<p>Monitoring landslides is essential to understand their dynamics and to reduce the risk of human losses by raising warnings before a failure. A decade ago, a decrease of apparent seismic velocity was detected several days before the failure of a clayey landslide, that was monitored with the ambient noise correlation method. It revealed its potential to detect precursor signals before a landslide failure, which could improve early warning systems. To date, nine landslides have been monitored with this method, and its ability to reveal precursors before failure seems confirmed on clayey landslides. However three challenges remain for operational early-warning applications: to detect velocity changes both rapidly and with confidence, to account for seasonal and daily environmental influences, and to check for potential instabilities in measurements. The ability to detect a precursory velocity change requires to adapt the processing workflow to each landslide: the key factors are the filtering frequency, the correlation time window, and the choice of temporal resolution. The velocity also fluctuates seasonally, by 1 to 6% on the reviewed landslide studies, due to environmental influences, with a linear trend between the amplitude of seasonal fluctuations and the filtering frequency over the 0.1&#8211;20&#8239;Hz range, encompassing both landslide and non-landslide studies. The environmental velocity fluctuations are caused mostly by groundwater levels and soil freezing/thawing, but could also be affected by snow height, air temperature and tide depending on the site. Daily fluctuations should also occur on landslides, and can be an issue when seeking to obtain a sub-daily resolution useful for early-warning systems. Finally, spurious fluctuations of apparent velocity&#8212;unrelated to the material dynamics&#8212;should be verified for. They can be caused by changes in noise sources (location or spectral content), in site response (change of scatterers, attenuation, or resonance frequency due to geometrical factors), or in inter-sensor distance. As a perspective, the observation of seismic velocity changes could contribute in assessing a landslide stability across time, both during the different creeping stages occurring before a potential failure, and during its reconsolidation after a failure.</p><p>----</p><p>Main references :</p><ul><li>Le Breton M., Bontemps N., Guillemont A., Baillet L., Larose E., 2021. Landslide Monitoring Using Seismic Ambient Noise Correlation: Challenges and Applications, Earth Science Reviews, In press</li>
<li>Larose, E., Carri&#232;re, S., Voisin, C., Bottelin, P., Baillet, L., Gu&#233;guen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S., Gimbert, F., Massey, C., 2015. Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics 116, 62&#8211;74. https://doi.org/10.1016/j.jappgeo.2015.02.001</li>
<li>Mainsant, G., Larose, E., Br&#246;nnimann, C., Jongmans, D., Michoud, C., Jaboyedoff, M., 2012. Ambient seismic noise monitoring of a clay landslide: Toward failure prediction. J. Geophys. Res. 117, F01030. https://doi.org/10.1029/2011JF002159</li>
</ul>
Effect of snowfall on changes in relative seismic velocity measured by ambient noise correlation
