Examples by task

Tutorials explain decisions; examples provide the complete scripts. Choose a task below, open its example page, then use that page’s Python or notebook download. Run from a source checkout with the required example data and optional engines installed; a rendered gallery page is not evidence that the script ran on your machine. Check the paths and settings at the beginning of each script.

The organizing question

How do geophysical data and hydrological models inform one another? Read Connecting geophysical data and hydrological models for the model–data connection, then choose a direction below. Individual geophysical methods are tools within that connection, not the endpoint of the learning route.

Recommended first route

Run the model–data connection: Run MODFLOW with geophysical structure is a small MODFLOW comparison using supplied geophysical structure, with actual solver runs.

1. Ex. Loading and Processing Hydrological Model Outputs: understand the hydrological inputs.

2. Ex. ERT Workflow: From Hydrological Models to ERT responses and Inversion: connect water content to a measurement.

3. Ex. Hydrology to Multi-Geophysics Responses: compare complementary methods.

4. Choose monitoring, joint inversion or uncertainty below for your question.

For field data, start directly with the single-method examples. For mouse-driven processing and project maps, use Desktop Studio (Qt).

Hydrology to geophysical responses

Connect model states to measurements through petrophysics. Start here to understand the package’s hydrological focus.

Example	Main outcome
Load MODFLOW / ParFlow outputs	Hydrological arrays for downstream modeling
Hydrology → ERT	Resistivity models and synthetic ERT responses
Hydrology → TDEM	Layered time-domain EM modeling and inversion
Hydrology → FDEM	Frequency-domain EM responses and recovered models
One profile → multiple methods	Compare ERT, seismic, EM and gravity responses from a shared hydrological profile
3D ERT with topography	Extend hydrology-driven forward modeling to a 3D mesh

Hydrological interpretation and uncertainty

Use recovered geophysical properties to estimate hydrological quantities under explicit petrophysical assumptions. Quantify uncertainty before comparing these estimates with hydrological model states.

Example	Main outcome
Geophysical structure → MODFLOW	Run and compare hydrological responses with uniform and interpreted interfaces
Monte Carlo hydrological interpretation	Water-content and porosity estimates with uncertainty from petrophysical parameters
Sensitivity and resolution	Where an ERT survey over the MODFLOW model is sensitive, resolved and data-controlled
Ensemble data assimilation	Update MODFLOW water content with an ERT survey by EnKF and ES-MDA
Posterior uncertainty propagation	Water-content intervals from an ERT inversion’s posterior, checked against MODFLOW

Survey processing and single-method inversion

Prepare trustworthy observations and physical-property models for the hydrological comparison. These are supporting workflows; a single-method inversion alone does not update a hydrological simulation.

Example	Main outcome
Field ERT inversion	Recover a resistivity section from survey data
Seismic forward modeling	Generate travel-time data
Seismic inversion	Recover a velocity section
Airborne EM line	Calibrate and invert a line of soundings
Ground TEM: LM + HM	Fit both moments with line constraints using the bundled nine-station project
Gravity and magnetics	Inspect anomalies, forward model and run compact 3D inversion

Time-lapse monitoring

Connect evolving hydrological states with repeated geophysical observations. Generate measurements from model states, recover temporal property changes, and inspect whether their timing and magnitude support the hydrological interpretation.

Example	Main outcome
1. Generate monitoring data	Synthetic ERT measurements at multiple times
2. Invert the time series	Temporally constrained resistivity changes
3. Scale to longer series	Compare standard and memory-oriented inversion workflows
4. Add structural information	Time-lapse inversion with geological constraints

Structural constraints and joint inversion

Use complementary information while keeping each method’s data fit visible.

Example	Main outcome
Structure-constrained ERT	Use an existing structural interpretation to guide resistivity inversion
Joint ERT + seismic	Recover coupled resistivity and velocity models with cross-gradient / geostatistical constraints

AI-assisted operation

Agent Workflows explains how to use agents with these scientific workflows. Install with an AI agent covers installation only; it is not an inversion tutorial. Browser apps and example-data generators are launch / preparation tools, not additional scientific examples.

Browse all figures

Use the complete gallery for thumbnails and downloads. The categories above cover every scientific Python example currently in the gallery.