Surface wave dispersion inversion using an energy likelihood function

TL;DR

This paper introduces a Bayesian inversion method using an energy likelihood function for seismic surface wave dispersion analysis, improving accuracy and efficiency by avoiding phase velocity picking and better handling spectrum multimodalities.

Contribution

The study presents a novel energy likelihood function within a Bayesian framework for surface wave dispersion inversion, enhancing robustness against spectrum multimodalities and reducing the need for phase velocity picking.

Findings

More accurate shear velocity models compared to traditional methods.

Efficient application to real dense seismic datasets.

Reduces bias caused by spectrum multimodalities.

Abstract

Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis and by picking phase velocities from the obtained f-c spectrum. However, because of potential contamination the f-c spectrum often has multimodalities at each frequency for each mode. These introduce uncertainty and errors in the picked phase velocities, and consequently the obtained shear velocity structure is biased. To overcome this issue, in this study we introduce a new method which directly uses the spectrum as data. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied efficiently since it removes the need to pick phase velocities, and with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood. We therefore recommend using the energy likelihood function rather than explicitly picking phase velocities in surface wave dispersion inversion.