Publication Date

5-2020

Date of Final Oral Examination (Defense)

3-6-2020

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Geophysics

Department

Geosciences

Supervisory Committee Chair

T. Dylan Mikesell, Ph.D.

Supervisory Committee Member

John Pelton, Ph.D.

Supervisory Committee Member

Lee M. Liberty, M.S.

Supervisory Committee Member

Jodi L. Mead, Ph.D.

Abstract

One uses seismic interferometry (SI) to recover Green’s functions (i.e. impulse response) from ambient seismic recordings and estimate surface-wave phase velocities to investigate subsurface structure. This method has been commonly used in the last 20 years because this method only utilizes ambient seismic recordings from seismic stations/sensors and does not rely on traditional seismic sources (e.g. earthquakes or active sources). SI assumes that the ambient seismic wavefield is isotropic, but this assumption is rarely met in practice. We demonstrate that, with linear-array spatial sampling of an anisotropic ambient seismic wavefield, SI provides a better estimate of Rayleigh-wave phase velocities than another commonly used ambient seismic method, the refraction microtremor (ReMi) method. However, even SI does not work in some extreme cases, such as when the out-of-line sources are stronger than the inline sources. This is because the recovered Green’s functions and surface-wave phase velocity estimations from SI are biased due to the anisotropic wavefield. Thus, we propose to use multicomponent data to mitigate this bias. The multicomponent data are vertical (Z) and radial (R) components, where the R direction is parallel to a line or great circle path between two sensors. The multicomponent data can deal with the extreme anisotropic source cases, because the R component is more sensitive to the in-line sources than the out-of-line sources, while the Z component possesses a constant sensitivity to sources in all directions.

Estimation of source distributions (i.e. locations and strengths) can aid correction of the bias in SI results, as well as enable the study of natural ambient seismic sources (e.g. microseism). We use multicomponent seismic data to estimate ambient seismic source distributions using full-waveform inversion. We demonstrate that the multicomponent data can better constrain the inversion than only the Z component data, due to the different source sensitivities between the Z and R components. When applying the inversion to field data, we propose a general workflow which is applicable for different field scales and includes vertical and multicomponent data. We demonstrate the workflow with a field data example from the CO2 degassing in Harstouˇsov, Czech Republic. We also apply the workflow to the seismic recordings in Antarctica during February 2010 and estimate the primary microseism source distributions.

The SI results include both direct and coda waves. While using the direct waves in investigating subsurface structure and estimating source distributions, one can utilize the coda waves to monitor small changes in the subsurface. The coda waves include multiply-scattered body and surface waves. The two types of waves possess different spatial sensitivities to subsurface changes and interact each other through scattering. We present a Monte Carlo simulation to demonstrate the interaction in an elastic homogeneous media. In the simulation, we incorporate the scattering process between body and Rayleigh waves and the eigenfunctions of Rayleigh waves. This is a first step towards a complete modelling of multiply-scattered body and surface waves in elastic media.

DOI

10.18122/td/1652/boisestate

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