Off-campus Michigan Tech users: To download campus access theses or dissertations, please use the following button to log in with your Michigan Tech ID and password: log in to proxy server

Non-Michigan Tech users: Please talk to your librarian about requesting this thesis or dissertation through interlibrary loan.

Date of Award


Document Type

Campus 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

Committee Member 1

Gregory Waite

Committee Member 2

Roohollah Askari


A primary objective of the seismic data processing workflow is to improve the signal to noise ratio. A seismic record has many types of noise besides primary reflections which convey the vital information. A non-negligible part of these noises is multiple reflections causing difficulties and misunderstandings. This work examines filtering techniques with different methods and deconvolution technique in an effort to attenuate multiples on a 2D line of marine data from southwest of the Taiwan and compares of their results.

Prior to evaluating methods for attenuating multiples, basic seismic processing was applied to the data. This consisted of the following: zeroing bad traces, applying a spherical divergence correction, and band-pass filtering. The data were then sorted into common-mid-point (CMP) gathers. These CMP gathers were analyzed, and stacking velocities were determined so that Normal Move-out (NMO) processing and stacking can be applied.

Following this basic processing, two methods of multiple suppression were applied separately and evaluated: 1) filtering; 2) deconvolution. The filtering methods included stacking, frequency(f)-wavenumber(k) filtering and the Radon Transform methods were applied in an effort to separate multiples and primaries. Deconvolution was also utilized. Finally, the results of these approaches were discussed and compared with the goal of obtaining reasonable results. For this data set, it appears that the Radon Transform attenuates the long-period multiples better than the other approaches. Applying deconvolution on Radon-filtered data also shows better results. Stacked and migrated section of the data was considered as the final image.