Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data

Simultaneous-source acquisition, breaking the limit of conventional seismic acquisition, is a rapidly evolving research field, due to its advantage in reducing survey time and improving data quality. The benefits of simultaneous-source acquisition are compromised by the intense blending interference...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2019-04, Vol.57 (4), p.2249-2262
Hauptverfasser:	Zu, Shaohuan, Zhou, Hui, Wu, Rushan, Mao, Weijian, Chen, Yangkang
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation Algorithms Approximation algorithms Computer simulation Constraint modelling Data Dictionaries Dictionary learning Frameworks Interference Iterative methods Learning Machine learning Noise Noise measurement Noise reduction noisy blended data Random noise Records Sparse matrices Sparsity sparsity promoting Surveying Transformations (mathematics)
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