Estimating particle size in silty lake sediments using a fine-tuned deep learning model on hyperspectral imagery

Milena Souza Kury; Hamid Ghanbari; Pierre Francus; Dermot Antoniades; Saeid Homayouni

doi:10.1016/j.sedgeo.2026.107123

May 1, 2026Sedimentary GeologyOpen Access

Estimating particle size in silty lake sediments using a fine-tuned deep learning model on hyperspectral imagery

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Key Points

This research aims to estimate particle size in silty lake sediments using hyperspectral imagery and deep learning techniques.
Utilized hyperspectral imaging to capture grain size variations in silty sediments.
Implemented a deep learning model trained on varied percentages of original data, assessing performance at 24% and 48%.
Analyzed the relationship between lithofacies properties and model performance.
Model performance was significant with only 24% of the original training data.
Inclusion of more than 48% of training data did not result in substantial improvement.
Lithofacies properties did not show an apparent relationship with model performance.

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Implication

Study estimates particle size in sediments through advanced imaging techniques, suggesting efficient model training.

Abstract

• Hyperspectral imaging effectively captured grain-size variations in the silt range. • Model performance remained significant using only 24% of original training data. • Performance did not substantially improved by including more than 48% of training data. • Lithofacies properties showed no apparent relationship with model performance.

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Cite This Study

Kury et al. (2026) studied this question.

synapsesocial.com/papers/6a1307f3f7bd4f5c7da73d82 https://doi.org/10.1016/j.sedgeo.2026.107123

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Estimating particle size in silty lake sediments using a fine-tuned deep learning model on hyperspectral imagery

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Estimating particle size in silty lake sediments using a fine-tuned deep learning model on hyperspectral imagery

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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