Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LISTIC
Durée : 4-6 mois
Contact : yajing.yan@univ-smb.fr
Date limite de publication : 2025-03-01

Contexte :
Satellite radar interferometry, more commonly known as InSAR,
provides precise displacement measurements over vast land
areas. The availability of satellite constellations and frequent
revisit times make it a crucial source of information for
monitoring volcanic activity. Understanding and
modeling a volcanic eruption are critical steps in decision-
making when dealing with such geological phenomena. The
opening of a dyke (volcanic vein) or a fissure, as
well as its initial geometry, depends on several factors, including
the pressures exerted and the mechanical properties of the
ground.

Volcanic fissures do not have a simple, flat geometry; they
narrow and widen, flare, branch, and stratify. Furthermore,
their width and shape can also change during an eruption
depending on various geological configurations.
The identification of volcanic fissures is therefore particularly
important for accurate volcanic modeling. However, this task is
currently performed manually based on in-situ observations. However, with the continuous increase in the
amount of available SAR data, there is a growing need for
advanced methods to effectively automate this detection
process. Surface deformation detection in interferograms is a
well-studied topic in the literature, whereas fissure
detection has not received the same level of attention. The Piton
de la Fournaise on the island of Réunion is the subject of
extensive monitoring and has a database spanning 24 years. Preliminary results obtained by our team on
this volcano have demonstrated the feasibility of detecting
fissures in the interferograms. Using classical methods, we
successfully detected the presence or absence of a fissure within the interferograms from a dozen different satellites. However, the mere presence or absence of a fissure is far from sufficient for analyzing the geological mechanisms associated with the volcano, and further work is needed to obtain precise locations of these fissures.

Sujet :
The objective of this project is to detect and localize volcanic
fissures in satellite radar interferograms using artificial
intelligence techniques and skeleton-based geometry
recognition. Several types of satellites pass over the Piton de la
Fournaise enclosure, allowing for regular and
continuous observation. However, each sensor has its own
characteristics, including mandated revisit times, operational
costs (free or paid), as well as different observation angles and
pass directions. One of the initial hypotheses is that the
localization of fissures follows a logical pattern depending on
the type of InSAR source and the spatial area around the
eruptive cone. The second hypothesis explores the similarity
between the structure of volcanic fissures and that of skeletons,
like action recognition based on skeletal data extracted from
photographs. Action recognition from skeletons is a task that
involves recognizing human actions from a sequence of point
data on joints captured by specific sensors. In our project, the
approach is reversed: given the eruptive attributes and the
InSAR data, we aim to recognize the fissure and associate it with
a geometric shape, regardless of the type of satellite and its field
of view.

For more details, please see the attached file.

Profil du candidat :

Formation et compétences requises :
The candidate should have knowledge and skills in machine
learning and AI programming (Python). Experience in remote
sensing and volcanic geophysics would be highly valued,
particularly concerning the analysis of InSAR data.

Adresse d’emploi :
LISTIC, 5 chemin de bellevue, CS80439, 74944, Annecy-le-Vieux

Document attaché : 202412050746_Internship LISTIC 2025 – Fissures.pdf