Interpretation of multifrequency complex resistivity data for a layered Earth model

1988 ◽  
Vol 26 (4) ◽  
pp. 399-408 ◽  
Author(s):  
S.F. Mahmoud ◽  
S.G. Tantawi ◽  
J.R. Wait
Keyword(s):  
Earth Model ◽  
Complex Resistivity ◽  
Layered Earth ◽  
Resistivity Data

INTERPRETATION OF BURIED ELECTRODE RESISTIVITY DATA USING A LAYERED EARTH MODEL

Geophysics ◽  
1978 ◽  
Vol 43 (5) ◽  
pp. 988-1001 ◽  
Author(s):  
Jeffrey J. Daniels
Keyword(s):  
Direct Current ◽  
Field Data ◽  
Apparent Resistivity ◽  
Current Source ◽  
Well Logs ◽  
Earth Model ◽  
Layered Earth ◽  
Resistivity Data ◽  
True Resistivity ◽  
Resistivity Logging

The layered earth model is a fundamental interpretation aid for direct current resistivity data. This paper presents a solution for the layered earth problem for a buried current source and a buried receiver. The model is developed for source and receiver electrodes buried anywhere within a horizontally stratified layered earth containing an arbitrary number of resistivity layers. Model results for the normal well‐logging array indicate that large departures between true and apparent resistivity can be caused by thin beds or highly resistant layers. A true resistivity distribution from well logs can be established by modeling when the effects from borehole rugosity and fluid resistivity are negligible. The equations derived for resistivity well logs can be used to interpret hole‐to‐hole, hole‐to‐surface, and conventional surface array data. A field example demonstrates that deviations between hole‐to‐hole field data and model results, based on well logs in the receiver hole, can be accounted for by combining the resistivity logging models in the receiver holes with information from geologic logs. Differences between the field data and the layered‐model results are attributed to lateral changes between or near the source and receiver holes.

scholarly journals Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network

2010 ◽  
Vol 17 (1) ◽  
pp. 65-76 ◽  
Author(s):  
U. K. Singh ◽  
R. K. Tiwari ◽  
S. B. Singh
Keyword(s):  
Neural Network ◽  
Poor Performance ◽  
Optimization Methods ◽  
Model Complexity ◽  
Earth Model ◽  
Dc Resistivity ◽  
Ann Model ◽  
Jammu And Kashmir ◽  
Resistivity Data ◽  
Artificial Neural Network Ann

Abstract. The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.

Approximation to Cutoffs of Higher Modes of Rayleigh Waves for a Layered Earth Model

2009 ◽  
Vol 166 (3) ◽  
pp. 339-351 ◽  
Author(s):  
Yixian Xu ◽  
Jianghai Xia ◽  
Richard D. Miller
Keyword(s):  
Rayleigh Waves ◽  
Earth Model ◽  
Higher Modes ◽  
Layered Earth
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