High-Resolution High-Performance Seismic Imaging

1 May 2024
Job Information

Organisation/Company

Mines Paris PSL

Research Field

Computer science » Informatics
Computer science » Digital systems
Physics

Researcher Profile

Recognised Researcher (R2)
Leading Researcher (R4)
First Stage Researcher (R1)
Established Researcher (R3)

Country

France

Application Deadline

17 May 2024 - 22:00 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

1 Oct 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Description of the topic and the problematic

Cutting-edge numerical techniques for seismic imaging are considered at different scales. Among them,
advanced methods relates to full-waveform inversion (FWI) and to reverse time migration (RTM). They
are essential to obtain quantitative images of the sub-surface, for example for the identification and
placement of hydrocarbon reservoirs and for the characterisation of the subsurface material like poros-
ity, viscosity, acoustic velocity, localisation, dimensions, and others. Such applications are extremely
important for the efficiency of oil and gas exploration, such as in coastal regions where hydrocarbon
reservoirs are found a few kilometres deep under salt bodies. One single well drilled in the wrong location
can waste millions of euros and delay the production for weeks or months. Even after the production
starts, it is important to track how reservoirs evolve (i.e., the quantities of remaining hydrocarbon, the
flow and pressure of fluids in porous rocks, where to inject fluids to increase pressure, etc) to maximise
production. But there are other applications. Seismic imaging methods can also be employed for the
location of of both onshore and offshore sites for the storage of carbon dioxide storage. Studies on the
use of caves for the capture and storage of CO2 are recent but with great potential for environmental
sustainability [RCGI (2018)]. The topic is really active from both its importance and its potential of
cutting-edge research activities Operto and Virieux (2009); Igel (2017); Munk and Wunsch (1982); Wu
and Zhang (2018); Zhang et al. (2019); Operto et al. (2013); Shi et al. (2020); Thrastarson et al. (2020).
In the geotechnical context or for environmental studies, quantiative seismic approaches are not yet fully
developed.

Full-waveform inversion (FWI) is a data-fitting procedure based on full-wavefield modelling to extract
quantitative information from collected data. In conventional time-domain FWI
workflows, most of the time is spent in the computation of the forward and adjoint wave propagation. The
kernel of this process involves the numerical solution of partial differential equations (PDEs) that model
the propagation of acoustic waves in multi-layer subsurface materials. The FWI workflow minimises
both amplitude and phase differences between the signals that are recorded with a set of microphones
(or hydrophones) located near the surface. The model is incrementally modified so that the functional
that represents the error is sufficiently reduced [Virieux and Operto (2009)]. The final objective is to
reconstruct various parameters of the materials, such as the velocities of P-waves and S-waves, density,
anisotropy, and attenuation. The reverse-time migration (RTM) is a quite similar process which uses
least-squares minimisation of the misfit between recorded and modelled data. One difference between
RTM and FWI is that the seismic wavefield recorded at the receiver is back propagated in reverse time
migration, whereas the data misfit is back propagated in the waveform inversion. Besides their application
for the identification and placement of hydrocarbon reservoirs, FWI and RTM are important tools for
studies for the use of caves for the capture and storage of carbon.

As a numerically sensitive inversion problem, FWI is computationally challenging. For instance,
cycle-skipping (related to the intrinsic oscillating nature of seismic waves) and non-linearity will lead to
convergence toward a local minimum. To mitigate this issue, there is a strong need for: (1) multi-scale
strategies, which progressively incorporate shorter wavelengths in the parameter space; (2) differential
approaches, in which the gradient and the Hessian operators can be efficiently estimated even in the
presence of multi-layer materials with sharp interfaces and diverse geological shapes; (3) better modelling
of the wave propagation physics; (4) noise reduction, and (5) efficient absorbing techniques along the
domain boundaries to mitigate spurious reflections.

In summary, FWI tries to iteratively approximate synthetic data (produced by simu-
lating waves propagating through an estimated velocity model) of the subsurface to actual
reflection data that were acquired during a seismic survey.

FWI and RTM workflows are known to be computationally heavy. Typically, the execution of an FWI
scenario can take several months on a Petaflop/s cluster with data that are collected with a frequency
within the range from 2 to 10 Hz. If the desired frequency if multiplied by a factor 2, the CPU time is
typically 16 times larger in 3d. As better quality data can be collected and made available, and because
higher resolution images are requested (i.e., processing higher frequency data) to shorten the “time to first
oil” and improve the industry efficiency, the computational cost of FWI will keep increasing significantly
in the coming years.

