pygimli

name pygimli description 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. version 1.0.0 author Geoscience Skills license MIT tags ["Geophysical Inversion","ERT","Seismic Refraction","IP","Near-Surface","FEM"] dependencies ["pygimli>=1.4.0","numpy","matplotlib"] complements ["simpeg","verde","pyvista"] workflow_role modelling 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 Start with higher lambda (50-100) and decrease Check data quality - remove outliers before inversion Use zWeight < 1 for layered structures Check coverage - low coverage = poorly resolved 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