基于压缩感知的快速 Bregman 地震数据重建方法.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	基于压缩感知的快速 Bregman 地震数据重建方法. (Chinese)
Alternate Title:	Reconstruction of seismic data with fast Bregman based on compressed sensing. (English)
Συγγραφείς:	孙小东, 李傲伟, 秦宁, 蒋润, 王敬伊, 赵亮, 孙耀庭
Πηγή:	Journal of China University of Petroleum; Aug2025, Vol. 49 Issue 4, p62-68, 7p
Θεματικοί όροι:	Compressed sensing, Curvelet transforms, Thresholding algorithms, Seismic surveys, Mathematical optimization, Iterative methods (Mathematics), Electronic data processing
Abstract (English):	Due to factors such as ground environment, equipment limitations, and costs, seismic data collected in the field often suffer from missing traces. Therefore, the rapid and effective reconstruction of missing seismic data is crucial. To address this issue, we propose a seismic data reconstruction method based on compressed sensing theory, utilizing a fast Bregman approach with multiscale and multidirectional curvelet transforms as the sparse basis. The Bregman method decomposes the solution of the L1 -norm minimization problem into a series of subproblems, which are efficiently and accurately solved using the fast iterative shrinkage-thresholding algorithm (FISTA), thereby achieving high-quality reconstruction of missing data. Experimental results demonstrate that the fast Bregman method based on compressed sensing can efficiently reconstruct complex synthetic seismic data and enhance the accuracy of iterative computations. Complared to LBM and FISTA methods, the proposed method achieves superior performance in both reconstruction efficiency and accuracy. [ABSTRACT FROM AUTHOR]
Abstract (Chinese):	受地面环境、设备及成本等因素的影响, 野外采集的地震数据往往存在缺失道, 快速有效地重建缺失地震数据十分重要。针对缺失道的地震数据, 根据压缩感知理论, 提出一种快速 Bregman 方法的地震数据重建方法, 并采用多尺度、多方向曲波变换作为稀疏基。通过 Bregman 方法将求解 L1 范数问题分解为一系列子问题, 引入快速迭代收缩阈值方法 (FISTA) 高效、准确地求解子问题, 从而实现对缺失数据的高质量重构。结果表明, 基于压缩感知的快速 Bregman 方法可以对构造复杂的地震数据进行高效的重建, 并且提高迭代计算的重建精度。对于缺失地震数据的重建, 所提方法在效率和精度方面均高于 LBM 和 FISTA 方法。 [ABSTRACT FROM AUTHOR]
	Copyright of Journal of China University of Petroleum is the property of China University of Petroleum and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων:	Complementary Index

καταχωρήστε σχόλιο πρώτοι!