Date of Award

2016

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Geophysics (MS)

Administrative Home Department

Department of Geological and Mining Engineering and Sciences

Advisor 1

Wayne D. Pennington

Advisor 2

Roohollah Askari

Committee Member 1

Gregory P. Waite

Abstract

Ambient noise seismic interferometry (ANSI) has been applied widely for geophysical investigations including earthquake tomography, civil engineering and seismic exploration purposes. Comparing this approach with the traditional active seismic survey, the application of ANSI is cost effective, environmentally friendly and easily repeatable. Conventional seismic interferometry by cross correlating wavefields recorded at different receivers has already obtained fruitful results.

Even though the application of seismic interferometry (SI) by cross correlation has been successful, different methods for the processing workflow such as cross coherence and deconvolution have been conducted in an effort to improve the resolution. While these three methods have been evaluated for shear wave imaging of the near surface using surface waves by other authors, no conclusive study has been performed to compare the results from these methods for reflection surveys. In this study, by considering three common methods of retrieving a virtual seismic record, I compare the methods and analyze the results with respect to their signal-to-noise ratios.

I applied ANSI to numerically modeled data to retrieve reflection responses for both base and repeat surveys monitoring the time-lapse changes of the impedance at the top of a reservoir before and after CO2 injection. The retrieved seismic response by the three methods including cross correlation, deconvolution and cross coherence are also compared for the field noise data recorded near the CO2 storage site in Ketzin, Germany. While all three provide adequate results in noise-free synthetic data examples, the cross coherence method yielded improved images using real data.


Share

COinS