1887

Abstract

Summary

Since the beginning of the nineteenth century, a significant evolution in optimization theory has been noticed. In the past lots of statistical algorithm have been used to invert apparent resistivity to get layer parameters. But these algorithm are not very stable with very wide range of values. This paper shows the application of a statistically sound algorithm called as neural network which is based on the analogy of human brain. In this paper we have used regularized neural network for the inversion purpose.The inverted model parameters was found to be independent of the search space, thereby showing the robustness of the algorithm.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201801737
2018-06-11
2024-03-29
Loading full text...

Full text loading...

References

  1. Calderon-Macias, C., Sen, M.K., & Stoffa, P.L.
    [2000] Artificial neural networks for parameter estimation in geophysics. Geophys. Prospect., 48, 21–47.
    [Google Scholar]
  2. Van der Baan, M. & Jutten, C.
    [2000] Neural networks in geophysical applications. Geophysics, 65, 1032–1047
    [Google Scholar]
  3. Koefoed, O;
    1970; A fast Method for Determining the Layer Distribution from the Raised Kernel Function of Geoelectrical Sounding; Geophysicsl Prospecting, 18, 564–570
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201801737
Loading
/content/papers/10.3997/2214-4609.201801737
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error