Build forward models and solve geophysical inversions with SimPEG.
npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill simpeg
Please help me install this Agent Skill. Skill: simpeg Source: https://github.com/SteadfastAsArt/geoscience-skills/tree/main/simpeg Command: npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill simpeg
SimPEG provides a modular framework to build forward models and solve geophysical inversion problems across multiple methods, enabling end-to-end workflows from data to recovered subsurface properties.
Create a 2D mesh, define a simple DC resistivity survey, generate synthetic data, and run a basic inversion.
High-intent search queries and answers about installing and using this skill.
To run a geophysical inversion, you first create a TensorMesh or TreeMesh using discretize, define the physics simulation, compute the data misfit, apply regularization, and execute the optimization loop to recover subsurface properties.
Yes, you can perform joint inversion by combining multiple datasets and applying parameter maps to simultaneously invert multi-method or multi-physics problems within the same framework.
The framework supports forward modeling and inversion for DC resistivity, magnetics, gravity, electromagnetic (EM), and magnetotelluric (MT) methods using modular simulation classes.
You can generate synthetic data by creating a 2D mesh, defining a DC resistivity survey geometry, building the forward simulation, and then computing predicted data for your synthetic subsurface model.
Yes, a Python environment with SimPEG, discretize, and numpy is required to build models and compute inversions, while matplotlib is needed for visualizing meshes and recovered subsurface properties.
For complex geometries, use TreeMesh from the discretize package to create adaptive octree meshes, which provide flexible discretization compared to standard TensorMesh grids for forward modeling.
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