# Steadfastasart Geoscience Skills Simpeg

> SimPEG - Geophysical Simulation & Inversion

- Skill: `tomevault-io/steadfastasart-geoscience-skills-simpeg` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/steadfastasart-geoscience-skills-simpeg`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/steadfastasart-geoscience-skills-simpeg/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/steadfastasart-geoscience-skills-simpeg

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# 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](references/survey_types.md)** - Survey configurations and receiver types
- **[Mesh Types](references/mesh_types.md)** - Mesh discretization and refinement

## Scripts

- **[scripts/dc_inversion.py](scripts/dc_inversion.py)** - Complete DC resistivity inversion example

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