A Vision-Based Approach for the Analysis of Core Characteristics of Volcanic Ash

Area of Science:

• Geosciences and Remote Sensing
• Volcanology and Natural Hazards
• Image Processing and Computer Vision

Background:

• Volcanic ash fall-out poses significant risks to air and road transportation safety.
• Accurate forecasting models for ash dispersion require precise data on volcanic particle granulometry.
• Current methods for granulometry assessment rely on direct sampling or costly instrumentation.

Purpose of the Study:

• To introduce and validate a novel vision-based methodology for estimating volcanic ash granulometry.
• To develop an image processing paradigm for accurate and efficient ash particle size analysis.
• To provide a cost-effective alternative for obtaining critical granulometric data for hazard assessment.

Main Methods:

• Development of a dedicated image processing paradigm using LabVIEW™ software.
• Experimental validation using digital reference images simulating various operational conditions.
• Quantitative assessment of the image processing algorithm's accuracy in granulometry estimation.

Main Results:

• The developed vision-based methodology successfully estimated volcanic ash granulometry.
• Experimental validation demonstrated high accuracy, with the image processing algorithm achieving 1.76% error.
• The results indicate the potential of image analysis for rapid and reliable ash characterization.

Conclusions:

• The presented vision-based approach offers a promising, accurate, and potentially cost-effective solution for volcanic ash granulometry assessment.
• This methodology can significantly enhance the capabilities of volcanic ash dispersion forecasting models.
• Further research could explore real-time implementation and application to diverse volcanic eruption scenarios.