Boletín de Geología

Vol. 48 Núm. 1 (2026): Boletín de Geología
Artículos científicos

El principio de discrepancia como método de optimización para un esquema de inversión de Gauss-Newton

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Brayan Quiceno-Arenas
Instituto Tecnológico Metropolitano
Andrés Mauricio Muñoz-García
Instituto Tecnológico Metropolitano
Luis Duque-Gómez
Instituto Tecnológico Metropolitano
Juan Paniagua-Castrillón
Instituto Tecnológico Metropolitano
Juan Navarro-Restrepo
Instituto Tecnológico Metropolitano
Moisés Bustamante-Rúa
Universidad Nacional de Colombia
DOI: https://doi.org/10.18273/revbol.v48n1-2026006

Publicado 2026-04-23

Palabras clave

  • Tomografía de resistividad eléctrica,
  • Inversión,
  • Método Gauss-Newton,
  • Optimización

Cómo citar

Quiceno-Arenas, B., Muñoz-García, A. M., Duque-Gómez, L., Paniagua-Castrillón, J., Navarro-Restrepo, J., & Bustamante-Rúa, M. (2026). El principio de discrepancia como método de optimización para un esquema de inversión de Gauss-Newton. Boletín De Geología, 48(1), 95–110. https://doi.org/10.18273/revbol.v48n1-2026006

Derechos de autor 2026 Boletín de Geología

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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

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Resumen

La inversión geofísica es un proceso matemático utilizado para inferir características del subsuelo a partir de modelos que representan sus propiedades físicas, según datos medidos o parámetros físicos. Uno de los retos de la inversión geofísica es generar modelos que se ajusten a los datos obtenidos. Para resolverlo, se han desarrollado diversas metodologías, como el esquema de Gauss-Newton. Estas metodologías requieren la selección óptima del parámetro de regularización, pero no proporcionan una función o clase predefinida para dicha selección. En consecuencia, este estudio propone un novedoso algoritmo de optimización, basado en el principio de discrepancia, para estimar el parámetro de regularización utilizando un conjunto de datos sintéticos y reales de tomografía de resistividad eléctrica. De acuerdo con los resultados, se obtuvieron valores ideales de tolerancia para cada conjunto de datos, y se demostró la eficacia y las ventajas computacionales del algoritmo propuesto. Además, el algoritmo mostró una mejor definición de las estructuras de interés que otros algoritmos reportados en la literatura científica.

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