Palabras clave
Modelamiento geológico implícito
Aprendizaje supervisado
Perceptrón multicapa
Función de distancia con signo
Cómo citar
Citas en Dimensions Service
Resumen
Este artículo presenta una novedosa metodología para modelamiento de unidades geológicas, inspirada en la concepción teórica del modelamiento geológico implícito, pero extendiendo su capacidad de incorporar covariables a través de un método de aprendizaje supervisado como el perceptrón multicapa. A partir de una función de distancia con signo aplicada a cada muestra de sondaje, se genera una variable auxiliar que, junto con las coordenadas espaciales, se asocia a la categoría geológica correspondiente. Estos datos se utilizan en un proceso de entrenamiento supervisado, en el que el perceptrón aprende la distribución espacial de las unidades geológicas. Una de las principales ventajas del modelo es la facilidad para incorporar covariables, permitiendo que aquellas de mayor calidad o densidad de muestreo contribuyan a una representación más robusta del subsuelo. Posteriormente, tanto la variable auxiliar como las covariables son interpoladas en las coordenadas objetivo. El perceptrón entrenado asigna una clase geológica a cada celda de la cuadrícula, y mediante un postproceso se ajustan los contactos entre unidades, mejorando la continuidad geológica. La validación de la propuesta metodológica se realiza utilizando una base de datos sintética que representa cuatro unidades litológicas estratificadas en una cuadrícula 2D. Cada unidad está asociada a coordenadas espaciales y a una variable secundaria correspondiente a la densidad de roca. A partir de esta cuadrícula, se extrae una muestra del 2.75 %, equivalente a 11 sondajes verticales dispuestos de forma irregular, que constituyen el punto de partida para la aplicación del método en un entorno controlado. Los resultados se comparan con los del modelamiento implícito convencional mediante métricas de precisión, recall, F1-score y coeficiente Kappa, evidenciando un desempeño superior del método propuesto, respetando los contactos geológicos y mejorando la distribución espacial de las unidades litológicas. Los hallazgos de este estudio proporcionan una base para escalar la metodología, explorando nuevos modelos de aprendizaje automático, optimizando su desempeño computacional y extendiéndola a aplicaciones en escenarios reales 3D.
Citas
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2026 Heber Hernández, Martín Díaz-Viera, Sebastián Donaire, Guillermo Sánchez-Vera, Jorge Morales-Leal