Towards a standard typology of endogenous landslide seismic sources

Provost, Floriane; Malet, Jean‐Philippe; Hibert, Clément; Helmstetter, Agnès; Radiguet, Mathilde; Amitrano, David; Langet, Nadège; Larose, Éric; Abancó, Clàudia; Hürlimann, Marcel; Lebourg, Thomas; Lévy, Clara; Roy, Gaëlle Le; Ulrich, Patrice; Vidal, Maurin; Vial, Benjamin

doi:10.5194/esurf-6-1059-2018

Cited by 43 publications

(59 citation statements)

References 151 publications

(226 reference statements)

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“…DEM = digital elevation model. signals described in previous studies (Helmstetter & Garambois, 2010;Provost et al, 2018). The configuration of the network, with large distances between stations, does not allow us to use the apparent velocity or the intertrace correlation as classifying parameters.…”

Section: Detection and Classification Of Seismic Signalsmentioning

confidence: 96%

Seismic Analysis of the Detachment and Impact Phases of a Rockfall and Application for Estimating Rockfall Volume and Free‐Fall Height

et al. 2019

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We analyzed 21 rockfalls that occurred in limestone cliffs of the Chartreuse Massif (French Alps). These rockfalls were detected both by Terrestrial Laser Scanning or photogrammetry and by a local seismological network. The combination of these methods allowed us to study relations between rockfall properties (location of detachment and impacts areas, volume, geometry, and propagation) and the induced seismic signal. We observed events with different propagation modes (sliding, mass flow, and free fall) that could be determined from digital elevation models. We focused on events that experienced a free fall after their detachment. We analyzed the first parts of the seismic signals corresponding to the detachment phase and to the first impact. The detachment phase has a smaller amplitude than the impact phase, and its amplitude and duration increases with rockfall volume. By measuring the time delay between the detachment phase and the first impact, we can estimate the free‐fall height. We found a relation Es = aEpb between the potential energy of a rockfall Ep and the seismic energy Es generated during an impact, with a = 10−8 and b = 1.55 and with a correlation coefficient R2 = 0.98. We can thus estimate both the potential energy of a block and its free‐fall height from the seismic signals. By combining these results, we obtain an accurate estimate of the rockfall volume. The relation between the Ep and Es was tested on different geological settings and for larger range of volumes using Yosemite, Mount Granier rockfalls, and with a data set of controlled releases of blocks (Hibert et al., 2017, https://doi.org/10.5194/esurf-5-283-2017, https://www.earth-surf-dynam.net/5/283/2017/).

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Section: Detection and Classification Of Seismic Signalsmentioning

confidence: 96%

Seismic Analysis of the Detachment and Impact Phases of a Rockfall and Application for Estimating Rockfall Volume and Free‐Fall Height

et al. 2019

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“…After the seismic signals of both landslide events were received at the nearest monitoring station (MID; 10 km), the seismic wave reached the DUG station (68 km) in approximately 30 s. The study used the Hilbert-Huang transform (HHT) method to analyze the seismic signals received by the DUG monitoring station, and the results revealed that the energy peaked 1 min after receiving the signals and was concentrated at 5 Hz. Provost et al [19] proposed a classification of seismic signals generated by various types of landslide processes, including slide, fall, topple, and flow. They analyzed duration, frequency content, and spectrogram shape of the seismic sources acquired from 13 unstable slopes and categorized the signals into three main classes-"slope quake", "rockfall", and "granular flow"-based on the characteristics of the signals.…”

Section: Introductionmentioning

confidence: 99%

Discussion on the Characteristics of Seismic Signals Due to Riverbank Landslides from Laboratory Tests

Zhang

Hsu

Chen

2019

Water

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Floods and erosion often cause landslides of riverbanks and induce problems such as river blockage, shift of river center, or flooding from rising riverbeds. Instrumentation and monitoring are often used to explore landslide and erosion behavior of riverbanks. Therefore, this study identified landslide types and characteristics of their seismic signals due to toe erosion of riverbanks through riverbank models with various instrumentation sensors in a laboratory flume. To induce landslides in the riverbank model, a test was set up for water to flow through the toe of the riverbank model. Seismic signals of each landslide event were measured during the tests with accelerometers. Nonpolarized electrodes were installed for observing the self-potential changes during the test. Water content and pore water pressure gauges were installed in the riverbank model. In addition, water levels were recorded. The Hilbert–Huang transform method was used to analyze the characteristics of seismic signals caused by water flow and riverbank landslides. Time points, landslide frequency distributions, and the characteristics of several landslide events in the riverbank models were estimated using the seismic signals. This study identified three types of landslides: single, intermittent, and successive. Moreover, changes in self-potential signals, pore water pressure, and water content during the tests were examined and were found to correspond to the landslide process of the riverbank model.