In summary, the existing computational methods for solving full-waveform inversion are
not only computationally expensive, but also yield low-resolution results because of the
ill-posedness and cycle skipping issues of FWI.

 

Main objectives

The main objective of this PhD proposal is to investigate high performance techniques for accelerating
seismic imaging applications such as FWI and RTM in the perspective of considering large-scale HPC
systems including exascale ones.
The expected focus and contribution of this PhD proposal is twofold. First, we will focus on stencil
codes (they are crucial for FWI and RTM problems), which are known to be computationally challenging
with high-performance processors [Tadonki (2017); Haggui et al. (2018)]. Stencil patterns can be fine-
grained for point-wise computations like in image processing or coarse-grained for macroscopic schemes
like what can be found with simulation codes in experimental physics [Tadonki (2017)]. The processing
power of modern processors is increasing, but their memory systems are more and more complex. Mapping
stencil codes efficiently on such processors is difficult and is still under intensive studies. Second, an
efficient parallelization of stencil codes on large cluster requires to skillfully program the compute nodes
and to minimize data exchanges. We will pay a particular attention on the non-uniform memory access
(NUMA) model, since most of large multi-core processors are of NUMA type. Efficient programming
for NUMA processors is a hot topic and the results of our achievements will certainly stand as an HPC
breakthrough, especially with exascale considerations. In addition, implementation of seismic imaging
codes on specific devices like GPUs and FPGAs still needs to be deeply explored for their potential
efficiency in terms of computing power and energy. The two main expected benefits are (1) to be able to
run FWI at higher frequencies for higher resolution images and (2) to possibly derive uncertainties on the
final result, instead of a unique deterministic solution. Such uncertainty maps are indeed not accessible
yet for computational reasons.

 

References

• Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., and Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88:442–452.
• Igel, H. (2017). Computational seismology: a practical introduction. Oxford University Press.
• Munk, W. H. and Wunsch, C. I. (1982). Observing the ocean in the 1990s. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 307(1499):439–464.
• Operto, S., Gholami, Y., Prieux, V., Ribodetti, A., Brossier, R., Metivier, L., and Virieux, J. (2013). A guided tour of multiparameter full-waveform inversion with multicomponent data: From theory to practice. The Leading Edge, 32(9):1040–1054.
• Operto, S. and Virieux, J. (2009). An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6):WCC1.
• RCGI (2018). Rcgi scientists study storage of carbon-rich nat. gas in underwater salt caves. https://www.rcgi.poli.usp.br/rcgi-scientists-study-storage-of-carbon-ri… .
• Shi, J., Beretta, E., de Hoop, M. V., Francini, E., and Vessella, S. (2020). A numerical study of multi-parameter full waveform inversion with iterative regularization using multi-frequency vibroseis data. Computational Geosciences, 24(1):89–107.
• Tadonki, C. (2017). Scalable numa-aware wilson-dirac on supercomputers. pages 315–324.
• Thrastarson, S., van Driel, M., Krischer, L., Boehm, C., Afanasiev, M., van Herwaarden, D.-P., and Fichtner, A. (2020). Accelerating numerical wave propagation by wavefield adapted meshes. Part II: full-waveform inversion. Geophysical Journal International, 221(3):1591–1604.
• Virieux, J. and Operto, S. (2009). An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6).
• Wu, H. and Zhang, B. (2018). A deep convolutional encoder-decoder neural network in assisting seismic horizon tracking.
• Zhang, Y., Gao, J., Han, W., and He, Y. (2019). A discontinuous Galerkin method for seismic wave propagation in coupled elastic and poroelastic media. Geophysical Prospecting, 67(5):1392–1403.

Funding category: Contrat doctoral
Contrat doctoral classique
PHD title: Ingénierie des Systèmes, Matériaux, Mécanique, Energétique
PHD Country: France

Requirements
Specific Requirements

The candidate must have a solid background in mathematics and numerical methods as well as in programming, with a taste for physics and geophysical applications or signal processing. Fluency in English is essential (communications in conferences and publication of scientific articles during the thesis).

Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

Mines Paris PSL

Country

France

Geofield

Where to apply

Website

https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/123540

Contact

Website

http://www.mines-paristech.fr/

STATUS: EXPIRED