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You are here: Home / Research / Publications / Seafloor Engineering and Characterization / Advances in Interpretation and Analysis Techniques for Spectral-Analysis-of-Surface-Waves (SASW) Measurements / B89

B89

Abstract ID#:
B89

 

Report Title:
Advances in Interpretation and Analysis Techniques for Spectral-Analysis-of-Surface-Waves (SASW) Measurements

 

Authors:
Sung-Ho Joh, University of Texas at Austin

 

Report Date:
January, 1997

Procedures for the interpretation and analysis of Spectral-Analysis-of-Surface-Waves (SASW) measurements were investigated. This work is part of a continuing effort to develop the SASW technique into a robust and user-friendly seismic technique for evaluating in situ stiffness profiles on-land and offshore. The data interpretation and analysis procedures which were studied are: 1. determination of the phase velocity dispersion curve, and 2. evaluation of an in situ shear wave velocity profile from the dispersion curve.

To determine the phase velocity dispersion curve, it is necessary to interpret the number of jumps in the wrapped phase spectrum. A new technique for interpreting the phase spectrum, called the impulse response filtration (IRF) technique, was developed. The IRF technique extracts only the desired wave group from the spectrum by filtering an impulse response determined for two receivers. The interpreted phase spectrum which provides phase information is not contaminated by other wave groups. Use of the IRF technique was verified both theoretically and experimentally.

In most SASW applications, the shear wave velocity profile is evaluated from the dispersion curve by an iterative forward modeling analysis. This procedure is a trial-and-error procedure which is very time- consuming and lacks systematic convergence. In this research, two robust inversion procedures (global and array inversions) based on the maximum likelihood approach were developed. The algorithm can be used to evaluate expeditiously the shear wave velocity profile from the dispersion data and an optimum profile can be obtained.

For improved performance in the inversion analysis, a new algorithm to compact a composite experimental dispersion curve into a representative dispersion curve was also developed. The proposed algorithm employs a polynomial best-fit analysis using a data window overlapped with adjacent data windows.

As a result of this work, significant improvements were made in interpretation of the phase spectrum and in inversion of the dispersion curve. This work should contribute to advancing the SASW technique and making these measurements easier to analyze and more consistent in interpretation.

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