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“…5c.). The second type of endogenous signals is generated by slopequakes; these sources are assumed to result from stress release by both fracture opening or shearing (Helmstetter & Garambois 2010;Walter et al 2013;Tonnellier et al 2013;Vouillamoz et al 2017;Provost et al 2018). The recorded signals last less than 5 s and different waveforms and frequency content may be observed (Provost et al 2018).…”

Section: Microseismic Monitoring and Seismic Signal Descriptionmentioning

confidence: 99%

“…The second type of endogenous signals is generated by slopequakes; these sources are assumed to result from stress release by both fracture opening or shearing (Helmstetter & Garambois 2010;Walter et al 2013;Tonnellier et al 2013;Vouillamoz et al 2017;Provost et al 2018). The recorded signals last less than 5 s and different waveforms and frequency content may be observed (Provost et al 2018). It must be noted that some events are only recorded by one of the seismic arrays (Walter et al 2012;Tonnellier et al 2013;Vouillamoz et al 2017) and that the intertrace correlation of the whole signal is low due to scattering and dispersion of the seismic waves (Walter et al 2012;Tonnellier et al 2013).…”

Section: Microseismic Monitoring and Seismic Signal Descriptionmentioning

confidence: 99%

“…rockslides; Amitrano et al (2005); Spillmann et al (2007); Helmstetter & Garambois (2010); Levy et al (2011); Walter et al (2012); Brückl et al (2013); Provost et al (2018)] and ductile medium [e.g. earth/mudslides; Rouse et al (1991); Gomberg et al (1995); Tonnellier et al (2013); Walter et al (2013); Provost et al (2018)]. However the mechanisms controlling the generation of these seismic signals remain poorly understood, mainly because of the poor accuracy of the seismic source location with respect to the landslide dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Automatic approach for increasing the location accuracy of slow-moving landslide endogenous seismicity: the APOLoc method

Provost

Malet

Gance

et al. 2018

Geophysical Journal International

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Seismological observations offer valuable insights on the stress-strain states, the physical mechanisms and the possible precursory signs of activation of various Earth surface processes (i.e. volcanoes, glaciers and landslides). Comprehensive catalogues of the endogenous landslide seismicity, that is corresponding to seismic sources generated by the unstable slope from either mechanical or hydrological origins, should include the typology and an estimate of the source parameters (location, magnitude) of the event. These advanced catalogues constitute a strong basis to better describe the slope deformation and its time evolution and better understand the controlling factors. Because the number of seismic events in landslide catalogues is generally large, automatic approaches must be considered for defining both the typology and the location of the sources. We propose here a new location approach called Automatic Picking Optimization and Location method-APOLoc for locating landslide endogenous seismic sources from seismological arrays located at close distance. The approach is based on the automatic picking of the P waves arrivals by optimizing the intertrace correlations. The method is tested on calibration shots realized at the Super-Sauze landslide (Southeast French Alps) and compared to other location approaches. By using a realistic velocity model obtained from a seismic tomography campaign, APOLoc reduces the epicentre errors to 23 m (on average) compared to ca. 40 m for the other approaches. APOLoc is then applied for documenting the endogenous seismicity (i.e. slopequakes and rockfalls) at the landslide.

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Towards a standard typology of endogenous landslide seismic sources

Cited by 43 publications

References 151 publications

Seismic Analysis of the Detachment and Impact Phases of a Rockfall and Application for Estimating Rockfall Volume and Free‐Fall Height

Seismic Analysis of the Detachment and Impact Phases of a Rockfall and Application for Estimating Rockfall Volume and Free‐Fall Height

Discussion on the Characteristics of Seismic Signals Due to Riverbank Landslides from Laboratory Tests

Automatic approach for increasing the location accuracy of slow-moving landslide endogenous seismicity: the APOLoc method

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