# Pygimli

> Multi-method geophysical modelling and inversion framework. Use when Claude needs to: (1) Perform electrical resistivity tomography (ERT) inversion, (2) Run seismic refraction tomography (SRT), (3) Model induced polarization (IP) data, (4) Simulate ground penetrating radar (GPR), (5) Create finite element meshes for geophysical problems, (6) Perform joint inversions of multiple datasets, (7) Forward model geophysical responses, (8) Analyze time-lapse monitoring data.

- Skill: `steadfastasart/pygimli` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add steadfastasart/pygimli`
- Raw SKILL.md: https://api.skillmd.com/api/skills/steadfastasart/pygimli/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: SteadfastAsArt (https://skillmd.com/u/steadfastasart)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/steadfastasart/pygimli

---


# pyGIMLi - Geophysical Inversion

## Quick Reference

```python
import pygimli as pg
from pygimli.physics import ert, srt

# Load ERT data
data = ert.load("survey.ohm")

# Invert
mgr = ert.ERTManager(data)
model = mgr.invert(lam=20, verbose=True)

# View result
mgr.showResult()
```

## Key Classes

| Class | Purpose |
|-------|---------|
| `pg.Mesh` | Finite element meshes |
| `pg.DataContainer` | Survey data and geometry |
| `pg.Inversion` | Base inversion framework |
| `ert.ERTManager` | ERT processing and inversion |
| `srt.SRTManager` | Seismic refraction inversion |

## Essential Operations

### Load and View ERT Data
```python
import pygimli as pg
from pygimli.physics import ert

data = ert.load("survey.ohm")
print(f"Measurements: {data.size()}")
ert.showData(data)  # Pseudosection
```

### ERT Inversion
```python
from pygimli.physics import ert

mgr = ert.ERTManager(data)
model = mgr.invert(
    lam=20,          # Regularization
    verbose=True
)
mgr.showResult()
resistivity = mgr.model
```

### Seismic Refraction
```python
from pygimli.physics import srt

data = srt.load("traveltimes.sgt")
mgr = srt.SRTManager(data)
model = mgr.invert(lam=30, zWeight=0.3)
mgr.showResult()
```

### Create Custom Mesh
```python
import pygimli as pg
from pygimli.physics import ert

data = ert.load("survey.ohm")
mesh = pg.meshtools.createParaMesh(
    data.sensors(),
    quality=34.0,
    paraMaxCellSize=5,
    boundary=2
)
pg.show(mesh)
```

### Save and Export
```python
# Save mesh and model
mgr.mesh.save("result_mesh.bms")
pg.save(model, "resistivity_model.vector")

# Export to VTK for ParaView
mgr.mesh.exportVTK("result", mgr.model)
```

## Array Types

| Code | Array |
|------|-------|
| `wa` | Wenner-alpha |
| `wb` | Wenner-beta |
| `dd` | Dipole-dipole |
| `pd` | Pole-dipole |
| `pp` | Pole-pole |
| `slm` | Schlumberger |
| `gr` | Gradient |

## Data Formats

| Format | Extension | Description |
|--------|-----------|-------------|
| BERT/pyGIMLi | .ohm | Unified data format |
| Syscal | .txt | IRIS export |
| Res2DInv | .dat | 2D inversion format |
| ABEM | .ohm | ABEM Terrameter |
| SRT | .sgt | Seismic traveltimes |

## When to Use vs Alternatives

| Scenario | Recommendation |
|----------|---------------|
| Standard ERT inversion with common arrays | **pyGIMLi** - simplest API, built-in array types |
| Seismic refraction tomography (SRT) | **pyGIMLi** - integrated SRT manager |
| Multi-method inversion (DC, magnetics, gravity, EM) | **SimPEG** - broader method coverage |
| Commercial ERT processing with reporting | **Res2DInv** - industry standard, GUI-based |
| Custom forward operators or research flexibility | **SimPEG** - more modular design |
| FEM-based geophysical modelling | **pyGIMLi** - native FEM mesh support |

**Choose pyGIMLi when**: You need near-surface geophysical inversion (ERT, SRT, IP) with
minimal code. Its manager classes (`ERTManager`, `SRTManager`) handle the full workflow
from data loading to inversion to visualization in a few lines.

**Avoid pyGIMLi when**: You need methods beyond near-surface (use SimPEG), or you
require a commercial-grade reporting pipeline.

## Common Workflows

### ERT data inversion and visualization

- [ ] Load ERT data file with `ert.load("survey.ohm")`
- [ ] Inspect data: check measurement count with `data.size()`, plot pseudosection
- [ ] Remove outliers or bad data points
- [ ] Create `ERTManager` with data
- [ ] Run inversion: `mgr.invert(lam=20)` (start with higher lambda)
- [ ] Check chi-squared value (target ~ 1)
- [ ] Visualize result with `mgr.showResult()`
- [ ] Export mesh and model to VTK for ParaView: `mgr.mesh.exportVTK()`
- [ ] Adjust lambda and zWeight if needed, re-invert

## Inversion Tips

1. **Start with higher lambda** (50-100) and decrease
2. **Check data quality** - remove outliers before inversion
3. **Use zWeight < 1** for layered structures
4. **Check coverage** - low coverage = poorly resolved
5. **Chi-squared ~ 1** indicates good fit without overfitting

## References

- **[Geophysical Methods](references/methods.md)** - Supported methods and workflows
- **[Mesh Generation](references/mesh.md)** - Mesh creation and quality control

## Scripts

- **[scripts/ert_inversion.py](scripts/ert_inversion.py)** - Complete ERT inversion workflow