Academic Journal
Digital Holographic Microscopy and Machine Learning for Quantitative 3D Analysis and Automatic Classification of Volcanic Ash Particles.
| Title: | Digital Holographic Microscopy and Machine Learning for Quantitative 3D Analysis and Automatic Classification of Volcanic Ash Particles. |
|---|---|
| Authors: | Monaldi, A. C., Díaz, J. I., Martínez, M. F., Budini, N., Báez, W. A. |
| Source: | Journal of Geophysical Research. Atmospheres; 7/16/2025, Vol. 130 Issue 13, p1-16, 16p |
| Subject Terms: | Digital holographic microscopy, Machine learning, Transport theory, Scientific method, Morphology, Volcanic ash, tuff, etc., Volcanic eruptions, Dimensional analysis |
| Abstract: | Determining the shapes, sizes and optical properties of volcanic ash presents a significant challenge in volcanology, the aviation industry and atmospheric models involving transport and dispersion of particles. Eruptive dynamics, including fragmentation mechanisms, magma viscosity and particle transport processes, among others, are encoded in the intricate shapes and sizes of these particles. Traditionally, the analysis of ash particles' morphology has relied on quantitative non‐dimensional parameters, primarily derived from their 2D silhouette projected area, using conventional microscopy or particle analyzers. However, these fail to capture the 3D structure of their morphology. Additionally, atmospheric dispersion models often assume spherical particles with uniform refractive indices, introducing uncertainties in particle size estimations and dispersion calculations. In this study, we introduce a novel 3D characterization method for volcanic ash using digital holographic microscopy (DHM) combined with machine learning (ML). We implemented an off‐axis interferometer to register holograms of volcanic ash samples. We show that segmented phase maps from the reconstructed holograms can be used to derive both 2D and 3D phase‐based morphological parameters for individual ash particles or to estimate their refractive index. To illustrate the potential of this technique, we analyzed morphological differences between ashes acccording to their transport mechanism: fallout and flow. A ML algorithm based on support vector machine (SVM) was trained to classify particles into one of these two categories, achieving an average accuracy of 76%. These results show that the proposed approach serves as a valuable tool for monitoring volcanic eruptions providing insights on their characteristics and associated environmental impact. Plain Language Summary: Volcanic ash plays a critical role in understanding eruptions, their environmental effects, and the risks they constitute for aviation and public health. To study these tiny particles more effectively, we combined digital holographic microscopy with machine learning. Unlike traditional methods that rely on flat, 2D images, this approach captures the full shape, size, and optical properties of ash particles. By analyzing samples from different eruption scenarios and training computer programs to classify them, we distinguished particles transported by air from those carried by magmatic flows, achieving an accuracy of approximately 75%. This method not only reveals how volcanic ash travels and settles but also offers potential applications for improving hazard models and studying eruption processes. Because it is non‐destructive and highly accurate, this technique can complement other scientific tools, deepening our understanding of volcanic activity and its environmental impact while providing insights relevant to multiple disciplines. Key Points: Digital Holographic Microscopy with machine learning enables 3D analysis of volcanic ash providing morphological and optical propertiesThe proposed method allows classification of particles by transport mechanism (fallout vs. flow), offering insights into eruption dynamicsThis non‐destructive method aids in studying ash transport, deposition, and eruptions, expanding and enhancing geological analyses [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 186527182 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33422851563 |
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| Items | – Name: Title Label: Title Group: Ti Data: Digital Holographic Microscopy and Machine Learning for Quantitative 3D Analysis and Automatic Classification of Volcanic Ash Particles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Monaldi%2C+A%2E+C%2E%22">Monaldi, A. C.</searchLink><br /><searchLink fieldCode="AR" term="%22Díaz%2C+J%2E+I%2E%22">Díaz, J. I.</searchLink><br /><searchLink fieldCode="AR" term="%22Martínez%2C+M%2E+F%2E%22">Martínez, M. F.</searchLink><br /><searchLink fieldCode="AR" term="%22Budini%2C+N%2E%22">Budini, N.</searchLink><br /><searchLink fieldCode="AR" term="%22Báez%2C+W%2E+A%2E%22">Báez, W. A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Geophysical Research. Atmospheres; 7/16/2025, Vol. 130 Issue 13, p1-16, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+holographic+microscopy%22">Digital holographic microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Transport+theory%22">Transport theory</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+method%22">Scientific method</searchLink><br /><searchLink fieldCode="DE" term="%22Morphology%22">Morphology</searchLink><br /><searchLink fieldCode="DE" term="%22Volcanic+ash%2C+tuff%2C+etc%2E%22">Volcanic ash, tuff, etc.