TU-AB-201-09: Calibration of An Element of a New Directional Pd-103 Planar Source Array

2015 ◽  
Vol 42 (6Part31) ◽  
pp. 3595-3596
Author(s):  
M Aima ◽  
W Culberson ◽  
J Reed ◽  
L DeWerd
Keyword(s):  
Planar Source ◽  
Source Array

Sound Field Synthesis on Flat Panels Using a planar Source Array Controlled by its Active and Reactive Radiation Modes

2019 ◽  
Vol 105 (1) ◽  
pp. 139-151 ◽  
Author(s):  
Clinton André Merlo ◽  
Alexander Mattioli Pasqual ◽  
Eduardo Bauzer Medeiros
Keyword(s):  
Sound Field ◽  
Planar Source ◽  
Source Array

Vertical Source Array Development.

1995 ◽  
Author(s):  
W. S. Hodgkiss
Keyword(s):  
Source Array

Near-Field Transverse Resolution in Planar Source Reconstructions

2021 ◽  
pp. 1-1
Author(s):  
Maria Antonia Maisto ◽  
Rocco Pierri ◽  
Raffaele Solimene
Keyword(s):  
Near Field ◽  
Planar Source ◽  
Transverse Resolution

scholarly journals Evaluation of carbon nanotube x‐ray source array for stationary head computed tomography

2020 ◽  
Author(s):  
Derrek Spronk ◽  
Yueting Luo ◽  
Christina R. Inscoe ◽  
Yueh Z. Lee ◽  
Jianping Lu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Carbon Nanotube ◽  
X Ray ◽  
Head Computed Tomography ◽  
Source Array

Acquisition/Processing: Machine learning-based deblending: Dispersed source array data example

2021 ◽  
Vol 40 (10) ◽  
pp. 759-767
Author(s):  
Rolf H. Baardman ◽  
Rob F. Hegge
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Low Frequency ◽  
Seismic Interpretation ◽  
Salt Dome ◽  
Training Data ◽  
Array Data ◽  
Seismic Processing ◽  
Processing Machine ◽  
Source Array

Machine learning (ML) has proven its value in the seismic industry with successful implementations in areas of seismic interpretation such as fault and salt dome detection and velocity picking. The field of seismic processing research also is shifting toward ML applications in areas such as tomography, demultiple, and interpolation. Here, a supervised ML deblending algorithm is illustrated on a dispersed source array (DSA) data example in which both high- and low-frequency vibrators were deployed simultaneously. Training data pairs of blended and corresponding unblended data were constructed from conventional (unblended) data from another survey. From this training data, the method can automatically learn a deblending operator that is used to deblend for both the low- and the high-frequency vibrators of the DSA data. The results obtained on the DSA data are encouraging and show that the ML deblending method can offer a good performing, less user-intensive alternative to existing deblending methods.

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