Landslide mechanisms unraveled by RFID monitoring with a Machine Learning approach Article Swipe
Arthur Charléty
,
Mathieu Le Breton
,
Éric Larose
,
Laurent Baillet
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu23-13287
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu23-13287
Radio-Frequency Identification (RFID) shows great potential for earth-sciences applications [1], notably in landslide surface monitoring at high spatio-temporal resolution [2] with meteorological robustness [3]. Ten 865MHz RFID tags were deployed on part of a landslide (Harmalière) and continuously monitored for 12 months by a station composed of 4 reader antennas. 2D relative localization was performed using a Phase-of-Arrival approach [4,5], and compared with optical reference measurements. The spatio-temporal accuracy of the method allowed for a thorough exploration of the landslides mechanisms during a 6-months period of activity. Laplacian clustering was applied to the RFID data and groups of tags with coherent behavior were identified, allowing a fine description of the kinematic motion of the landslide blocks and various mass transfer mechanisms. Each identified block can be monitored individually. Different deformation zones were highlighted on the monitored zone. The surface movement was initiated by the topmost blocks, transferring after several weeks to the bottom of the monitored zone. This opens the way to building a landslide mechanical model in order to interpret the acquired data. RFID landslide monitoring allows dense observation of ground surface movements at a centimeter scale and with sub-hourly time precision, and new results bring a finer understanding the the landslides inner mechanisms. References :[1] M. Le Breton, F. Liébault, L. Baillet, A. Charléty, E. Larose, and S. Tedjini,“Dense and long-term monitoring of earth surface processes with passiverfid—a review,” Earth-Science Reviews, p. 104225, 2022.[2] M. Le Breton, L. Baillet, E. Larose, E. Rey, P. Benech, D. Jongmans, F. Guy-oton, and M. Jaboyedoff, “Passive radio-frequency identification ranging, adense and weather-robust technique for landslide displacement monitoring,”Engineering geology, vol. 250, pp. 1–10, 2019.[3] M. Le Breton, L. Baillet, E. Larose, E. Rey, P. Benech, D. Jongmans, andF. Guyoton, “Outdoor uhf rfid: Phase stabilization for real-world appli-cations,” IEEE Journal of Radio Frequency Identification, vol. 1, no. 4,pp. 279–290, 2017[4] A. Charléty, M. Le Breton, E. Larose, and L. Baillet, “2d phase-based rfid lo-calization for on-site landslide monitoring,” Remote Sensing, vol. 14, no. 15,p. 3577, 2022. [5] P. V. Nikitin, R. Martinez, S. Ramamurthy, H. Leland, G. Spiess, andK. Rao, “Phase based spatial identification of uhf rfid tags,” in 2010 IEEEInternational Conference on RFID (IEEE RFID 2010), pp. 102–109, IEEE,2
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu23-13287
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4322209990Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu23-13287Digital Object Identifier
- Title
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Landslide mechanisms unraveled by RFID monitoring with a Machine Learning approachWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
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2023-02-26Full publication date if available
- Authors
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Arthur Charléty, Mathieu Le Breton, Éric Larose, Laurent BailletList of authors in order
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https://doi.org/10.5194/egusphere-egu23-13287Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/egusphere-egu23-13287Direct OA link when available
- Concepts
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Landslide, Robustness (evolution), Geology, Computer science, Kinematics, Remote sensing, Geodesy, Seismology, Physics, Chemistry, Classical mechanics, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.interpret | 173 |
| abstract_inverted_index.kinematic | 111 |
| abstract_inverted_index.landslide | 12, 34, 115, 167, 179, 266, 326 |
| abstract_inverted_index.long-term | 225 |
| abstract_inverted_index.monitored | 38, 127, 138, 158 |
| abstract_inverted_index.movements | 187 |
| abstract_inverted_index.performed | 54 |
| abstract_inverted_index.potential | 5 |
| abstract_inverted_index.processes | 230 |
| abstract_inverted_index.reference | 64 |
| abstract_inverted_index.technique | 264 |
| abstract_inverted_index.  | 66, 130, 177 |
| abstract_inverted_index.Conference | 360 |
| abstract_inverted_index.centimeter | 190 |
| abstract_inverted_index.clustering | 89 |
| abstract_inverted_index.identified | 123 |
| abstract_inverted_index.landslides | 80, 206 |
| abstract_inverted_index.mechanical | 168 |
| abstract_inverted_index.mechanisms | 81 |
| abstract_inverted_index.monitoring | 14, 180, 226 |
| abstract_inverted_index.precision, | 196 |
| abstract_inverted_index.real-world | 296 |
| abstract_inverted_index.resolution | 18 |
| abstract_inverted_index.robustness | 22 |
| abstract_inverted_index.sub-hourly | 194 |
| abstract_inverted_index.Jaboyedoff, | 256 |
| abstract_inverted_index.Ramamurthy, | 342 |
| abstract_inverted_index.deformation | 132 |
| abstract_inverted_index.description | 108 |
| abstract_inverted_index.exploration | 77 |
| abstract_inverted_index.highlighted | 135 |
| abstract_inverted_index.identified, | 104 |
| abstract_inverted_index.mechanisms. | 121 |
| abstract_inverted_index.observation | 183 |
| abstract_inverted_index.phase-based | 321 |
| abstract_inverted_index.applications | 8 |
| abstract_inverted_index.continuously | 37 |
| abstract_inverted_index.displacement | 267 |
| abstract_inverted_index.localization | 52 |
| abstract_inverted_index.transferring | 149 |
| abstract_inverted_index.“2d | 320 |
| abstract_inverted_index.Earth-Science | 234 |
| abstract_inverted_index.lo-calization | 323 |
| abstract_inverted_index.stabilization | 294 |
| abstract_inverted_index.understanding | 203 |
| abstract_inverted_index.Identification | 1 |
| abstract_inverted_index.earth-sciences | 7 |
| abstract_inverted_index.identification | 259, 352 |
| abstract_inverted_index.meteorological | 21 |
| abstract_inverted_index.weather-robust | 263 |
| abstract_inverted_index.1–10, | 273 |
| abstract_inverted_index.Identification, | 303 |
| abstract_inverted_index.data.  | 176 |
| abstract_inverted_index.radio-frequency | 258 |
| abstract_inverted_index.spatio-temporal | 17, 68 |
| abstract_inverted_index.“Phase | 349 |
| abstract_inverted_index.Phase-of-Arrival | 57 |
| abstract_inverted_index.tags,” | 356 |
| abstract_inverted_index.IEEEInternational | 359 |
| abstract_inverted_index.“Outdoor | 290 |
| abstract_inverted_index.“Passive | 257 |
| abstract_inverted_index.102–109, | 367 |
| abstract_inverted_index.2022. [5] | 335 |
| abstract_inverted_index.279–290, | 308 |
| abstract_inverted_index.Charléty, | 218, 311 |
| abstract_inverted_index.Liébault, | 214 |
| abstract_inverted_index.review,” | 233 |
| abstract_inverted_index.(Harmalière) | 35 |
| abstract_inverted_index.monitoring,” | 327 |
| abstract_inverted_index.measurements.  | 65 |
| abstract_inverted_index.passiverfid—a | 232 |
| abstract_inverted_index.Tedjini,“Dense | 223 |
| abstract_inverted_index. Radio-Frequency | 0 |
| abstract_inverted_index.appli-cations,” | 297 |
| abstract_inverted_index.    | 129 |
| abstract_inverted_index.mechanisms. References | 208 |
| abstract_inverted_index.individually.   | 128 |
| abstract_inverted_index.monitoring,”Engineering | 268 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5003745613 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.03963992 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |