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              <p>Given that clay-rich landslides may become mobilized, leading to rapid mass movements (earthflows and debris flows), they pose critical problems in risk management worldwide. The most widely proposed mechanism leading to such flow-like movements is the increase in water pore pressure in the sliding mass, generating partial or complete liquefaction. This solid-to-liquid transition results in a dramatic reduction of mechanical rigidity in the liquefied zones, which could be detected by monitoring shear wave velocity variations. With this purpose in mind, the ambient seismic noise correlation technique has been applied to measure the variation in the seismic surface wave velocity in the Pont Bourquin landslide (Swiss Alps). This small but active composite earthslide-earthflow was equipped with continuously recording seismic sensors during spring and summer 2010. An earthslide of a few thousand cubic meters was triggered in mid-August 2010, after a rainy period. This article shows that the seismic velocity of the sliding material, measured from daily noise correlograms, decreased continuously and rapidly for several days prior to the catastrophic event. From a spectral analysis of the velocity decrease, it was possible to determine the location of the change at the base of the sliding layer. These results demonstrate that ambient seismic noise can be used to detect rigidity variations before failure and could potentially be used to predict landslides.</p>
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              <addrLine>14-20 Boulevard Newton - Cité Descartes, Champs sur Marne - F-77447 Marne la Vallée Cedex 2</addrLine>
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        <org type="institution" xml:id="struct-303008" status="OLD">
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          <date type="end">2014-12-31</date>
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            <address>
              <country key="FR"/>
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          <idno type="ROR">https://ror.org/019whta54</idno>
          <orgName>Université de Lausanne = University of Lausanne</orgName>
          <orgName type="acronym">UNIL</orgName>
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            <address>
              <addrLine>CH-1015 Lausanne</addrLine>
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          <desc>Project FP7 ERC Advanced grant 227507, Whisper</desc>
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