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Software Products & Codes by industry

Waste management Software & Codes

Review contamination, groundwater transport, and source-termination choices. Explore geological context and uncertainty relevant to waste disposal and management. Browse software and scientific codes relevant to this area, with methods, screenshots, and resource links.

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Choose an industry to explore relevant software and codes. Research & scientific AI brings together methods used across industries.

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Related dark EnviCloud workflow showing synthetic wells, geological horizons, faults, and terrain.
Platforms & applicationsSubsurface product

GeoML

  • Applies science-informed AI to the exploration, development, and use of geologic reservoirs.
  • Addresses subsurface energy extraction and storage, carbon sequestration, water supply, and resource assessment.
  • Connects reservoir understanding with modeling and decision support across a project life cycle.
Related contaminant source identification workflow with monitoring wells and concentration-history fits.
Platforms & applicationsEnvironmental product

ChemML

  • Uses science-informed machine learning to characterize and predict contamination processes.
  • Connects site information with tools for contaminant source interpretation, uncertainty, and remediation decisions.
  • Supports environmental teams evaluating complex geochemical behavior and management options.
Related Geysers Seismicity workflow in dark mode, showing clustered earthquake observations.
Platforms & applicationsSeismicity product

LAPIS

  • Combines seismic and injection information with physics-informed AI for induced-seismicity research.
  • Investigates stress changes and deformation associated with geothermal development and other subsurface activities.
  • Supports hazard interpretation and operational planning while keeping site evidence and uncertainty visible.
Related MADS parameter-calibration and model-diagnostics workflow in dark mode.
Inference & uncertaintyPublic scientific code

BIGUQ.jl

  • Applies Bayesian Information Gap Decision Theory to uncertainty and decision analysis.
  • Supports experimental-design and robustness questions within MADS workflows.
  • Includes an example comparing source-termination decisions through robustness curves.