimental Data ◽  
Spline Approximation ◽  
Numerical Differentiation ◽  
Data Interpretation ◽  
Continuous Functions ◽  
Electromagnetic Data ◽  
Polynomial Coefficients ◽  
Transient Electromagnetic ◽  
1D Model ◽  
Tem Method

The transient electromagnetic (TEM) method is a time-domain, controlled source, electromagnetic (EM) geophysical technique which is often applied to image the subsurface conductivity distributions of shallow layers due to its effectiveness and adaptability to complex site working conditions. The means for an express analysis of such experimental data in several practical cases have advantages and are suitable for use. We developed our approach for determining the approximate one-dimensional (1D) model of background conductivity based on the formal transformation of the TEM experimental data and the mathematical analysis of continuous functions. Our algorithm, which allows the 1D model’s parameters to be obtained in terms of a layer’s thickness and resistivity, widely utilizes the numerical differentiation of experimental curves as well as of transformed ones. Since the noise level increases with time in the attenuating TEM signals and differentiation even enhances it, special procedures are required to calculate the derivative values. We applied the piecewise cubic spline approximation to solve this problem. In that case, the derivatives are obtained using polynomial coefficients which are available for each node. The application of the created facilities is demonstrated using real experimental data of the TEM soundings.

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