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name: pygimli description: | Multi-method geophysical modelling and inversion framework. Use when the agent 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. license: MIT metadata: version: 1.0.1 author: Geoscience Skills tags: '["Geophysical Inversion", "ERT", "Seismic Refraction", "IP", "Near-Surface", "FEM"]' dependencies: '["pygimli>=1.4.0", "numpy", "matplotlib"]' complements: '["simpeg", "verde", "pyvista"]' workflow_role: modelling skill_type: domain

pyGIMLi - Geophysical Inversion

Quick Reference

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

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

from pygimli.physics import ert

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

Seismic Refraction

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

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

# 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 - Supported methods and workflows
• Mesh Generation - Mesh creation and quality control

Scripts

• scripts/ert_inversion.py - Complete ERT inversion workflow