Aprendizaje automático para análisis y procesamiento de datos sísmicos


  • Danilo R. Velis Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, y CONICET
  • Julián L. Gómez Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, y CONICET, YPF Tecnología S.A.
  • Gabriel R. Gelpi Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata
  • Germán I. Brunini Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, y CONICET
  • Daniel O. Pérez Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, y CONICET
  • Juan I. Sabbione Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, y CONICET

Palabras clave:

exploración sísmica, velocdades, redes neuronales


El aprendizaje automático está marcando el ritmo del avance del análisis de datos en muchos campos de la ciencia, la tecnología y la industria. En este contexto, el procesamiento y la inversión de datos sísmicos se abordan mediante estrategias que extraen la información relevante de los datos de forma casi automática. El “dictionary learning” y las Redes Neuronales son dos ejemplos comunes de algoritmos capaces de capturar las estructuras y patrones complejos incrustados en los datos e inferir o predecir cierta información de interés a partir de ellos. Utilizamos la técnica de “residual dictionary denoising” para atenuar la huella de adquisición en los datos sísmicos 3D. Además, demostramos algunos avances en el uso de una red neuronal profunda para invertir el tensor de momento sísmico en escenarios de monitorización de pozos. El aprendizaje automático también incluye técnicas de optimización global, como el recocido simulado y la evolución diferencial. Exploramos cómo estos dos algoritmos pueden automatizar procesos en la exploración sísmica, como el análisis de la velocidad y el “well-tying” que convencionalmente se hacen a mano y, por lo tanto, son susceptibles de la subjetividad y la experiencia del usuario.


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Cómo citar

Velis, D. R., Gómez, J. L., Gelpi, G. R., Brunini, G. I., Pérez, D. O., & Sabbione, J. I. (2022). Aprendizaje automático para análisis y procesamiento de datos sísmicos. Geoacta, 43(2), 7–29. Recuperado a partir de https://revistas.unlp.edu.ar/geoacta/article/view/14284