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Getting started:

  • Workflow
  • Requirements
  • Installation
  • Usage
  • Velocity models
  • TieBeNN package
  • About
TieBeNN
  • TieBeNN documentation
  • View page source

TieBeNN

TieBeNN documentation

TieBeNN (TIEfenBEstimmung mittels Neuronaler Netze, Depth Estimation using Neural Networks) is an event-based Python-wrapper that leverages several tools—some machine-learning-based, some traditional—to automatically generate phase picks for probabilistic hypocenter estimation of local events using NonLinLoc.

Getting started:

  • Workflow
  • Requirements
    • Optional
  • Installation
    • Create a virtual environment
    • Installing TieBeNN and its Python dependencies
    • Installing NonLinLoc and setting paths
  • Usage
    • Input file
    • Syntax
    • Output
  • Velocity models
    • List of available velocity models
    • Structure of NonLinLoc 1D velocity model files
    • How to add a new 1D velocity model
  • TieBeNN package
    • Subpackages
      • tiebenn.tools.gammaasoc module
      • tiebenn.tools.nicetools module
      • tiebenn.tools.nonlinloc module
      • tiebenn.tools.pyoctools module
      • tiebenn.tools.retrieve_data module
      • tiebenn.tools.sb_tools module
      • tiebenn.tools.utm module
      • tiebenn.tools.velocity_models module
      • tiebenn.tools.visualization module
  • About
    • Citing TieBeNN
    • Acknowledgements
    • License
    • Links & availability

Indices and tables

  • Index

  • Module Index

  • Search Page

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