Near-real-time design of experiments for seismic monitoring of volcanoes

TL;DR

This paper introduces a Bayesian experimental design code package for optimizing seismic sensor networks on volcanoes, enabling rapid, tailored deployment for improved eruption monitoring and hazard assessment.

Contribution

It is the first to optimize travel-time, amplitude, and source location methods simultaneously for volcano seismic networks, making design adaptable and quick to implement.

Findings

Automated design process within minutes using generic data.

Refinement of sensor placement achievable within hours with specific prior info.

Code links to global volcano databases for rapid deployment.

Abstract

Monitoring the seismic activity of volcanoes is crucial for hazard assessment and eruption forecasting. The layout of each seismic network determines the information content of recorded data about volcanic earthquakes, and experimental design methods optimise sensor locations to maximise that information. We provide a code package that implements Bayesian experimental design to optimise seismometer networks to locate seismicity at any volcano, and a practical guide to make this easily and rapidly implementable by any volcano seismologist. This work is the first to optimise travel-time, amplitude and array source location methods simultaneously, making it suitable for a wide range of volcano monitoring scenarios. The code-package is designed to be straightforward to use and can be adapted to a wide range of scenarios, and automatically links to existing global databases of topography and properties of volcanoes worldwide to allow rapid deployment. Any user should be able to obtain an initial design within minutes using a combination of generic and volcano-specific information to guide the design process, and to refine the design for their specific scenario within hours, if more specific prior information is available.