arXiv subjects

Guillaume Barnier

Publications and source records attributed to Guillaume Barnier.

4 recordsLinked to original sources

arXiv2026

RAMSES-GPU: Cell-by-Cell Adaptive Mesh Refinement with Magneto-Hydrodynamics and Self-Gravity on Graphics Processing Units

We present the implementation and optimization of the cosmological simulation code RAMSES on Graphics Processing Units (GPUs) using CUDA Fortran. This accelerated version ports the main computational routines, including hydrodynamics, particle dynamics, and self-gravity, to multi-GPU architectures. We detail our strategy for managing cell-by-cell Adaptive Mesh Refinement (AMR) on the GPU, utilizing bucket sort with prefix sums for AMR level sorting, radix sort via the CUB library for Hilbert key ordering, and an fnv64 hash table with linear probing for fast spatial indexing. Portability across diverse hardware architectures is achieved via a dispatcher and C-Fortran wrappers, calling CUDA, HIP, and Metal kernels directly translated from the CUDA Fortran framework. Hydrodynamics updates are executed via a Godunov MUSCL-Hancock HLLC Riemann solver managed through a three-tier shared-memory kernel architecture (named rock, paper, and scissor). Particle mass deposition uses Cloud-in-Cell (CIC) interpolation optimized with atomic additions or prefix sums, combined with a kick-drift-kick time integration pusher. Self-gravity is handled via a Multigrid (MG) Poisson solver performing hierarchical V-cycles on individual levels. Performance benchmarks conducted on NVIDIA A100 and H200 GPUs demonstrate substantial accelerations compared to multi-core CPUs, yielding 10x up to a 100x speedup for standard test problems such as the Sedov blast wave, molecular core collapse, and cosmological simulations. Finally, we briefly discuss additional accelerated physics modules, including equilibrium cooling, polytropic equations of state, ideal and non-ideal magneto-hydrodynamics (MHD), and stellar feedback.

astro-ph.IM↗

arXiv2022

Target-oriented elastic full-waveform inversion through extended image-space redatuming

Elastic full-waveform inversion (FWI) when successfully applied can provide accurate and high-resolution subsurface parameters. However, its high computational cost prevents the application of this method to large-scale field-data scenarios. To mitigate this limitation, we propose a target-oriented elastic FWI methodology based on a redatuming step that relies upon an extended least-squared migration process. In our approach, the surface-reflection data can be attributed to a given subsurface portion when mapped into the image space. This process allows us to reconstruct reflection data generated by a target area and recorded with a virtual acquisition geometry positioned directly above it. The redatuming step enables the application of an elastic FWI method within the target portion only. The entire workflow drastically diminishes the overall cost of the surface-data inversion and allows the retrieval of accurate elastic parameters of the area of interest. We demonstrate the effectiveness of our approach on a synthetic case based on the well-known Marmousi2 model and on a 3D ocean-bottom-node (OBN) pressure data recorded in the Gulf of Mexico. We first discuss the fundamental aspects of the methodology and apply the proposed workflow to the synthetic test case. We also employ the methodology on the field-data scenario and show its efficacy at correctly retrieving the elastic parameters and rock-physical properties of a gas-bearing sand reservoir positioned in proximity of a salt-dome flank.

physics.geo-ph↗

arXiv2022

Full waveform inversion by model extension: practical applications

Producing reliable acoustic subsurface velocity models still remains the main bottleneck of the oil and gas industry's traditional imaging sequence. In complex geological settings, the output of conventional ray-based or wave-equation-based tomographic methods may not be accurate enough for full waveform inversion (FWI) to converge to a geologically satisfactory Earth model. We create a new method referred to as full waveform inversion by model extension (FWIME) in which a wave-equation migration velocity analysis (WEMVA) technique is efficiently paired with a modified version of FWI. We show that our method is more powerful than applying WEMVA and FWI sequentially, and that it is able to converge to accurate solutions without the use of a good initial guess or low-frequency energy. We demonstrate FWIME's potential on five realistic and challenging numerical examples that simulate complex geological scenarios often encountered in hydrocarbon exploration. We guide the reader step by step throughout the optimization process. We show that our method can simultaneously invert all wave types with the same simple mechanism and without the need for a user-intensive hyper-parameter tuning process. In an online repository, we provide a fully-reproducible open-source software solution implemented with general-purpose graphics processing units (GPU) and with a user-friendly Python interface.

physics.geo-ph↗

arXiv2022

Full waveform inversion by model extension: theory, design and optimization

We describe a new method, full waveform inversion by model extension (FWIME) that recovers accurate acoustic subsurface velocity models from seismic data, when conventional methods fail. We leverage the advantageous convergence properties of wave-equation migration velocity analysis (WEMVA) with the accuracy and high-resolution nature of acoustic full waveform inversion (FWI) by combining them into a robust mathematically-consistent workflow with minimal need for user inputs. The novelty of FWIME resides in the design of a new cost function using the variable projection method, and a novel optimization strategy to combine the two techniques, making our approach more efficient and powerful than applying them sequentially. We observe that FWIME mitigates the need for accurate initial models and low-frequency long-offset data, which can be challenging to acquire. We generate three cycle-skipped 2D synthetic datasets, each containing only one type of wave (transmitted, reflected, refracted), and we analyze how FWIME successfully recovers accurate solutions with the same procedure for all three cases. In a second paper, we apply FWIME to challenging realistic examples where we simultaneously invert all wave modes.

physics.geo-ph↗