Earthquake source inversion by integrated fiber-optic sensing

Nils Müller; Sebastian Noe; Dominik Husmann; Jacques Morel; Andreas Fichtner

doi:10.26443/seismica.v3i2.1405

Authors

Nils Müller Department of Earth Sciences, ETH Zurich, Zurich, Switzerland https://orcid.org/0009-0001-4255-5915
Sebastian Noe Department of Earth Sciences, ETH Zurich, Zurich, Switzerland https://orcid.org/0000-0002-2622-1843
Dominik Husmann Swiss Federal Institute of Metrology, METAS, Bern, Switzerland https://orcid.org/0000-0003-4291-1075
Jacques Morel Swiss Federal Institute of Metrology, METAS, Bern, Switzerland
Andreas Fichtner Department of Earth Sciences, ETH Zurich, Zurich, Switzerland https://orcid.org/0000-0003-3090-963X

DOI:

https://doi.org/10.26443/seismica.v3i2.1405

Keywords:

fiber-optic seismology, earthquake, moment tensor inversion, source inversion

Abstract

We present an earthquake source inversion using a single time series produced by integrated fiber-optic sensing in a phase noise cancellation (PNC) system used for frequency metrology. Operating on a 123 km long fiber between Bern and Basel (Switzerland), the PNC system recorded the Mw3.9 Mulhouse earthquake that occurred on 10 September 2022 around 10 km north-west of the northern fiber end. A generalised least-squares inversion in the 4 - 13 s period band constrains the components of a double-couple moment tensor with an uncertainty that corresponds to around 0.2 moment magnitude units, nearly independent of prior information. Uncertainties for hypocenter location and original time are more variable, ranging between 4 - 20 km and 0.1 - 1 s, respectively, depending on whether injected prior information is realistic or almost absent. This work is a proof of concept that quantifies the resolvability of earthquake source properties under specific conditions using a single-channel stand-alone integrated (non-distributed) fiber-optic measurement. It thereby constitutes a step towards the integration of long-range phase-transmission fiber-optic sensors into existing seismic networks in order to fill significant seismic data gaps, especially in the oceans.

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Earthquake source inversion by integrated fiber-optic sensing

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