Solve seismic wave propagation PDEs with symbolic Devito equations.
npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill devito
Please help me install this Agent Skill. Skill: devito Source: https://github.com/SteadfastAsArt/geoscience-skills/tree/main/devito Command: npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill devito
Automates high-fidelity PDE-based seismic wave propagation modeling by providing a symbolic, code-generated solver that accelerates research and production workflows.
Launch a simple 2D acoustic forward model to generate and run a Devito stencil.
High-intent search queries and answers about installing and using this skill.
Seismic wave propagation PDEs are solved by specifying the equations symbolically, which triggers automatic code generation for 2D/3D finite-difference simulations. This approach automates high-fidelity modeling and accelerates research workflows.
FWI and RTM workflows are executed using the symbolic PDE solver to generate finite-difference stencils with absorbing boundary conditions. This supports forward modeling to generate shot gathers and enables large-scale seismic simulations.
Devito supports GPU-ready code generation and optional parallelism for high-performance finite-difference simulations. This allows seamless Python integration to run large-scale seismic wave propagation tasks efficiently.
You need both numpy and the Devito library installed to perform symbolic PDE code generation. These dependencies provide the foundational array operations and symbolic computation required to generate and run stencils.
Automated code generation is needed for finite-difference wave propagation when rapid prototyping or large-scale production simulations are required. It avoids manual stencil coding and accelerates high-fidelity PDE-based research.
You can use the symbolic solver for both 2D and 3D acoustic forward modeling to generate and run Devito stencils. This produces shot gathers and handles complex seismic wave propagation scenarios efficiently.
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