Properties of near surface marine sediments from wavelet correlation analysis

Abstract

[1] We present the first results from applying the wavelet correlation analysis to study physical properties of near top marine sediments. This technique applied to the synthetic and field data yields well resolved group velocity dispersion curves for the first two shear modes and surface waves. This method can be potentially useful for detecting of different seismic modes at frequencies masked by other neighboring modes. This novel technique is superior to traditional cross-correlation and Fourier analysis for inferring the physical properties of near surface marine sediments, such as density, porosity variations in depth and distance under diverse field conditions.

Seismic Surface-Wave Tomography of Waste Sites

“…The method uses a wavefield transformation data processing technique to detect phase velocities of Rayleigh waves, and a conventional inversion technique for shear-wave velocity versus depth. The results are comparable to those of SASW and the multi-channel analysis of surface waves, but still does not offer the three-dimensional resolution of tomography A wavelet correlation analysis technique for resolving group velocity dispersion curves has been presented by Kritski et al (2002). They claim that the technique is superior to traditional cross-correlation and Fourier analysis for defining the phase velocity, particularly where higher modes may overlap.…”

Interface Waves

“…Single-sensor method, which can be used to study S-wave speed variations as function of distance (Kritski, 2002), estimates group speed dispersion of one trace at a time from…”

Time-series analysis of a heterolithic, ripple-laminated deposit (Early Jurassic, Tilje Formation) and implications for reservoir modelling

“…If the accuracy of the applied wavelet is good enough, all variations of periodic oscillations in the data will be imaged and can be further interpreted and examined to define their role in the physical phenomena which is underlying the observed data. In our study we applied the wavelet processing algorithm developed in Kritski et al (2002). The algorithm is based on the well-known Morlet type wavelet and proved its applicability for processing seismic traces with complex periodic structure.…”

“…Continuous wavelet transform of function f (s) at time (or depth location) b relative to the wavelet base function ψ(s) at period scale a is given by (Kritski et al, 2002):…”