1996
DOI: 10.1061/(asce)0733-9453(1996)122:4(168)
|View full text |Cite
|
Sign up to set email alerts
|

Basic Concepts ofL1Norm Minimization for Surveying Applications

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
25
0

Year Published

2002
2002
2015
2015

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 37 publications
(25 citation statements)
references
References 4 publications
0
25
0
Order By: Relevance
“…Equation is solved by minimizing the L1 norm of the residuals. The advantage of the L1 norm over the more widely used L2 norm is that it is less sensitive to outliers and thus reduces their impact on the estimated parameters [ Marshall and Bethel , ]. To determine the temporal evolution of the volume change, we additionally use a linear Kalman filter [ Grewal and Andrews , ] to generate time series of the volume change for each PCDs [ Shirzaei and Walter , ].…”
Section: Methodsmentioning
confidence: 99%
“…Equation is solved by minimizing the L1 norm of the residuals. The advantage of the L1 norm over the more widely used L2 norm is that it is less sensitive to outliers and thus reduces their impact on the estimated parameters [ Marshall and Bethel , ]. To determine the temporal evolution of the volume change, we additionally use a linear Kalman filter [ Grewal and Andrews , ] to generate time series of the volume change for each PCDs [ Shirzaei and Walter , ].…”
Section: Methodsmentioning
confidence: 99%
“…To associate the daily time series of surface displacement with the kinematics of the spatiotemporal distribution of the afterslip, we implement a time-dependent inverse modeling scheme . This method consists of two main operators; (1) a robust optimization operator using the L1-norm minimization as a minimum spatial mean error estimator (Marshall and Bethel, 1996), and (2) a linear Kalman filter (LKF) (Grewal and Andrews, 2001) to generate the time series of the afterslip for each triangular dislocation of the fault-interface mesh as a minimum temporal mean square error estimator. These two steps are implemented iteratively (Shirzaei and Walter, 2010).…”
Section: Time-dependent Afterslip Modelmentioning
confidence: 99%
“…This provided us with a larger group of pixels (Figure 3c) which may be more affected by phase noise. This step‐wise increase in observation points did not lead to significant global unwrapping errors [ Marshall and Bethel , 1996], since we used an L1 norm minimization approach for phase unwrapping [ Costantini , 1998] and we reduced the spatial aliasing by removing the ADM. Hereafter we unwrapped the residual phase and restored the ADM, so that eventually the complete unwrapped data set was obtained (Figure 3e). As a test, we unwrapped the filtered interferograms without removing the ADM and noted significant unwrapping errors.…”
Section: Application To Llaima Volcanomentioning
confidence: 99%