Missions

To adapt the stereotomography to seismic acquisition carried out with sparse layout of multicomponent stations (either marine or land)

Activités

- Feasibility analysis of angle measurements by polarization analysis from multi-component data using 2D synthetic examples of increasing complexity.
- Assessment of the footprint of the errors of slope measurements in the velocity models built by stereotomography.
- Application of stereotomography based on polarization analysis on seismic data collected by multi-component OBS in the eastern-Nankai trough (Tokai segment), Japan (SFJ cruise). Application to industrial 4C-OBS data from the western continental shelf, Australia.
- Extension of the method in 3D.

Compétences

- Solid geophysics
- Seismic imaging.
- Numerical methods for ODP/EDP.
- Inverse problem theory.
- Numerical optimization (gradient-based local methods).
- Signal processing (spectral analysis, sampling, filtering, deconvolution).
- Scientific computing (programmaing langage : Fortran90, OpenMP , MPI).

Contexte de travail

Stereotomography (or slope tomography) is a seismic imaging method aiming at building velocity models of the subsurface from a dense semi-automatic picking of traveltimes and slopes of seismic locally-coherent events. The slopes are defined by the horizontal component of the slowness vector at the source and receiver positions of the seismic experiment. They can be picked in common-receiver and common-source gathers when the sources and the receivers of the survey are finely sampled. One slope and the traveltime are typically used to locate a reflection point or a diffractor in the subsurface. Then, the velocity model is updated by minimizing the residuals of the second slope. In this framework, it is not possible to pick the slopes at the stations due to the large spacing between instruments. Instead, we propose to estimate the slopes at the receivers by polarization analysis of the multiple components. The accuracy with which the slopes are estimated by polarization analysis will be estimated before assessing the footprint of this accuracy in the velocity models built by stereotomography. If this feasibility analysis is conclusive, the method will be applied on real data.

The recruited researcher's work will be carried out at the Geoazur institute (https://geoazur.oca.eu/fr/acc-geoazur) and will be supervised by S. Operto and A. Ribodetti, who are geophysicists specialized in imaging methods (stereotomography, Full Waveform Inversion (FWI)). Located at Valbonne-Sophia Antipolis, Geoazur (Unité mixte de recherché 7329) is a multidisciplinary laboratory in Earth Science, which is part of University Côte d'Azur (UCA), CNRS, Observatory of Côte d'Azur (OCA) and Institute for Research and Development (IRD). It hosts 170 persons, researchers, engineers and technicians, PhD students, through seven research teams: SEISMES, MARGES, RISQUES, mouvGS, GeoMAT, Imaging & Waves, ASTROGEO-GPM and different observatories in the field of astronomy, geodesy, seismology and seabed technologies. The young researcher will belong to the team Imaging & Waves to develop the stereotomography method. To develop and run codes, the researcher will have access to several computational infrastructures such as mesocentre SIGAMM hosted by OCA (https://www.oca.eu/fr/mesocentre-sigamm), the CICADA cluster of UCA and the supercomputers of GENCI (http://www.genci.fr/fr). This research will be performed in the framework of the WIND project (https://www.geoazur.fr/WIND) sponsored by a consortium of oil companies gathering AkerBP, ExxonMobil, Petrobras, Shell et Sinopec and coordinated by S. Operto. The researcher will be part of a multi-disciplinary team gathering applied mathematicians, computer scientists and computational and applied geophysicists and will have the opportunity to develop interactions with both the academic and industrial communities of researchers involved in the field of imaging. In particular, the research will be performed in close collaboration with another researcher in charge of the develoment of automatic picking based on machine learning methods. Real datasets will be made available to test the developed method. An existing slope tomography code will be made available to test the new approach based on polarization analysis.