Multimodal phase velocity-frequency dispersion images using different MASW transformation techniques

TL;DR

This paper compares three transformation techniques (w-c, w-k, tau-p) for generating multimodal dispersion images from MASW data, analyzing their effectiveness and practical advantages using synthetic and field data.

Contribution

It provides a detailed comparison of three MASW transformation methods, highlighting their relative merits and practical implementation considerations.

Findings

w-c transform offers the clearest dispersion images

All three methods generally produce matching results

w-c does not require high sampling rate or zero padding

Abstract

Three different transformation techniques, namely, (i)w-c, (ii) w-k and (iii) tau-p, has been employed for generating multimodal dispersion images on the basis of multi-channel analysis of surface waves (MASW) data recorded in distance-time domain; here w= circular frequency, c = phase velocity, tau = time intercept, p = phase slowness (1/c) and k = wavenumber. All the three methods have been first clearly described. The results from these three different transforms have been examined by using synthetic as well as field data obtained from field tests using 48 geophones. The effect of sensor spread length (X) and geophone numbers (M) on multimodal dispersion images were examined. The solutions from these three transforms were found to match generally well with each other. The w-c transform has been noted to provide the most clarity since it does not require either high sampling rate as normally needed for the tau-p method or inclusion of the zero padding of the data in a distance domain for the w-k approach. The paper will be useful since it not only describes the methods, but it brings out simultaneously their merits in implementation and for generating the dispersion images.