This scheme is non-uniform azimuthally and may be biased toward the gridlines of the images, resulting in potential “footprints”. Thus, the structural features were still detected and connections made predominantly in two orthogonal directions within the image. During the first pass, the Y direction (north) was treated as the “time”, and prior to the second pass, the grid was transposed and the X direction (east) was treated as the “time”. In the applications of such algorithms to potential-field grids described by Eaton and Vasudevan (2004), this problem was alleviated by making two passes of pattern analysis. When applied to gridded potential-field data maps, a significant drawback of seismic-algorithm based skeletonization consists in one direction being preferred and treated as reflection travel-time.
More recently, Eaton and Vasudevan (2004) extended this method to aeromagnetic data by using detection on the basis of strike direction, event linearity, event amplitude and polarity. This technique was based on the so-called binary consistency-checking (BCC) scheme by Cheng and Lu (1989) however, the use of any particular ranking scheme is not important for skeletonization. Starting from the stronger seismic-reflection events, weaker events were identified and connected iteratively. In these approaches, pattern primitives, such as wavelet amplitudes, durations and polarities, were extracted from seismic traces and connected along the offset dimension according to certain similar features to form a coherent event. The original development of the skeletonization technique targeted automatic event picking in reflection seismic data (Le and Nyland, 1990 Lu and Cheng, 1990 Li et al., 1997). Introduction: Seismic and Potential-field Image Skeletonization This material is reproduced here, with modifications, with permission from the Saskatchewan Geological Survey.
The majority of the material in the following sections was originally published as Paper A-3 in Summary of Investigations 2012, Volume 1, Saskatchewan Geological Survey Miscellaneous Report 2012-4.1 (Gao and Morozov, 2012). This paper describes a new, azimuthally-uniform skeletonization approach to 2-D potential field data.
Automatic identification of such spatially-connected wavelets and measurement of their parameters is the general objective of skeletonization. The types of spatial dimensions of the images may also vary, ranging from the usual distances, elevations and depths to travel times and travel-time lags.
These features can be expressed by linear continuity, branching, amplitudes, widths, polarities, curvatures, orientations and/or other attributes and can be subdivided into “background trends” on top of which some kinds of “anomalies” or “wavelets” can be recognized. In this paper, we investigate one of such pattern-analysis approaches called “skeletonization”.Īlthough using different physical fields and models, many types of 2-D geophysical images, such as seismic sections and slices, gravity and magnetic maps, possess a number of similar geometrical features. In both of these cases, it is important to extract quantitative attributes useful for further data analysis and inversion for the subsurface structure. Automated pattern-recognition methods can be useful for both seismic and potential-field images. The volumes of modern gridded data collected by the geophysical exploration industry are often large and can benefit from methods for image decomposition, pattern analysis, and interpretation.
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