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

Laboratoire/Entreprise : LISTIC
Durée : 3 ans
Contact : yajing.yan@univ-smb.fr
Date limite de publication : 2022-07-10

Contexte :
This Ph.D thesis is proposed along with the increasing and regular availability of the amount of remote sensing data and the response to the requirement of operational prediction of natural hazards. The main objective is to improve the near-real-time integration of remote sensing data and dynamical geophysical models for the mitigation of natural hazards. This thesis is partly (50%) funded by the national action plan in Artificial Intelligence. The relevance of the methodology developed in this thesis 1) compared to the actually emmerging data-driven methods, lies in the incorporation of geophysical knowledge (which helps increase the interpretability and the accountability of the results for operational purpose) and its near-real-time implementation ; 2) compared to previous attemps to improve the near-real-time integration of InSAR data based on
the Kalman Filter, lies in the capability in taking non-Gaussian error statistics (which can represent better the reality) into account. First application will be in volcanology, using InSAR & GNSS data, but the methodology can be easily utilized for other natural hazards (e.g. landslides, slow slip, etc.), as well as for anthropogenic hazards like forest fire.

Sujet :
In a perspective of volcanic hazard assessment, it is fundamental to be able to know, in advance, if magma that has started to propagate from a reservoir will reach the surface, where and when. The propagation phase is generally rapid, lasting a few hours to a few months but it induces
seismicity and deformation signals. These signals are recorded by continuous sensors (GNSS) and InSAR data whose temporal sampling frequency as well as latency have been greatly improved in recent years. In addition, we have dynamic magmatic intrusion propagation models that can be used to calculate, depending on the physical parameters of the Earth’s crust, the properties of the magma and the state of local stress, the trajectory followed by the magma and its propagation velocity (Pinel et al. 2017). Data assimilation, a method that combines a dynamic model with observations at present and in the past based on error statistics and predicts the future state of the observed system, is therefore an appropriate tool to respond to the need to be able to predict
the position and timing of an eruption in volcanology.

Among numerous data assimilation methods, the particle filter is distinguished from others by its great ability to deal with non-linear models and non-Gaussian error statistics (van Leeuwen P.J 2009, van Leeuwen P.J, 2010). The particle filter is based on a representation of the probability density of the dynamic model by a discrete set of states of the model (namely particles) and relies on the Bayes theorem so without worrying about the distribution of model errors (different from most other data assimilation methods which assume a Gaussian distribution of the errors). The evolution of the probability density of the model is realized through the propagation of all the particles (states of the model) over time following the model equation (the physics). When
observations of the system are available, the relative contributions of the particles are modified so that the information available in the observations is integrated into the particles swarm. The particle filter does not directly correct the values of particles, but their relative contributions, this is very important for estimating magmatic intrusion propagation trajectories. The particle filter is therefore the appropriate tool in the present specific context of estimating the position of a volcanic eruption.

In this thesis, we will develop an efficient data assimilation strategy using the particle filter allowing to use timely available geodedic data to predict the location and timing of eruptive vents induced by magmatic intrusion propagation. This work will be based on the Ph.D thesis of Mary Grace Bato who, under our supervision and for the first time, successfully applied sequential data assimilation techniques (i.e. Ensemble Kalman Filter) to volcanological problems focussing on the pressurization and rupture of magmatic reservoirs (Bato et al, 2017, Bato et al., 2018). Moreover, this thesis will benefit from the results obtained in the TOSCA AssimSAR project (2018-2019). It will be part of the Franco-German ANR MagmaPropagator (ANR-18-CE92-0037, 2019-2022) with an application to Piton de la Fournaise volcano. It will also be the oppotunity to collaborate with Andy Hooper from the University of Leeds, who is developping new methods to automatically extract a physical signal from InSAR time series (Gaddes et al, 2019).

