Receiver Deghosting with Blended Data Using Sparse Inversion

2020 ◽  
Author(s):  
J. Cao ◽  
G. Blacquière
Keyword(s):  
Sparse Inversion ◽  
Blended Data

Separation of seismic blended data by sparse inversion over dictionary learning

2014 ◽  
Vol 106 ◽  
pp. 146-153 ◽  
Author(s):  
Yanhui Zhou ◽  
Wenchao Chen ◽  
Jinghuai Gao
Keyword(s):  
Dictionary Learning ◽  
Sparse Inversion ◽  
Blended Data

Deblending based on the frequency-structure weighted threshold: The case of the slope-constrained f-k transform

Geophysics ◽  
2021 ◽  
pp. 1-76
Author(s):  
Siyuan Chen ◽  
Siyuan Cao ◽  
Yaoguang Sun
Keyword(s):  
High Frequency ◽  
Primary Source ◽  
Performance Model ◽  
Secondary Source ◽  
L1 Norm ◽  
Frequency Information ◽  
Frequency Structure ◽  
Global Threshold ◽  
Sparse Inversion ◽  
Blended Data

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.

Using surface multiples to estimate primaries by sparse inversion from blended data

2010 ◽  
Vol 59 (1) ◽  
pp. 10-23 ◽  
Author(s):  
G.J.A. Van Groenestijn ◽  
D.J. Verschuur
Keyword(s):  
Sparse Inversion ◽  
Blended Data

An amplitude-preserving deblending approach for simultaneous sources

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. V185-V196 ◽  
Author(s):  
Chengbo Li ◽  
Charles C. Mosher ◽  
Yongchang Ji
Keyword(s):  
Seismic Data ◽  
Line Search ◽  
Inversion Problem ◽  
Coherent Noise ◽  
Amplitude Variation With Offset ◽  
Alternating Direction ◽  
Line Search Techniques ◽  
Sparse Inversion ◽  
Improved Performance ◽  
Blended Data

A goal of simultaneous shooting is to acquire high-quality seismic data more efficiently, while reducing operational costs and improving acquisition efficiency. Effective sampling and deblending techniques are essential to achieve this goal. Inspired by compressive sensing (CS), we have formulated deblending as an analysis-based sparse inversion problem. We solve the inversion problem with an algorithm derived from the classic alternating direction method (ADM), associated with variable splitting and nonmonotone line-search techniques. In our testing, the analysis-based formulation together with nonmonotone ADM algorithm provides improved performance compared with synthesis-based approaches. A major issue for all deblending approaches is how to deal with real-world variations in seismic data caused by static shifts and amplitude imbalances. We evaluate the concept of including static and amplitude corrections obtained from surface-consistent solutions into the deblending formulation. We implement solutions that use a multistage inversion scheme to overcome the practical issues embedded in the field-blended data, such as strong coherent noise, statics, and shot-amplitude variations. The combination of these techniques gives high-fidelity deblending results for marine and land data. We use two field-data examples acquired with simultaneous sources to demonstrate the effectiveness of the proposed approach. Imaging and amplitude variation with offset quantitative analysis are carried out to indicate the amplitude-preserving character of deblended data with this methodology.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

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.

scholarly journals Deblending by direct inversion

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. A9-A12 ◽  
Author(s):  
Kees Wapenaar ◽  
Joost van der Neut ◽  
Jan Thorbecke
Keyword(s):  
Inverse Problem ◽  
Seismic Data ◽  
Iterative Procedure ◽  
Inversion Process ◽  
Direct Inversion ◽  
Source Data ◽  
Band Limited ◽  
Blended Data ◽  
Simultaneous Source ◽  
Additional Constraints

Deblending of simultaneous-source data is usually considered to be an underdetermined inverse problem, which can be solved by an iterative procedure, assuming additional constraints like sparsity and coherency. By exploiting the fact that seismic data are spatially band-limited, deblending of densely sampled sources can be carried out as a direct inversion process without imposing these constraints. We applied the method with numerically modeled data and it suppressed the crosstalk well, when the blended data consisted of responses to adjacent, densely sampled sources.

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