In geophysical research it is an important objective to find more accurate measuring and more effective data processing methods. Measurement data always contain noise, which can mislead the interpreter, or hide useful information. The often used traditional DFT algorithm shows low noise rejection capability. On the other hand there are robust methods to solve the overdetermined inverse problem with excellent noise rejection capabilities. Therefore we suggest a new inversion based Fourier transformation method, where the continuous frequency spectrum is discretized with series expansion and the series expansion coefficients as model parameters are determined in the framework of the Iteratively Reweighted Least Squares (IRLS) algorithm using the so-called Cauchy-Steiner weights. In this paper the method was tested on noisy synthetic magnetic data generated above two magnetic bodies. The results prove the successful applicability of our inversion based S-IRLS-FT algorithm.


Article metrics loading...

Loading full text...

Full text 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