Tecnologías para el análisis granulométrico en Geociencias: de la fotogrametría y el LiDAR a los enfoques de aprendizaje profundo
DOI:
https://doi.org/10.65093/aci.v17.n2.2026.55Palabras clave:
granulometría, LiDAR, fotogrametría, aprendizaje profundoResumen
La granulometría es fundamental para comprender flujos granulares geológicos, transporte de sedimentos y procesos de deposición. Tradicionalmente, el tamizado ha sido el método estándar para determinar el tamaño de partícula; sin embargo, presenta limitaciones de precisión, tiempo y aplicabilidad en campo. En las últimas décadas han surgido técnicas basadas en análisis de imágenes, fotogrametría y tecnologías LiDAR, que permiten realizar granulometría mediante enfoques ópticos y modales más eficientes. Este artículo de perspectiva analiza tecnologías emergentes, destacando la integración de inteligencia artificial y aprendizaje profundo en el análisis granulométrico. Se discuten aplicaciones de Structure from Motion (SfM), redes neuronales convolucionales (CNN), arquitecturas transformador y Redes Neuronales de Grafos (GNN), así como metodologías LiDAR para generar nubes de puntos tridimensionales. Estas tecnologías permiten análisis no invasivos, rápidos y aplicables en entornos complejos, además de proporcionar información 3D detallada. El futuro apunta al desarrollo de sistemas híbridos con cuantificación de incertidumbre mediante enfoques bayesianos.
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