# Bayesian waveform-based calibration of high-pressure acoustic emission systems with ball drop measurements

## TL;DR

This paper presents a Bayesian waveform-based method to calibrate high-pressure acoustic emission sensors using ball drop measurements, enabling accurate in situ AE analysis during rock failure experiments.

## Contribution

It introduces a novel Bayesian calibration approach for AE sensors in high-pressure environments using waveform data from ball drops.

## Key findings

- Quantified sensor transfer function uncertainties.
- Enabled accurate in situ AE analysis at high pressures.
- Validated calibration method with waveform data.

## Abstract

Acoustic emission (AE) is a widely used technology to study source mechanisms and material properties during high-pressure rock failure experiments. It is important to understand the physical quantities that acoustic emission sensors measure, as well as the response of these sensors as a function of frequency. This study calibrates the newly built AE system in the MIT Rock Physics Laboratory using a ball-bouncing system. Full waveforms of multi-bounce events due to ball drops are used to infer the transfer function of lead zirconate titanate (PZT) sensors in high pressure environments. Uncertainty in the sensor transfer functions is quantified using a waveform-based Bayesian approach. The quantification of \textit{in situ} sensor transfer functions makes it possible to apply full waveform analysis for acoustic emissions at high pressures.