Suchergebnisse

Continuous noise-based monitoring of seismic velocity changes provides insights into volcanic unrest, earthquake mechanisms and fluid injection in the subsurface. The standard monitoring approach relies on measuring traveltime changes of late coda arrivals between daily and reference noise cross-correlations, usually chosen as stacks of daily cross-correlations. The main assumption of this method is that the shape of the noise correlations does not change over time or, in other terms, that the ambient-noise sources are stationary through time. These conditions are not fulfilled when a strong episodic source of noise, such as a volcanic tremor, for example, perturbs the reconstructed Green's function. In this paper, we propose a general formulation for retrieving continuous time-series of noise-based seismic velocity changes without the requirement of any arbitrary reference cross-correlation function (CCF). Instead, we measure the changes between all possible pairs of daily cross-correlations and invert them using different smoothing parameters to obtain the final velocity change curve. We perform synthetic tests in order to establish a general framework for future applications of this technique. In particular, we study the reliability of velocity change measurements versus the stability of noise CCFs. We apply this approach to a complex data set of noise cross-correlations at Klyuchevskoy volcanic group (Kamchatka), hampered by loss of data and the presence of highly non-stationary seismic tremors. ; All the data used in this study were provided by the Kamchatka Branch of Geophysical Survey of Russian Academy of Sciences (http://www.emsd.ru). This study was supported by REPSOL CO-DOS and REPSOL CO-DOS Phase 2 projects, by the European COST action TIDES (ES1401), by the Russian Ministry of Education and Science (grant number 14.W03.31.0033), by the French projects 'Labex UnivEarth' and Université Sorbonne Paris Cité project 'VolcanoDynamics', by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (grant agreement number 742335, F-IMAGE) and by a research grant from Science Foundation Ireland (SFI) under grant number 13/RC/2092 and co-funded under the European Regional Development Fund and by iCRAG industry partners. Computations were performed using the IPGP High-Performance Computing infrastructure S-CAPADE (supported by the Îlle-de-France region via the SEASAME programme, by France-Grille, and by the CNRS MASTODONS programme). ; Peer reviewed