Retrieving robust noise-based seismic velocity changes from sparse data sets: synthetic tests and application to Klyuchevskoy volcanic group (Kamchatka)

Crustal pore pressure, which could trigger seismicity and volcanic activity, varies with fluid invasion. Various studies have discussed the potential of using seismic velocity changes from ambient noise to evaluate pore pressure conditions, especially due to rainfall perturbations. Although the influence of rainfall on seismic velocity changes has been reported, consideration of the spatial influence on rainfall towards seismic velocity and its mechanism have not been well understood. We investigated the mechanism of rainfall-induced pore pressure diffusion in southwestern Japan, using seismic velocity change (Vs) inferred from ambient noise. We modeled pore pressure changes from rainfall data based on a diffusion mechanism at the locations where infiltration is indicated. By calculating the correlation between Vs changes and the modeled pore pressure with various hydraulic diffusion parameters, the optimum hydraulic diffusion parameter was obtained. We estimated the diffusion parameters with the highest negative correlation between pore pressure and Vs change because a negative correlation indicates pore pressure increase due to diffusion induced by groundwater load. Furthermore, the spatial variation of the hydraulic diffusivity infers the heterogeneity of the rocks in different locations. This finding suggests that the response of pore pressure induced by rainfall percolation depends on location.&#160; We show that seismic velocity monitoring can be used to evaluate the status of pore pressure at different locations, which is useful for fluid injection, CO2 wellbore storage, and geothermal development.

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Can land subsidence be monitored with ambient seismic auto-correlations?

10.5194/egusphere-egu21-14059 ◽

2021 ◽

Author(s):

Laura Ermert ◽

Marine Denolle ◽

Enrique Cabral Cano ◽

Estelle Chaussard ◽

Dario Solano Rojas

Keyword(s):

Ambient Noise ◽

Seismic Velocity ◽

Urban Infrastructure ◽

Temporal Correlations ◽

Long Term Effect ◽

Seismic Velocity Changes ◽

Subsurface Layers ◽

Velocity Changes ◽

Seasonal Oscillations

Mexico City has been undergoing rapid subsidence for more than 100 years due to groundwater extraction. During the 2010s, rates surpassing 30 centimeters/year were observed by satellite geodetic measurements. Not only does this subsidence pose grave challenges for buildings, urban infrastructure, and water management, but it also changes the seismic response of the affected subsurface layers and thereby alters the seismic hazard in the metropolis that has seen devastating site effects both in the 1985 Michoacan and 2017 Puebla earthquake. We use data and numerical modeling of ambient noise auto-correlations to gain a better insight into the subsidence process through ambient noise techniques.We establish a baseline for the long-term and seasonal variations of seismic velocity near the basin from long-term recordings of the Geoscope station UNM, located at the Universidad Nacional Aut&#243;noma de M&#233;xico in the geotechnical hill zone. We further study temporary recordings from the MASE array (MASE (2007): Meso America Subduction Experiment. Caltech. Dataset. doi:10.7909/C3RN35SP) to see how subsidence and other factors may influence seismic velocity in the geotechnical hill, transition, and lake zones.We find that seasonal oscillations and a strong, rapid velocity drop coincident with the 2017 Puebla earthquake overlay a multi-year increasing trend in seismic velocity. We cautiously interpret the multi-year increase as a long-term effect of subsidence. We further study the temporal correlations of seismic velocity changes with other environmental time series like precipitation, and model auto-correlations in order to improve our understanding of their composition and sensitivity.

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Effect of snowfalls on relative seismic velocity changes measured by ambient noise correlation

10.5194/egusphere-egu21-12637 ◽

2021 ◽

Author(s):

Antoine Guillemot ◽

Alec Van Herwijnen ◽

Laurent Baillet ◽

Eric Larose

Keyword(s):

Snow Cover ◽

Solid Earth ◽

Ambient Noise ◽

Seismic Velocity ◽

Coda Wave ◽

Noise Correlation ◽

Geophysical Research ◽

Seismic Velocity Changes ◽

Velocity Changes ◽

Seismic Measurements

Seismic noise correlation is a broadly used method to monitor the subsurface, in order to detect physical processes into the surveyed medium such changes in rigidity, fluid injection or cracking (1). The influence of several environmental variables on measured seismic observables were studied, such as temperature, groundwater level fluctuations, and freeze-thawing cycles (2). In mountainous, cold temperate and polar sites, the presence of a snowcover can also affect relative seismic velocity changes (dV/V), but this relation is relatively poorly documented and ambiguous (3)(4). In this study, we analyzed raw seismic recordings from a snowy flat field site located above Davos (Switzerland), during one entire winter season (from December 2018 to June 2019). Our goal was to better understand the effect of snowfall and snowmelt events on dV/V measurements through both seismic and meteorological instrumentation.We identified three snowfall events with a substantial response of dV/V measurements (drops of several percent between 15 and 25 Hz), suggesting a detectable change in elastic properties of the medium due to the additional fresh snow.To better interpret the measurements, we used a physical model to compute frequency dependent changes in the Rayleigh wave velocity computed before and after the events. Elastic parameters of the ground subsurface were obtained from a seismic refraction survey, whereas snow cover properties were obtained from the snow cover model SNOWPACK. The decrease in dV/V due to a snowfall were well reproduced, with the same order of magnitude than observed values, confirming the importance of the effect of fresh and dry snow on seismic measurements.We also observed a decrease in dV/V with snowmelt periods, but we were not able to reproduce those changes with our model. Overall, our results highlight the effect of the snowcover on seismic measurements, but more work is needed to accurately model this response, in particular for the presence of liquid water in the snowcover.&#160;References<ul><li>(1) Larose, E., Carri&#232;re, S., Voisin, C., Bottelin, P., Baillet, L., Gu&#233;guen, P., Walter, F., et al. (2015) Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics, 116, 62&#8211;74. doi:10.1016/j.jappgeo.2015.02.001</li> <li>(2) Le Breton, M., Larose, &#201;., Baillet, L., Bontemps, N. & Guillemot, A. (2020) Landslide Monitoring Using Seismic Ambient Noise Interferometry: Challenges and Applications. Earth-Science Reviews</li> <li>(3) Hotovec&#8208;Ellis, A.J., Gomberg, J., Vidale, J.E. & Creager, K.C. (2014) A continuous record of intereruption velocity change at Mount St. Helens from coda wave interferometry. Journal of Geophysical Research: Solid Earth, 119, 2199&#8211;2214. doi:10.1002/2013JB010742</li> <li>(4) Wang, Q.-Y., Brenguier, F., Campillo, M., Lecointre, A., Takeda, T. & Aoki, Y. (2017) Seasonal Crustal Seismic Velocity Changes Throughout Japan. Journal of Geophysical Research: Solid Earth, 122, 7987&#8211;8002. doi:10.1002/2017JB014307</li> </ul>

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