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Updated: Jan 17, 2026

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
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Capturing expert uncertainty: ICC-informed soft labelling for volcano-seismicity.

Sam Mitchinson1, Jessica H Johnson1, Ben Milner2

  • 1School of Environmental Sciences, University of East Anglia, Norwich, UK.

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|September 19, 2025
PubMed
Summary

This study introduces ICC-informed soft labels for volcano-seismic signals, improving machine learning accuracy by quantifying expert disagreement. This method enhances volcano monitoring and eruption forecasting by capturing classification uncertainty.

Keywords:
Inter-rater reliabilityIntraclass correlation coefficientRuapehuUncertaintyVolcano-seismicity

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Area of Science:

  • Geophysics
  • Seismology
  • Machine Learning

Background:

  • Volcano-seismic signal classification is crucial for monitoring and eruption forecasting.
  • Traditional methods may not account for expert judgment variability and uncertainty.
  • Inter-expert agreement is often overlooked in seismic data analysis.

Purpose of the Study:

  • To develop a novel method for quantifying inter-expert agreement in volcano-seismic signal classification.
  • To incorporate this agreement measure into probabilistic, ICC-informed soft labels for machine learning.
  • To enhance the accuracy, robustness, and transferability of machine learning models in volcanology.

Main Methods:

  • A global survey of 89 experts classifying 80 volcano-seismic events from Ruapehu, New Zealand.
  • Utilized the intraclass correlation coefficient (ICC) to quantify inter-expert agreement.
  • Developed a soft labeling methodology weighting class probabilities by ICC scores.

Main Results:

  • Single-rater scores showed poor agreement, even for established volcano-tectonic (VT) and long-period (LP) classifications.
  • Combining multiple expert ratings significantly improved reliability for VT and LP signals.
  • Substantial expert disagreement persisted for hybrid (HYB) and other (OT) categories.
  • ICC-informed soft labels effectively captured and reflected expert uncertainty.

Conclusions:

  • ICC-informed soft labels offer a robust alternative to hard labels by explicitly capturing classification uncertainty.
  • This probabilistic approach can significantly enhance machine learning model performance in volcano monitoring.
  • The methodology represents a fundamental shift in labeling and interpreting volcano-seismic data for automated frameworks.