Karst areas occupy about 14% of the world’s land and cause serious economic losses, which are estimated at dozens of billions dollars, set aside the physical danger. Among various geophysical methods that can be used to detect these objects at the first stage gravity and magnetic surveys were selected. However, complex host geological media and an unfavorable S/N ratio often do not enable to reveal the geophysical anomalies from the desired targets. Our approach to recognition of separate geophysical field peculiarities and geophysical integration consists in application of modern developments in the wavelet theory and data mining. Wavelet approach is applied for derivation of enhanced (e.g., coherence portraits) and combined images of geophysical fields simulated for the typical physical-geological models of karst development. The methodology based on the matching pursuit with wavelet packet dictionaries enables to extract desired signals even from strongly noised data. The recently developed technique of diffusion clustering combined with the abovementioned wavelet methods is utilized in to integrate the geophysical data and to detect existing irregularities in the subsurface structure. The most important factor is that these obtained results may be applied for revealing karst targets (on the basis of “learning” approach) from the field geophysical observations. The combination of the above approaches enables to create a new methodology, which enhances reliability and confidence of application of any individual geophysical method and geophysical method integration. This methodology could be an effective tool to recognizing not only hidden karst terranes, but also any typical geological (economic minerals, geological mapping, etc.), archaeological, ecological and other targets.
|Number of pages||11|
|Journal||ANAS Transactions, Earth Sciences|
|State||Published - 2014|