Introducing <span style="" class="text typewriter">noisi</span>: a Python tool for ambient noise cross-correlation modeling and noise source inversion

Ermert, Laura; Igel, Jonas; Sager, Korbinian; Stutzmann, Eléonore; Nissen-Meyer, Tarje; Fichtner, Andreas

doi:https://doi.org/10.5194/se-11-1597-2020

Articles | Volume 11, issue 4

https://doi.org/10.5194/se-11-1597-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/se-11-1597-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 4

Method article

|

28 Aug 2020

Method article |

| 28 Aug 2020

Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion

Laura Ermert, Jonas Igel, Korbinian Sager, Eléonore Stutzmann, Tarje Nissen-Meyer, and Andreas Fichtner

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Final revised paper (published on 28 Aug 2020)
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Preprint (discussion started on 13 May 2020)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'seismology', Cornelis Wapenaar, 28 May 2020
- AC1: 'reply', Laura Ermert, 23 Jun 2020
RC2: 'Review of Ermert et al.', Erdinc Saygin, 01 Jun 2020
- AC2: 'reply', Laura Ermert, 23 Jun 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Laura Ermert on behalf of the Authors (25 Jun 2020) Author's response Manuscript

ED: Publish as is (03 Jul 2020) by Michal Malinowski

ED: Publish as is (05 Jul 2020) by CharLotte Krawczyk (Executive editor)

AR by Laura Ermert on behalf of the Authors (07 Jul 2020) Author's response Manuscript

Short summary

We present an open-source tool to model ambient seismic auto- and cross-correlations with spatially varying source spectra. The modeling is based on pre-computed databases of seismic wave propagation, which can be obtained from public data providers. The aim of this tool is to facilitate the modeling of ambient noise correlations, which are an important seismologic observable, with realistic wave propagation physics. We present a description and benchmark along with example use cases.