Thread: Session: Reliability of the Green's function extracted from ambient noise

Started: 2016-07-22 01:34:24
Last activity: 2016-07-22 01:34:24
Topics: AGU Meetings
Dear colleagues,

We would like to draw your attention to our AGU session “Reliability of the Green’s function extracted from ambient noise” (session ID 12831). In this session, we discuss the extraction of reliable information from ambient noise based on cross correlation techniques. All theoretical and methodological approaches are welcome. The detailed session description is attached below.

We are very proud of inviting Francisco Sanchez-Sesma (UNAM) and Loic Viens (University of Tokyo).

Abstracts can be submitted via the AGU Fall Meeting website ( ). The deadline is 3 August.

Please, feel free to forward that message to anyone who could be interested by this session.

Best regards,
Nori Nakata (Stanford University)
Zack Spica (Stanford University)


Session Description:

Ambient-noise based seismological techniques have been used for understanding the 3D/4D Earth’s properties such as structure, stiffness, and attenuation in various scales. The key for the precise estimation of these parameters is the accuracy of the Green’s function extracted, which relies on, for example, ambient-noise energy distribution, receiver coverage, and appropriate signal processing.

In this session, we focus on theoretical and methodological approaches for extracting more accurate Green’s functions, and their applications. We invite contributions of novel processing and data acquisition (e.g., C3, double beamforming, multi-dimensional deconvolution (MDD), or very dense receiver array) to overcome azimuthal variation of ambient noise energy. Presentations related to amplitudes extraction, higher-mode surface waves, Love waves, or body waves in addition to the phases of the fundamental-mode Rayleigh waves are highly encouraged. We also welcome studies of the usage of multi-component signals for ambient-noise correlation, which can decompose different wave types.

Page built 03:57:10 | v.10e76c12