L1 norm constrained migration of blended data with the FISTA algorithm

2015 ◽  
Vol 12 (4) ◽  
pp. 620-628 ◽  
Author(s):  
Xinting Lu ◽  
Liguo Han ◽  
Jianglong Yu ◽  
Xue Chen
Keyword(s):  
L1 Norm ◽  
Geophysics ◽  
2021 ◽  
pp. 1-76
Author(s):  
Siyuan Chen ◽  
Siyuan Cao ◽  
Yaoguang Sun

In the process of separating blended data, conventional methods based on sparse inversion assume that the primary source is coherent and the secondary source is randomized. The L1-norm, the commonly used regularization term, uses a global threshold to process the sparse spectrum in the transform domain; however, when the threshold is relatively high, more high-frequency information from the primary source will be lost. For this reason, we analyze the generation principle of blended data based on the convolution theory and then conclude that the blended data is only randomly distributed in the spatial domain. Taking the slope-constrained frequency-wavenumber ( f- k) transform as an example, we propose a frequency-dependent threshold, which reduces the high-frequency loss during the deblending process. Then we propose to use a structure weighted threshold in which the energy from the primary source is concentrated along the wavenumber direction. The combination of frequency and structure-weighted thresholds effectively improves the deblending performance. Model and field data show that the proposed frequency-structure weighted threshold has better frequency preservation than the global threshold. The weighted threshold can better retain the high-frequency information of the primary source, and the similarity between other frequency-band data and the unblended data has been improved.


2019 ◽  
Author(s):  
Philipp Kremer ◽  
Damian Brzyski ◽  
Malgorzata Bogdan ◽  
Sandra Paterlini
Keyword(s):  

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 65091-65100
Author(s):  
Ayyad Maafiri ◽  
Omar Elharrouss ◽  
Saad Rfifi ◽  
Somaya Ali Al-Maadeed ◽  
Khalid Chougdali

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.


2018 ◽  
Vol 98 ◽  
pp. 32-49 ◽  
Author(s):  
Chu-Dong Pan ◽  
Ling Yu ◽  
Huan-Lin Liu ◽  
Ze-Peng Chen ◽  
Wen-Feng Luo

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