A Scale-Aware Framework for Characterizing Multiscale Aquifer Heterogeneity by Coordinating Machine Learning and Stochastic Modeling

Scale-aware aquifer heterogeneity workflow

The files include the four-stage workflow code, de-identified restricted inputs, and public run outputs for scale-aware aquifer heterogeneity characterization. Raw borehole records and full processed geological input tables are not redistributed because reuse of the source records is restricted. The included input tables are randomized and de-identified, and are sufficient to run the same model structure used in the manuscript.

Contents

– `code/run_four_stage_workflow.py`: four-stage workflow entry point.
– `code/external_geost_engine/ZXDZ.exe`: GEOST executable used by the transition-probability workflow.
– `data/sample_inputs/`: de-identified and randomly shuffled sample inputs.
– `outputs/`: output directory created by the workflow script.
– `docs/`: metadata, data dictionary, and data-availability text.
– `tests/`: verification test for the runnable workflow.
– `requirements.txt`: Python packages required by the workflow.

Quick start

Create a Python environment with the required packages:

“`bash
pip install -r requirements.txt
“`

Run the four-stage workflow from the root directory of this package:

“`bash
python code/run_four_stage_workflow.py
“`

The workflow writes outputs to:

“`text
outputs/four_stage_workflow_run/
“`

Expected output files:

– `stage1_domain_ensemble.csv`: sediment-bedrock ensemble output.
– `stage2_weathering_ensemble.csv`: weathered-unweathered bedrock ensemble output.
– `stage3_structural_realizations.csv`: clay, sand, weathered bedrock, and unweathered bedrock structural realizations.
– `stage3_geost_indicator_points.csv`: clay-sand conditioning indicators for the GEOST-style facies module.
– `stage3_reference_transition_probabilities.csv`: reference transition-probability curves from the restricted input.
– `stage3_geost_parameter_samples.csv`: facies proportion and mean-length parameters used by each public structural realization.
– `stage3_external_geost_engine.json`: record of the archived external GEOST executable and public run mode.
– `stage4_k_field_realizations.csv`: facies-conditioned prior log10(K) realizations.
– `stage4_k_pilot_points.csv`: pilot-point values used by the workflow.
– `stage4_class_statistics.csv`: class-wise statistics for the generated prior K-field realizations.
– `four_stage_section.png`: section-style visualization of one structural and K-field realization.
– `four_stage_summary.json`: run metadata and basic diagnostics.

Workflow summary

The four-stage workflow uses de-identified grid points and conditioning lithology labels. It first trains a five-classifier ensemble to separate sediment and bedrock domains. It then trains a second ensemble to separate weathered and unweathered bedrock. Within the predicted sediment domain, it uses a GEOST-style transition-probability modul