Robust Data Assimilation Using $L_1$ and Huber Norms
نویسندگان
چکیده
منابع مشابه
Robust data assimilation using L1 and Huber norms
Data assimilation is the process to fuse information from priors, observations of nature, and numerical models, in order to obtain best estimates of the parameters or state of a physical system of interest. Presence of large errors in some observational data, e.g., data collected from a faulty instrument, negatively affect the quality of the overall assimilation results. This work develops a sy...
متن کاملRobust inversion of seismic data using the Huber norm
The “Huber function” (or “Huber norm”) is one of several robust error measures which interpolates between smooth (l 2) treatment of small residuals and robust (l 1) treatment of large residuals. Since the Huber function is differentiable, it may be minimized reliably with a standard gradient-based optimizer. We propose to minimize the Huber function with a quasi-Newton method that has the poten...
متن کاملRobust and stable velocity analysis using the Huber function
The Huber function is an hybrid l1-l2 misfit measure. We demonstrate, on velocity analysis examples, that the Huber function is more robust to outlier effects, and more stable with respect to the number of iterations than the l2 norm. We also show that the Huber threshold which controls the transition between the l1 and the l2 norm may be chosen within a certain range of values without damaging...
متن کاملA robust l_1 penalized DOA estimator
The SPS-LASSO has recently been introduced as a solution to the problem of regularization parameter selection in the complex-valued LASSO problem. Still, the dependence on the grid size and the polynomial time of performing convex optimization technique in each iteration, in addition to the deficiencies in the low noise regime, confines its performance for Direction of Arrival (DOA) estimation....
متن کاملRobust blind methods using $\ell_p$ quasi norms
It was shown in a previous work that some blind methods can be made robust to channel order overmodeling by using the l1 or lp quasi-norms. However, no theoretical argument has been provided to support this statement. In this work, we study the robustness of subspace blind based methods using l1 or lp quasi-norms. For the l1 norm, we provide the sufficient and necessary condition that the chann...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2017
ISSN: 1064-8275,1095-7197
DOI: 10.1137/15m1045910