Selected references :
1) Bato M.-G., Pinel V., Yan Y., Jouanne F., Vandemeulebrouck J., “Possible deep connection between volcanic systems evidenced by sequential assimilation of geodetic d”, Scientific Reports, Nature Publishing Group, 2018, https://doi.org/10.1038/s41598-018-29811-x
2) Bato M.-G., Pinel V., Yan Y., “Assimilation of Deformation Data for Eruption Forecasting: Potentiality Assessment Based on Syntheti”, Frontiers in Earth Science, Frontiers Media, 2017, pp.doi: 10.3389/feart.2017.00048
3) Dalaison, M., Jolivet, R., A Kalman filter time series analysis method for InSAR, Journal of Geophysical Research : Solid Earth , 2020, e2019JB019150. e2019JB019150 2019JB019150.
4) Gaddes, M. E., Hooper, A., Bagnardi, M. (2019), Using machine learning to automatically detect volcanic unrest in a time series of interferograms, Journal of Geophysical Research : Solid Earth , 124(11), 12304–12322.
5) Pinel V., Carrara A., Maccaferri F., Rivalta E., Corbi F., A two-step model for dynamical dike propagation in two dimensions: Application to the July 2001 Etna eruption, 2017, Journal of Geophysical Research, vol. 122, doi:10.1002/2016JB013630.
6) van Leeuwen P.J., Review Particle Filtering in Geophysical System, Mathematical Advances in Data Assimilation, 2009, pp. 4089-4114.
7) van Leeuwen P.J. Nonlinear data assimilation in geosciences : an extremely efficient particle filter, 2010, Quarterly Journal of the Royal Meteorological Society, vol. 136, pp. 1991-1999.

Profil du candidat :
The Ph.D candidate should have good skills in signal/image processing,
mathematics/statistics or geophysics.

Formation et compétences requises :
mathematics/statistics, image processing, remote sensing

Adresse d’emploi :
Laboratoire d’Informatique, Systèmes, Traitement de l’Information et de la Connaissance, Université Savoie Mont-Blanc, Annecy, France

Document attaché : 202203081350_sujet_these_DA_volcan_en2022.pdf

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

Laboratoire/Entreprise : LAMSADE
Durée : 3 years
Contact : sana.mrabet@dauphine.psl.eu
Date limite de publication : 2022-04-15

Contexte :
Evaluating the level of biodiversity in marine and terrestrial locations is currently a universal practice for estimating the impact of human activities and climate change on the ecosystems of these environments. An innovative and modern approach to perform this observation is the high-throughput sequencing of DNA taken from the environment (“environmental DNA)
Environmental DNA (eDNA) analysis consists mainly of the identification of species from the DNA they leave in their environment. Many studies show that the use of eDNA allows a good estimation of the different species (or taxa) present in different types of environments. eDNA analysis is based on classical molecular biology techniques (PCR, sequencing…). The identified taxa are assigned to ecological weights which are used to calculate the biotic indices (BI). These indices are used to determine the ecological quality of the site in question (generally classified in five categories from “very poor” to “very good”). However, these studies relied on reference sequence databases (Silva Greengenes LTP) for taxonomic assignment, in order to retrieve taxon-specific ecological weights and to calculate BI values. This phase may fail if some sequences in the environment to be analysed are incomplete or do not appear in the reference databases.
Recent works [1,2,5] has demonstrated that machine learning can be used to predict accurate values of biotic indices from eDNA metabarcoding, regardless of sequence affiliation. The idea is to generate a clustering model to group closely related sequences (high percentage of identity) belonging to a well-defined taxon. The approach then consists in using a clustering technique on the analysis data of the marine samples applicable even if the determined sequences are incomplete. These sequences are then affiliated automatically to the taxon of the central sequence of the cluster identified in a reference database. The validity of the model can be checked by searching for the exact affiliation of the cluster centres in reference databases.

Sujet :
The subject of this thesis has a double objective. The first objective is the implementation of conventional techniques for clustering eDNA data of a marine environment allowing to evaluate the BI of this environment. The generated models in this phase may be non-generalizable solutions and very dependent on the sequencing methods used in the analysed samples. Hence the second objective of the thesis which aims at the generalization of prediction models to data from other samples of the same marine environment using evolutionary methods. The idea is to develop a hybrid evolutionary algorithm that evolves clustering models as a function of sample parameters (i.e. sequencing markers) and clustering parameters (i.e. distance measurement indicators). The model scheme can be based on the “Genetic K-means algorithm” (GKA) of Krishna and Murty.
The first models are to be tested on marine samples studied by Cordier [1,2] and available online. Other samples will be studied after validation of the algorithm. The validation of the method and its application on other marine data may lead to a possible exchange with OFB (French Office of Biodiversity).

Profil du candidat :
Master’s degree in computer science or equivalent degree giving access to PhD studies.

Formation et compétences requises :
– Good knowledge in Machine Learning
– Good knowledge in Computer Science and Mathematics.
– Good programming skills, especially in Python programming.
– Some knowledge in Bio-informatic would be good but it is not mandatory.
– Good command of written and spoken English.

