Αντιστροφή βυθοσκοπήσεων επαγόμενης πόλωσης στο πεδίο των χρόνων = Time domain inversion of induced polarization sounding data.
Περίληψη
The present thesis focuses on the development of Time Domain Inversion techniques of Induced Polarization Sounding Data (1D-TDIP). The developed algorithms are based on the use of the smoothness-constrained inversion to find the model correction. The 1D VES forward problem calculated using the digital linear filtering method. The basic principles of the induced polarization method are introduced and the solution to the forward problem for a 1D-time domain IP sounding is solved using the Pelton equation while a new variable of IP resistance is constructed. The perturbation technique was used to calculate the Jacobian matrix for the total system, whenever it was needed. The software was developed using MATLAB. Two inversion techniques were tested for the 1D-TDIP data: a) The first approach involved the distinct inversion of the VES data for each time window and the subsequent creation of chargeability curves for each layer of the inverted model from which the Cole Cole parameters are extracted using the Gauss Newton method. b) The second approach involves the direct inversion of all TD induced polarization data including also the DC measurements. All Cole-cole and resistivity parameters of all layers are now considered to be the unknown parameter vector. Test with the distinct inversion method, it was observed that there is a dependence of the result on the initial values of the Cole Cole parameters given to start the Gauss Newton method. For this reason, an improved empirical method was developed, based on the shape of the TD-IP curves, in which the Gauss Newton method is repeated with corrected initial values, giving a final solution which is less dependent on the initial parameter values. The performance and effectiveness of the proposed algorithms was checked using synthetic data produced from realistic models and real field data. The evaluation shows that the method with direct inversion of all data is effective with a smaller overall error than the other inversion method but does not always achieve a more accurate reconstruction of the model. The distinct inversion method with the improvement for non-dependence on initial variable values is effective in perfect synthetic data but has difficulty finding a solution when an error of more than 3% is added. In fact, in synthetic data where an error of 6% was added, it cannot find a solution. It also presents similar issues when applied in real data. The method of distinct inversion without the improvement of the intimal values can deal with synthetic data, which suffer from high-level noise, but in some cases, it cannot converge to a reliable solution. For real data, the method of direct inversion is generally more robust, but still there is a possibility that it cannot reach to a reliable inverted model.
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