Estimates on the possible annual seismicity of Venus
Estimates on the possible annual seismicity of Venus

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2023JE008048. This is version 7 of this Preprint.

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Authors

Iris van Zelst , Julia Maia, Ana-Catalina Plesa, Richard Ghail, Moritz Spühler

Abstract

There is a growing consensus that Venus is seismically active, although its level of seismicity could be very different from that of Earth due to the lack of plate tectonics. Here, we estimate upper and lower bounds on the expected annual seismicity of Venus by scaling the seismicity of the Earth. We consider different scaling factors for different tectonic settings and account for the lower seismogenic zone thickness of Venus. We find that 95 - 296 venusquakes equal to or bigger than moment magnitude (Mw) 4 per year are expected for an inactive Venus, where the global seismicity rate is assumed to be similar to that of continental intraplate seismicity on Earth. For the active Venus scenarios, we assume that the coronae, fold belts, and rifts of Venus are currently seismically active. This results in 1,161 - 3,609 venusquakes >= M_w4 annually as a realistic lower bound and 5,715 - 17,773 venusquakes >= Mw4 per year as a maximum upper bound for an active Venus. 

DOI

https://doi.org/10.31223/X5DQ0C

Subjects

Planetary Geophysics and Seismology

Keywords

Venus, Seismicity, Earthquakes, planets

Dates

Published: 2023-03-31 04:45

Last Updated: 2024-07-26 09:40

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The Jupyter Notebooks used to make the results and plot the figures as well as the CMT database and geospatial vector data (shapefiles) of the tectonic setting areas on Earth can be found in a zenodo link to be finalised upon acceptance. Explanation of individual files in this repository and additional figures and tables are provided in the Supplementary Material. Figures were made with Python and Adobe Illustrator. We used the colorblind friendly color map from the IBM Design Library.