FN Archimer Export Format
PT J
TI Modeling spatial dynamics of the Fani Maoré marine volcano earthquake data
BT 
AF Manou-Abi, Solym
   Hachim, Said
   Dabo, Sophie
   Nguala, Jean-Berky
AS 1:1,4;2:2;3:3;4:4,5;
FF 1:;2:;3:;4:;
C1 Institut Montpelli´erain Alexander Grothendieck, UMR CNRS 5149, Place Eugene Bataillon, Montpellier, 34090, France
   Conseil Departemental de Mayotte, Mamoudzou, Mayotte, 97600, France
   Laboratory Painleve, UMR 8524, Cite scientifique, Villeneuve d’ascq, 59653, France
   Centre Universitaire de Formation et de Recherche, 8 Rue de l’universite, Dembeni, 97660, Mayotte, France
   Laboratoire d’Informatique et de Math´ematiques, EA 2525 Parc Technologique Universitaire, 2 rue Joseph Wetzell, La Reunion, 97490, Sainte Clotilde, France
C2 UNIV MONTPELLIER, FRANCE
   CONSEIL DEPART MAYOTTE, FRANCE
   UNIV LILLE, FRANCE
   UNIV MAYOTTE, FRANCE
   UNIV LA REUNION, FRANCE
TC 0
UR https://archimer.ifremer.fr/doc/00856/96746/105293.pdf
LA English
DT Article
CR MAYOBS
   MAYOBS1
BO Marion Dufresne
DE ;Classification;Spatio-temporal model;Time series;Spatial point pattern analysis;Earth Science;Spatial density smoothing
AB This paper provides the outcomes of a work consisting in modeling and learning some earthquakes data collected during the Mayotte seismovolcanic crisis of 2018-2021. We highlight the performance of some process data models in order to illustrate the spatial and temporal dynamic. Unsupervised clustering method, spatial pattern analysis, spatial density estimation through spatial marked point process; time series and spatio-temporal models are efficient tools that we studied in this paper to look for the spatial and temporal variation of such spatial data mainly driven by the detected underwater volcano around Mayotte called Fani Maoré . The dynamic of the magnitude and depth events of the Fani Maoré with the use of the above mentionned models seems to perform the data. We present a discussion thoughout the presentation of the obtained results together with the limit of this study and some forthcoming projects and modeling developments. 
PY 2023
PD OCT
SO Preprint
PU Research Square Platform LLC
DI 10.21203/rs.3.rs-3403019/v1
ID 96746
ER

EF