Agent skill

simpeg

Simulation and Parameter Estimation in Geophysics. Framework for geophysical forward modeling and inversion. Use when Claude needs to: (1) Run geophysical inversions (DC resistivity, magnetics, gravity, EM), (2) Create forward models for potential fields or electromagnetic methods, (3) Build survey geometries and receiver configurations, (4) Design mesh discretizations for simulations, (5) Apply regularization and optimization to inverse problems, (6) Model subsurface physical properties from geophysical data.

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SKILL.md

SimPEG - Geophysical Simulation & Inversion

Quick Reference

python
from discretize import TensorMesh
from simpeg.electromagnetics.static import resistivity as dc
from simpeg import maps, data_misfit, regularization, optimization
from simpeg import inverse_problem, inversion, directives
import numpy as np

# Create mesh
hx, hz = np.ones(100) * 10, np.ones(50) * 5
mesh = TensorMesh([hx, hz], origin='CN')

# Forward model
simulation = dc.Simulation2DNodal(mesh, survey=survey, sigmaMap=maps.ExpMap(mesh))
dpred = simulation.dpred(model)

# Inversion
dmis = data_misfit.L2DataMisfit(data=data, simulation=simulation)
reg = regularization.WeightedLeastSquares(mesh)
opt = optimization.InexactGaussNewton(maxIter=20)
inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)
inv = inversion.BaseInversion(inv_prob, directiveList=[...])
mrec = inv.run(m0)

Key Classes

Class Purpose
TensorMesh, TreeMesh Discretization (regular grid, adaptive octree)
Survey Data acquisition geometry
Simulation Forward modeling engine
Data Observed/predicted data container
InvProblem Combines misfit, regularization, optimization

Essential Operations

Create Mesh

python
from discretize import TensorMesh

# 2D mesh (x, z) - centered in x, top at z=0
hx, hz = np.ones(100) * 20, np.ones(50) * 10
mesh = TensorMesh([hx, hz], origin='CN')

# 3D mesh
mesh = TensorMesh([np.ones(50)*25, np.ones(50)*25, np.ones(30)*10], origin='CCN')

DC Resistivity Survey

python
from simpeg.electromagnetics.static import resistivity as dc

elec_locs = np.c_[np.linspace(-95, 95, 20), np.zeros(20)]
source_list = []
for i in range(17):  # dipole-dipole
    rx = dc.receivers.Dipole(elec_locs[[i+2]], elec_locs[[i+3]])
    src = dc.sources.Dipole([rx], elec_locs[i], elec_locs[i+1])
    source_list.append(src)
survey = dc.Survey(source_list)

Forward Model

python
model = np.ones(mesh.nC) * 100  # 100 ohm-m
simulation = dc.Simulation2DNodal(mesh, survey=survey, sigmaMap=maps.ExpMap(mesh))
dpred = simulation.dpred(np.log(1/model))  # input: log(conductivity)

Inversion

python
from simpeg import data_misfit, regularization, optimization
from simpeg import inverse_problem, inversion, directives, data

obs_data = data.Data(survey, dobs=dobs, standard_deviation=0.05*np.abs(dobs))
dmis = data_misfit.L2DataMisfit(data=obs_data, simulation=simulation)
reg = regularization.WeightedLeastSquares(mesh, alpha_s=1e-4, alpha_x=1, alpha_z=1)
opt = optimization.InexactGaussNewton(maxIter=20)
inv_prob = inverse_problem.BaseInvProblem(dmis, reg, opt)
dir_list = [directives.BetaSchedule(coolingFactor=2), directives.TargetMisfit()]
inv = inversion.BaseInversion(inv_prob, directiveList=dir_list)
mrec = inv.run(m0)

Common Maps

Map Description Use Case
IdentityMap No transformation Susceptibility, density
ExpMap exp(m) Log-parameterized conductivity
ReciprocalMap 1/m Resistivity to conductivity
Wires Split model Joint inversion

Physical Property Ranges

Property Typical Range Units
Resistivity 1 - 10000 ohm-m
Conductivity 0.0001 - 1 S/m
Susceptibility 0 - 0.1 SI
Density contrast -1 to 1 g/cc

When to Use vs Alternatives

Scenario Recommendation
Multi-method geophysical inversion (DC, magnetics, gravity, EM) SimPEG - broadest method coverage
Near-surface ERT with standard arrays pyGIMLi - simpler API, built-in array support
ERT-focused inversion with GUI export pyGIMLi - better ERT-specific tooling
Custom forward modelling with flexible physics SimPEG - modular design, easy to extend
Joint inversion of multiple geophysical datasets SimPEG - built-in support via Wires maps
Commercial ERT processing Res2DInv / Res3DInv - industry standard

Choose SimPEG when: You need a unified framework for multiple geophysical methods, custom forward operators, or research-grade flexibility. Its modular design (mesh + survey + simulation + inversion) suits complex and non-standard problems.

Avoid SimPEG when: You only need standard ERT inversion (pyGIMLi is faster to set up), or you need a turnkey commercial solution.

Common Workflows

Run DC resistivity inversion from survey data

  • Define electrode locations and build dipole-dipole (or other) survey geometry
  • Create TensorMesh or TreeMesh with appropriate cell sizes
  • Set up dc.Simulation2DNodal with mesh, survey, and ExpMap
  • Load observed data into data.Data with standard deviations
  • Configure L2DataMisfit, WeightedLeastSquares regularization, and optimizer
  • Set directives: BetaSchedule, TargetMisfit
  • Build BaseInvProblem and BaseInversion
  • Run inversion with inv.run(m0) using a homogeneous starting model
  • Plot recovered model and compare observed vs predicted data
  • Check data misfit convergence (target chi-squared ~ 1)

Tips

  1. Use log parameters for positive quantities (resistivity, susceptibility)
  2. Start with coarse mesh and refine after initial tests
  3. Check data fit by plotting observed vs predicted
  4. Tune regularization to balance data fit and model smoothness
  5. Use TreeMesh for 3D problems to improve efficiency

References

  • Survey Types - Survey configurations and receiver types
  • Mesh Types - Mesh discretization and refinement

Scripts

  • scripts/dc_inversion.py - Complete DC resistivity inversion example

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