عنوان مقاله [English]
Determination of boundary surface structures is commonly used for data interpretation. Horizontal and vertical derivatives are useful tool for determining the edges boundary. Using horizontal and vertical derivative in signal analysis method; that is effective method for interpretation of edges boundary, have been used commercially.
In acquisition potential field data, using different methods of interpolation for transform data to the regular network and applying different filters on the regular network, then the edges of anomalies can be determined. In usual methods, applying the filters directly on interpolated data in the regular network. The result of this method it is reflecting that, increased the accuracy in determining the edges boundary of data anomalies, and causes the amplified noise also; finally the results are very complicated and it will be difficult to interpret.
In this paper it is suggested that the first three spectrum of image (an image that has three main frequency spectrum red, blue and green) are prepared on a regular network of interpolated data, because each color of spectrogram have a defined wavelength and own frequency ranges. It's possible that breakdown potential field anomaly grid to three frequency spectrum, and applying different filters on each spectrogram. Using this technique for edge detection a potential field anomaly data, causes the noise and other unwanted elements that haven't continuous spectrum, only amplified in specific range and not match with results of other spectrum (concept that the color code produce for storing in particular cell or pixel of image), so for discontinuous spectrum we have tree color cod for tree main spectrum that not match together and formation a meaningless color cod for storage in specific pixel. In this situation we have black color showing. For continuous spectrogram in tree main spectrum, tree spectrum match together and formation a meaningful color cod for storage in specific pixel. In this situation we have white color showing; usually potential field data have continuous spectrogram so this technique be able to decrease the noise effect and increase the accurately edge detection a potential field of anomaly data. In this method only the strongest range of continuous spectrum amplified, so the complexities of other factors that make difficult interpret filtered. Apply this method has two advantages, first that unwanted factors such as noise, which haven't continuous range spectrum deleted and second the color spectrum that have continuous behavior (such as the survey anomaly) in three color spectra, depending on the filter used, amplified and improving the filter results.
In this paper, we used eight filters with usual method and color spectrum analysis method, this filters shows the boundary and limited area of survey anomaly. Results shows the usual method have very complicated for interpretation but the color spectrum analysis method with elimination noise and discontinuous spectrum improve the result of potential field edge detection.
Here, firstly this method applied on synthetic model with five percentage Gaussian noise and twenty percentage inverse Gaussian noise, then applied on the magnetic data of Ojat-Abad iron ore deposit, in Semnan.