Adresse d’emploi :
LAMSADE CNRS UMR 7243, University of Paris Dauphine, France

Document attaché : 202203072123_PhD Application – Lamsade 2022.pdf

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

Laboratoire/Entreprise : LISTIC (Université Savoie Mont-Blanc)
Durée : 3 years
Contact : guillaume.ginolhac@univ-smb.fr
Date limite de publication : 2022-07-03

Contexte :
The thesis will be funded by the ANR REPED-SARIX project and will be start from the 1st September 2022 (or possibly october 1st).

Keywords : SAR interferometry, robust statistics, recursive estimation, missing data imputation, time series.

Sujet :
The systematic acquisition of and free access to Sentinel-1 A/B Synthetic Aperture Radar (SAR) images covering Europe every 6 days (every 12 days elsewhere) provide scientists with both opportunities and challenges for operational monitoring of Earth deformation by SAR image time series. For displacement estimation from SAR image time series, numerous multi-temporal Interferometry SAR (InSAR) methods, such as Small BAseline Subset, Permanent Scatterer Interferometry, SqueeSAR, Phase Linking methods, Multi-link InSAR, CAESAR, Least-Square estimator and EMI, have been extensively developed and implemented. Thanks to these methods, the accuracy of the displacement velocity estimation has been revolutionized to millimeters per year. However, these methods are mainly retrospective analysis tools and do not allow efficient gradual integration of new SAR images that arrive over time and it is necessary to restart part of or
the whole displacement estimation processing chain, which would be prohibitively expensive in practice and does not answer the need for operational monitoring. Therefore, it is still necessary to develop more elaborated recursive multi-temporal InSAR methods allowing for efficient gradual integration of new arriving SAR images and considering non Gaussianity of data statistics.

In this Ph.D thesis, we aim to develop a novel robust and recursive multi-temporal InSAR approach for operational displacement estimation from SAR image time series. We consider the state-of-theart Phase Linking approach as the baseline approach in which the sample covariance matrix of SAR image time series is fully exploited. First, we propose a sequential or recursive estimation of the covariance matrix of SAR images, taking into account the structure of the covariance matrix that is
directly related to the decorrelation properties of the targets under observation. Second, we integrate temporal decorrelation models (with possible unknown parameters) providing prior information on the structure of the covariance matrix in the sequential or recursive estimation process in order to improve the efficiency. We then deploy the Expectation – Maximization (EM) algorithm to estimate jointly the unknown model parameters and the covariance matrix in an
iterative way. The displacement time series can be later obtained from the properly estimated covariance matrix. This displacement times series is finally used to estimate physical parameters of the deformation source in depth. However, missing data can exist in the displacement time series, mainly due to the coherence loss that results in unreliable displacement estimations. Data gaps can hinder the full understanding of the phenomenon under observation. Therefore, the third objective
of this Ph.D consists of imputing missing data in displacement time series, with the missing data mechanism taken into account by assuming statistical laws and estimating the parameters that describe these statistical laws.

We consider the ‘’Piton de la Fournaise’’ and Merapi volcano test sites as proving ground for the developed approach in this Ph.D thesis. Both descending and ascending Sentinel-1 A/B acquisitions are available. GPS measurements from permanent GNSS stations are also available for results comparison and validation.

Profil du candidat :
The Ph.D candidate should have good skills in mathematics/statistics and/or signal/image processing. Knowledge in Interferometry SAR is appreciated.

Formation et compétences requises :
Statistics, Optimisation, Python, Remote Sensing.

Adresse d’emploi :
LISTIC, Annecy, FRANCE.

Document attaché : 202203071519_sujet_these_InSAR_en2022.pdf

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

Laboratoire/Entreprise : Université de Brest
Durée : 3 ans
Contact : dorian.cazau@ensta-bretagne.fr
Date limite de publication : 2022-07-31

Contexte :
cf Présentation du projet du pdf joint

Sujet :
Application de l’intelligence artificielle à la détection automatique d’évènements pour les observat

Profil du candidat :
Le candidat doit avoir une solide expérience en programmation et en AI, et en éventuellement mathématiques et physique (traitement du signal et propagation des ondes).

Formation et compétences requises :
Formation universitaire ou école d’ingénieurs niveau master 2 en informatique, mathématiques ET/OU en océanographie, physique, sismologie

Adresse d’emploi :
Laboratoire Geo-Ocean (https://www.geo-ocean.fr/), Technopôle Brest-Iroise, Brest 29200

Document attaché : 202203041050_Bazin_LGO_IACOUSA_fr.pdf