</searchLink><br /><searchLink fieldCode="DE" term="%22Volcanic+eruptions%22">Volcanic eruptions</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+analysis%22">Dimensional analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Determining the shapes, sizes and optical properties of volcanic ash presents a significant challenge in volcanology, the aviation industry and atmospheric models involving transport and dispersion of particles. Eruptive dynamics, including fragmentation mechanisms, magma viscosity and particle transport processes, among others, are encoded in the intricate shapes and sizes of these particles. Traditionally, the analysis of ash particles' morphology has relied on quantitative non‐dimensional parameters, primarily derived from their 2D silhouette projected area, using conventional microscopy or particle analyzers. However, these fail to capture the 3D structure of their morphology. Additionally, atmospheric dispersion models often assume spherical particles with uniform refractive indices, introducing uncertainties in particle size estimations and dispersion calculations. In this study, we introduce a novel 3D characterization method for volcanic ash using digital holographic microscopy (DHM) combined with machine learning (ML). We implemented an off‐axis interferometer to register holograms of volcanic ash samples. We show that segmented phase maps from the reconstructed holograms can be used to derive both 2D and 3D phase‐based morphological parameters for individual ash particles or to estimate their refractive index. To illustrate the potential of this technique, we analyzed morphological differences between ashes acccording to their transport mechanism: fallout and flow. A ML algorithm based on support vector machine (SVM) was trained to classify particles into one of these two categories, achieving an average accuracy of 76%. These results show that the proposed approach serves as a valuable tool for monitoring volcanic eruptions providing insights on their characteristics and associated environmental impact. Plain Language Summary: Volcanic ash plays a critical role in understanding eruptions, their environmental effects, and the risks they constitute for aviation and public health. To study these tiny particles more effectively, we combined digital holographic microscopy with machine learning. Unlike traditional methods that rely on flat, 2D images, this approach captures the full shape, size, and optical properties of ash particles. By analyzing samples from different eruption scenarios and training computer programs to classify them, we distinguished particles transported by air from those carried by magmatic flows, achieving an accuracy of approximately 75%. This method not only reveals how volcanic ash travels and settles but also offers potential applications for improving hazard models and studying eruption processes. Because it is non‐destructive and highly accurate, this technique can complement other scientific tools, deepening our understanding of volcanic activity and its environmental impact while providing insights relevant to multiple disciplines. Key Points: Digital Holographic Microscopy with machine learning enables 3D analysis of volcanic ash providing morphological and optical propertiesThe proposed method allows classification of particles by transport mechanism (fallout vs. flow), offering insights into eruption dynamicsThis non‐destructive method aids in studying ash transport, deposition, and eruptions, expanding and enhancing geological analyses [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2024JD043283 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Digital holographic microscopy Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Transport theory Type: general – SubjectFull: Scientific method Type: general – SubjectFull: Morphology Type: general – SubjectFull: Volcanic ash, tuff, etc. Type: general – SubjectFull: Volcanic eruptions Type: general – SubjectFull: Dimensional analysis Type: general Titles: – TitleFull: Digital Holographic Microscopy and Machine Learning for Quantitative 3D Analysis and Automatic Classification of Volcanic Ash Particles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Monaldi, A. C. – PersonEntity: Name: NameFull: Díaz, J. I. – PersonEntity: Name: NameFull: Martínez, M. F. – PersonEntity: Name: NameFull: Budini, N. – PersonEntity: Name: NameFull: Báez, W. A. IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 07 Text: 7/16/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 2169897X Numbering: – Type: volume Value: 130 – Type: issue Value: 13 Titles: – TitleFull: Journal of Geophysical Research. Atmospheres Type: main |
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