Matches in SemOpenAlex for { <https://semopenalex.org/work/W4322006735> ?p ?o ?g. }
Showing items 1 to 64 of
64
with 100 items per page.
- W4322006735 abstract "In the context of climate change, the occurrence of geohazards such as landslides or rockfalls might increase. Therefore, it is important to have the ability to characterise their (spatial and temporal) occurrences in order to implement protection measures for the potential impacted populations and infrastructures. Nowadays, several methods including Machine Learning algorithms are used to study landslides-triggered micro-seismicity and the associated seismic sources (eg. rockfalls and  slopequakes). Those innovative algorithms allow the automation of the processing chains used to build micro-seismicity catalogues, leading to the understanding of the landslide deformation pattern and internal structure. Unfortunately, each landslide context has its own seismic signature which requires the use of the most complete and handmade training samples to train a Machine Learning algorithm. This is highly time consuming because it involves an expert that needs to manually check every seismic signal recorded by the seismic network, which can be thousands per day.The aim of this study is to develop semi-supervised and unsupervised clustering methods to characterise the micro-seismicity of landslides in near real time. Here, we present the preliminary results obtained for creating a landslide micro-seismicity catalogue from the analysis of a dense network of 50 seismic stations deployed temporarily at the Super-Sauze landslide (French Alps). First, we present the performance of supervised Random Forest and XGBoost trained models on the event signals. Then, an approach aimed at processing streams of raw seismic data based on 18s-length windows is explored. Finally, we discuss the clustering results and the transferability possibilities of the approach to other landslides and even environments (glaciers, volcanoes)." @default.
- W4322006735 created "2023-02-26" @default.
- W4322006735 creator A5030958578 @default.
- W4322006735 creator A5043575239 @default.
- W4322006735 creator A5051791661 @default.
- W4322006735 creator A5071758014 @default.
- W4322006735 creator A5089088172 @default.
- W4322006735 date "2023-05-15" @default.
- W4322006735 modified "2023-09-29" @default.
- W4322006735 title "Towards a generic clustering approach for building seismic catalogues from dense sensor networks" @default.
- W4322006735 doi "https://doi.org/10.5194/egusphere-egu23-7136" @default.
- W4322006735 hasPublicationYear "2023" @default.
- W4322006735 type Work @default.
- W4322006735 citedByCount "0" @default.
- W4322006735 crossrefType "posted-content" @default.
- W4322006735 hasAuthorship W4322006735A5030958578 @default.
- W4322006735 hasAuthorship W4322006735A5043575239 @default.
- W4322006735 hasAuthorship W4322006735A5051791661 @default.
- W4322006735 hasAuthorship W4322006735A5071758014 @default.
- W4322006735 hasAuthorship W4322006735A5089088172 @default.
- W4322006735 hasConcept C121332964 @default.
- W4322006735 hasConcept C124101348 @default.
- W4322006735 hasConcept C127313418 @default.
- W4322006735 hasConcept C151730666 @default.
- W4322006735 hasConcept C154945302 @default.
- W4322006735 hasConcept C165205528 @default.
- W4322006735 hasConcept C186295008 @default.
- W4322006735 hasConcept C204665574 @default.
- W4322006735 hasConcept C2779343474 @default.
- W4322006735 hasConcept C2779662365 @default.
- W4322006735 hasConcept C41008148 @default.
- W4322006735 hasConcept C62520636 @default.
- W4322006735 hasConcept C73555534 @default.
- W4322006735 hasConcept C83176761 @default.
- W4322006735 hasConceptScore W4322006735C121332964 @default.
- W4322006735 hasConceptScore W4322006735C124101348 @default.
- W4322006735 hasConceptScore W4322006735C127313418 @default.
- W4322006735 hasConceptScore W4322006735C151730666 @default.
- W4322006735 hasConceptScore W4322006735C154945302 @default.
- W4322006735 hasConceptScore W4322006735C165205528 @default.
- W4322006735 hasConceptScore W4322006735C186295008 @default.
- W4322006735 hasConceptScore W4322006735C204665574 @default.
- W4322006735 hasConceptScore W4322006735C2779343474 @default.
- W4322006735 hasConceptScore W4322006735C2779662365 @default.
- W4322006735 hasConceptScore W4322006735C41008148 @default.
- W4322006735 hasConceptScore W4322006735C62520636 @default.
- W4322006735 hasConceptScore W4322006735C73555534 @default.
- W4322006735 hasConceptScore W4322006735C83176761 @default.
- W4322006735 hasLocation W43220067351 @default.
- W4322006735 hasOpenAccess W4322006735 @default.
- W4322006735 hasPrimaryLocation W43220067351 @default.
- W4322006735 hasRelatedWork W1966333569 @default.
- W4322006735 hasRelatedWork W1981932415 @default.
- W4322006735 hasRelatedWork W2024730093 @default.
- W4322006735 hasRelatedWork W2072335731 @default.
- W4322006735 hasRelatedWork W2073444015 @default.
- W4322006735 hasRelatedWork W2153932160 @default.
- W4322006735 hasRelatedWork W2163006767 @default.
- W4322006735 hasRelatedWork W2780068850 @default.
- W4322006735 hasRelatedWork W2804622836 @default.
- W4322006735 hasRelatedWork W3137067466 @default.
- W4322006735 isParatext "false" @default.
- W4322006735 isRetracted "false" @default.
- W4322006735 workType "article" @default.