Seismic Interferometry in Linear Radon Domain Applying to Noise Passive Data

We explored a new method for noise passive data to retrieve the virtual-source gathers in linear Radon domain. Seismic interferometry based on the cross correlation algorithm can be viewed as cancellation of a common part of a raypath from a physical source to two different receivers, and a stacking...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Hauptverfasser: Wang, Xiannan, Zhang, Jian, Guo, Cean, Zhao, Shuang, Cheng, Hao
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We explored a new method for noise passive data to retrieve the virtual-source gathers in linear Radon domain. Seismic interferometry based on the cross correlation algorithm can be viewed as cancellation of a common part of a raypath from a physical source to two different receivers, and a stacking operator is then applied to the crosscorrelagram to take advantage of the contributions from all the sources. As the contributions of retrieving the virtual-shot gathers mainly come from the stationary-phase point, limited passive data with the same ray-parameter plays the major role. That is, the interference effects of most of passive data are counteracted through stacking with each other. Seismic interferometry in linear-Radon domain is proposed for selecting traces with the same ray parameters to retrieve the virtual-shot gathers. As the common-receiver gathers are needed by linear-Radon interferometry, a rearranged operator is used to rearrange noise passive data according to the periodicity. In this way, the common-receiver gathers of noise passive data can be obtained. Then, the common-receiver gathers are transformed into linear-Radon domain and the selected-ray range is used for cross correlation, which can save the computation time significantly. That has important practical significance in processing noise passive data.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3088141