Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 13 6 2006 10.5194/npg-13-661-2006 http://www.nonlin-processes-geophys.net/13/661/2006/ http://www.nonlin-processes-geophys.net/13/661/2006/npg-13-661-2006.html http://www.nonlin-processes-geophys.net/13/661/2006/npg-13-661-2006.pdf 661 669 2006-11-28 An algorithm for detecting layer boundaries in sediments K. Bube bube@icbm.de T. Klenke U. Feudel Institut für Chemie und Biologie des Meeres, Carl von Ossietzky Universität Oldenburg, Postfach 2503, 26111 Oldenburg, Germany In this paper we present an algorithm based on wavelet multiscale decomposition, designed to detect lines of maximal gradients in horizontal direction within two-dimensional data sets. The algorithm is capable of identifying layer boundaries within sediment profiles, as demonstrated for artificial as well as two field data sets. Layers are detected with a good resolution within (i) digital images of a deep sea sediment core (IODP-expedition 301, core 15H) and (ii) chemical concentration patterns of recent tidal sediments (North Sea). Block, A., von Bloh, W., Klenke, T., and Schellnhuber, H J.: Multifractal Analysis of the Microdistribution of Elements in Sedimentary Structures Using Images From Scanning Electron Microscopy and Energy Dispersive X Ray Spectroscopy, J. Geophys. Res., 96, 223–230, 1991. Canny, J.: A Computational Approach to Edge Detection, IEEE Trans. Patt.\ Anal. Mach. Intell., 8, 679–698, 1986. Capilla, C.: Application of the Haar wavelet transform to detect microseismic signal arrivals, J. Appl. Geophys., 59, 36–46, 2006. Cooper, G.: Interpreting potential field data using continuous wavelet transforms of their horizontal derivatives, Comput. Geosci., 32, 984–992, 2006. Daubechies, I.: Ten Lectures on Wavelets, vol 61 of CBMS–NSF Regional Conference Series in Applied Mathematics, SIAM Publications, Philadelphia, 1992. Expedition 301 Scientists: Expedition 301 summary, in: Proc. IODP, edited by: Fisher, A., Urabe, T., Klaus, A., and the Expedition 301~Scientists, vol. 301, Integrated Ocean Drilling Program Management International, Inc., College Station TX, \mbox\doi10.2204/iodp.proc.301.101.2005, 2005. Fedi, M., Paoletti, V., and Rapolla, A.: The role of multilevel data in potential field interpretation, Comput. Geosci., 31, 681–688, 2005. Gerdes, G., Krumbein, W E., and Reineck, H.-E.: The depositional record of sandy, versicolored tidal flats (Mellum island, southern North Sea), J. Sediment. Petrol., 55, 265–278, 1985. Gunninga, J. and Glinsky, M.: Wavelet extractor: A Bayesian well-tie and wavelet extraction, Comput. Geosci., 32, 681–695, 2006. Louis, A K., Maaß, P., and Rieder, A.: Wavelets, Teubner, Stuttgart, 1998. Mallat, S G.: A Theory for Multiresolution Signal Decomposition: The wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell., 11, 674–693, 1989a. Mallat, S G.: Multiresolution Approximations and Wavelet Orthonormal Bases of L²(R), Trans. Amer. Math. Soc., 315, 69–87, 1989b. Maroni, C.-S., Quinquis, A., and Vinson, S.: Horizon Picking on Subbottom Profiles Using Multiresolution Analysis, Digital Signal Processing, 11, 269–287, 2001. Niebuhr, B. and Prokoph, A.: Periodic-cyclic and chaotic successions of Upper Cretaceous (Cenomanian to Campanian) pelagic sediments in the North German Basin, Cretaceous Research, 18, 731–750, 1997. Sahimi, M.: Fractal-wavelet neural-network approach to characterization and upscaling of fractured reservoirs, Comput. Geosci., 26, 877–905, 2000.