3d electromagnetic low induction number modeling using integral equations

In this paper, a code for 3d forward modeling of electromagnetic low induction number (emlin) data is developed based on the linear integral equations (ie). at first, the code is manipulated for a resistive block immersed in a homogenous background, and the obtained results have rms errors of 2% comparing with the previously standard published results, which demonstrates the productivity of the 3d forward modeling code. then, a model composed of two conductive anomalies with different depth ranges and conductivities in a resistive background is considered. ie forward reposes shows that the shallower block produce larger values in spite of having less conductivity.since the forward modeling is linear, the productivity of the forward modeling code depends on the efficiency of the forward operator. furthermore, linear forward operator plays the key role in the linear inversion procedure, therefore, a real data set of a thick dyke in bloemfontein nature reserve region in south africa is manipulated. weighted damped minimum length solution is utilized for the inversion procedure and the inverted model is demonstrative of the forward operator efficiency in practical applications.

3D Electromagnetic Low Induction Number Modeling using Integral Equations

In this paper, a code for 3D forward modeling of electromagnetic low induction number (EMLIN) data is developed based on the linear integral equations (IE). At first, the code is manipulated for a resistive block immersed in a homogenous background, and the obtained results have RMS errors of 2% comparing with the previously standard published results, which demonstrates the productivity of the 3D forward modeling code. Then, a model composed of two conductive anomalies with different depth ranges and conductivities in a resistive background is considered. IE Forward reposes shows that the shallower block produce larger values in spite of having less conductivity.Since the forward modeling is linear, the productivity of the forward modeling code depends on the efficiency of the forward operator. Furthermore, linear forward operator plays the key role in the linear inversion procedure, therefore, a real data set of a thick dyke in Bloemfontein Nature Reserve region in South Africa is manipulated. Weighted damped minimum length solution is utilized for the inversion procedure and the inverted model is demonstrative of the forward operator efficiency in practical applications.