eprintid: 11743
rev_number: 13
userid: 740
dir: disk0/00/01/17/43
datestamp: 2012-06-04 16:52:40
lastmod: 2017-06-04 05:15:09
status_changed: 2012-06-04 16:52:40
type: thesis_degree
metadata_visibility: show
contact_email: rrg11@pitt.edu
item_issues_count: 0
eprint_status: archive
creators_name: Gutierrez, Ronald R.
creators_email: rrg11@pitt.edu
creators_id: RRG11
title: Discrimination of Bedform Scales Using Robust Spline Filters and Wavelet Transforms: Methods and Application to Synthetic Signals and the Rio Parana, Argentina
ispublished: unpub
divisions: sch_eng_civilenvironmental
full_text_status: public
keywords: River bedforms, wavelet analysis
abstract: Currently, there is no standard nomenclature and procedure to systematically identify the scale and magnitude of bedforms such as bars, dunes and ripples that are commonly present in many sedimentary environments. This thesis proposes a standardization of the nomenclature and symbolic representation of bedforms, and details the combined application of robust spline filters and continuous wavelet transforms to discriminate these morphodynamic features, namely bedform hierarchies (BHs). The proposed methodology for bedform discrimination is applied to synthetic bedform signals, which are sampled at a Nyquist ratio interval of 5 to 100 and a signal-to-noise ratio interval of 1 to 20, and to a detailed 3D bed survey of the Rio Parana, Argentina, which exhibits large-scale dune bedforms with superimposed, smaller bedforms. After discriminating the synthetic bedform signals into 3 BHs that represent bars, dunes and ripples, the accuracy of the methodology is quantified by estimating the reproducibility, the cross correlation and the standard deviation ratio of the actual and retrieved signals. For the case of the field measurements, the proposed method is used to discriminate small and large dunes; and subsequently, obtain and statistically analyze the common morphological descriptors such as wavelength, slope, and amplitude for both stoss and lee sides of these different size bedforms. The analysis of the synthetic signals demonstrates that the Morlet wavelet function is the most efficient in retrieving smaller periodicities such as ripples and that the proposed methodology effectively discriminate the waves of different periodicities scales for Nyquist ratios higher than 50 and signal-to-noise ratios. The analysis of the bedforms of the Parana River reveals that in most cases, a Gamma probability distribution (with a positive skewness) best describes the dimensionless wavelength and amplitude for both the lee and stoss sides of large dunes. For the case of the smaller superimposed dunes, the dimensionless wavelength shows a discrete behavior governed by the sampling frequency of the data, and the dimensionless amplitude better fits the Gamma probability distribution, again with a positive skewness.
date: 2012-06-04
date_type: published
pages: 61
institution: University of Pittsburgh
refereed: TRUE
etdcommittee_type: committee_chair
etdcommittee_type: committee_member
etdcommittee_type: committee_member
etdcommittee_type: committee_member
etdcommittee_type: committee_member
etdcommittee_name: Abad, Jorge D.
etdcommittee_name: Liang, Xu
etdcommittee_name: Budny, Daniel
etdcommittee_name: Rizzo, Piervincenso
etdcommittee_name: Langendoen, Eddy
etdcommittee_email: jabad@pitt.edu
etdcommittee_email: xuliang@pitt.edu
etdcommittee_email: budny@pitt.edu
etdcommittee_email: pir3@pitt.edu
etdcommittee_email: Eddy.Langendoen@ARS.USDA.GOV
etdcommittee_id: JABAD
etdcommittee_id: XULIANG
etdcommittee_id: BUDNY
etdcommittee_id: PIR3
etdcommittee_id: 
etd_defense_date: 2012-03-28
etd_approval_date: 2012-06-04
etd_submission_date: 2012-04-06
etd_release_date: 2012-06-04
etd_access_restriction: 5_year
etd_patent_pending: FALSE
assigned_doi: doi:10.5195/pitt.etd.2012.11743
thesis_type: thesis
degree: MS
citation:   Gutierrez, Ronald R.  (2012) Discrimination of Bedform Scales Using Robust Spline Filters and Wavelet Transforms: Methods and Application to Synthetic Signals and the Rio Parana, Argentina.  Master's Thesis, University of Pittsburgh.    (Unpublished)  
document_url: http://d-scholarship-dev.library.pitt.edu/11743/1/ETD-RRGutierrez.pdf