GeoBrain

GeoBrain is an open, modular platform for Geoscientific Bayesian Reasoning with Artificial Intelligence, built for integrated subsurface modeling.

It combines differentiable physics, Bayesian inference and deep learning so that a whole workflow, from a geostatistical earth model through rock physics to a geophysical forward model and back out as an inversion, is one computational graph. Every forward model is a composable operator, every operator declares how it differentiates, and the same problem object serves a deterministic optimiser and a posterior sampler alike.

Where to start

New here? Installation then Quick start is ten minutes. If you would rather see it work first, every figure in the examples gallery is the unedited output of a script you can run.

The whole platform on one screen: a density model, the gravity it produces, and the model recovered back out of that data. From examples/00_showcase/01_gravity_inversion.py.

The four ideas

Everything else in GeoBrain, from the physics families to the samplers to the neural parameterizations, is something you can add without touching these.

An operator is a contract, not a function

Every forward model is a ForwardOperator that declares a DifferentiabilitySpec: its trainable inputs, its outputs, and how its gradient is obtained: full autograd, an implicit adjoint, or a hand-written VJP. The declaration is checked against finite differences, so “this is differentiable” is something the platform can be held to rather than a promise in a docstring.

Physics composes with @

Chain operators in series, run them in parallel as an OperatorBundle, and the composition’s contract is derived from its links: trainable inputs from the entry link, outputs from the terminal link, differentiability level the weakest of the members. A chain that cannot be honoured is refused when you build it, not when you run it.

A mesh declares capabilities; physics declares requirements

TensorMesh, OctreeMesh and UnstructuredMesh each satisfy named protocols, and each physics says which it needs, so “this kernel cannot run on that mesh” is a type error rather than a wrong answer. A differentiable MeshProjection bridges them, which is what lets one joint inversion run the wave equation on a structured grid and gravity straight on triangles.

One problem, two doors

The same InverseProblem serves a deterministic optimiser (create_inverter().run()) and a posterior sampler (as_posterior().sample("nuts")). Uncertainty quantification is a method call on the object you already built, not a second pipeline to keep in step with the first.

Read the architecture in full, or see all four at once in examples/00_showcase/.

What is in the box

Geological modeling	kriging family, sequential and indicator simulation, variograms with fitting diagnostics, differentiable implicit modelling
Rock physics	effective medium, granular media, fluid substitution, empirical relations, petrophysics
Wave	acoustic / elastic / visco time-domain, Helmholtz with an implicit adjoint, AVO reflectivity, wavelets
Electromagnetics	DC, IP, SIP, MT, FDEM, TEM, CSEM, airborne, SP
Potential fields	gravity 2-D/3-D, magnetics with remanence, Euler deconvolution
Flow	differentiable two-phase reservoir simulation with wells
Inversion	regularizers, bounds, IRLS, gradient processors, Adam and L-BFGS
Bayesian	HMC, NUTS, Langevin, SVGD behind one Posterior, with transforms and diagnostics
Neural networks	Deep Image Prior and latent-space reparameterizations
Decision	efficacy of information, scored cell by cell from a prior ensemble