Field Research Potential Field Source Identification And Abnormal Separation Method Based On Wavelet Transform

The gravity and magnetic survey are the important geophysical methods. Theseparation and source identification of potential field are two key problems of gravityand magnetic field data processing. The classical separation method in frequencydomain can separate anomaly caused by sources of different depths and scaleseffectively. But they have some disadvantages, such as incomplete separation and cannot separate potential field with spatial localization, due to the lack of spatialinformation of Fourier transform. The wavelet transform, which has the ability ofmulti-resolution analysis and spatial localization, has been expected to solve theproblems presented above.In this paper, after the basic theory of wavelet analysis has been introduced, thecausing sources identification and separation of potential field have been studied. Themain results are listed as follow:1. The applicability of the identification of causing sources of potential field withContinuous Wavelet Transform(CWT) method, the principal of the choice of motherwavelet, the inference of noise and the choice of scaling factor have been studied,using several designed typical models, after the basic theory is re-derived in detail.2. The quantitative indictor, Sparse Index(SI), for choosing the preferentialmother wavelet, and the potential field separation method based on Discrete WaveletTransform (DWT) with spatial-scale localization have been proposed. The range ofwavelet coefficients in the spatial-scale domain corresponding to the regional andlocal anomaly respectively is determined by means of wavelet time-frequencyanalysis to the potential field data. The regional and local anomaly can bereconstructed using the wavelet coefficients within the range determined aboverespectively, and then the potential field separation with spatial-scale localization isaccomplished. The numerical results show that the total field anomaly can beseparated into regional anomaly and several isolated local ones, and the accuracy ofthe separation result of potential field can be improved obviously by the method ofpotential field separation with spatial-scale localization with the preferable mother wavelet chosen by the indicator SI.3. The preferable spatial-varying filtering method in wavelet domain has beenproposed based on the theories of the scale filtering in wavelet domain, Wienerfiltering and Green equivalent layer. The proposed method can solve the potentialfield separation problem whose local spectrum is different to global one obviouslybecause the spatial-varying filter parameter vary with position. The numerical resultsshow that the proposed method is as better as Butterworth filter and preferential filtermethod in the case of local spectrum is similar to global one, while the proposedmethod is better than the Butterworth filter and preferential filter method in the caseof local spectrum is much different to global one.4. The comparing experiments between identification the causing source afterseparation of potential field, and identification the causing source directly has beenmade. The conclusion can be drawn that separating the potential field into isolatedlocal anomaly before identifying using separation method based on DWT withspatial-scale localization can improve the accuracy of identification result of bothlocal and regional sources which interferes each other before separation. Theconclusion is verified by the field data processing result.