Seismic Bayesian Optimal Experiment Design in Python

This code is a Python implementation of Bayesian optimal experiment design (BOED) for seismic monitoring networks. This code provides the tools necessary to analyze and optimize seismic monitoring networks. Currently we target the location problem in which we want to study how well the network will identify the location of an event and then optimize the network to provide better locations. The user can specify models for generating synthetic data and assessing the likelihood of that synthetic data for different sensors and events in the domain of candidate events. The code is designed to use MPI so that it can run on HPC resources because OED is computationally expensive.

See Installation and the first tutorial, Getting Started to begin using this code. More details on the theory and application can be found in the accompanying paper.

Seismic Bayesian OED

• Package Installation
• Literature
• What’s New

Tutorials

• Getting Started: Network Analysis
• Getting Started: Network Optimization
• Performing bounded optimization
• Bounded Optimization
• Writing input files
• Customizing the event prior
• Sampling synthetic data
• Constructing likelihood models

API Reference

• seismic-boed