Seismic Ambient Noise Source Maps

SANS is a computational workflow to invert for the noise source distribution of the secondary microseisms on a regional to global scale. The workflow consists of three main steps:

  1. Data download and pre-processing
  2. Cross-correlation computation
  3. Finite-frequency sensitivity kernel inversion

A more in-depth explanation of the workflow and the publications that this research is based on can be found here. Because the noise sources we invert for, namely the secondary microseisms, are generated by overlapping ocean waves, the strength depends on the wave height. Hence, we also show the significant wave height maps for the daily maps.

All steps are run automatically on a daily basis for different station lists to create noise source maps for different areas. Currently the areas we invert for are:

  1. North Atlantic
  2. Global

Every morning at 4am the ETH web server sends a command to Piz Daint, a computer at the Swiss National Supercomputing Centre, where all available data is automatically downloaded, processed, and cross-correlated. Subsequently, we run 8 iterations for two seperate inversions: one for stations surrounding the North Atlantic and one for a global station distribution. All of this requires roughly 100 node hours. Once the inversions are completed, the output is collected and plotted before being copied to the ETH server to present it on this website.

Daily SANS workflow. All times are in CET.

Below you can find an animation of the final inversion iterations of the last 10 days for a global station list. For more information you can go to the science section or checkout some of our publications.

To check the daily noise source maps, go through the different iterations, obtain more information about each inversion, and download the inversion files and noise source models you can go to our daily maps section. The earliest available date is the 1st September 2021.

Feel free to contact us if you have any further questions or would like to collaborate.

Global noise source maps for the past 10 days



Tolman, H.L. & Chalikov, D. (1996) Source terms in a third-generation wind wave model. J. Phys. Oceanogr.<2497:STIATG>2.0.CO;2

Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C. & Wassermann, J. (2015) ObsPy: A bridge for seismology into the scientific Python ecosystem. Comput. Sci. Discov., 8, 0–17, IOP Publishing.

Ermert, L., Sager, K., Afanasiev, M., Boehm, C. & Fichtner, A. (2017) Ambient Seismic Source Inversion in a Heterogeneous Earth: Theory and Application to the Earth’s Hum. J. Geophys. Res. Solid Earth, 122, 9184–9207.

Ermert, L., Igel, J.K.H., Sager, K., Stutzmann, E., Nissen-Meyer, T. & Fichtner, A. (2020) Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion. Solid Earth, 11, 1597–1615.

Bowden, D., Sager, K., Fichtner, A. & Chmiel, M. (2020) Connecting Beamforming and Kernel-based Source Inversion. Geophys. J. Int., 1–14.

Igel, J.K.H., Ermert, L.A. & Fichtner, A. (2021) Rapid finite-frequency microseismic noise source inversion at regional to global scales. Geophys. J. Int., 227, 169–183, Oxford University Press.

If you are using this framework in your research, please cite the following:

Igel, J. K. H., Bowden, D. C. & Fichtner, A. (2023) SANS: Publicly Available Daily Multi-Scale Seismic Ambient Noise Source Maps. Journal of Geophysical Research: Solid Earth: e2022JB025114.


We would like to thank the Swiss National Supercomputing Centre for their constant support. And thanks to ETH, specifally the IT Department, for helping us setup the website on their servers.

The seismic data was collected from multiple data centers and the authors thank everyone involved in setting up and maintaining these: IRIS (, GEOFON (, ORFEUS (, NIEP (, RESIF (, INGV (, SCEDC (, BGR (, ETH (, KOERI (, LMU (, NCEDC ( The stationlists including all the network codes and locations can be found in the resources.