What is it about?
I present a rock-physics-informed convolutional neural network methodology for estimating elastic properties of the reservoirs from 3D onshore seismic data. I compare the CNN results with the traditional prestack seismic inversion method and show enhanced robustness in reservoir prediction.
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Why is it important?
The integration of theoretical background into deep learning applications has been proven useful in various fields of geophysics in recent years. This study investigates and contributes to the rock physics aspect of such developments.
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This page is a summary of: Rock Physics-Guided Convolutional Neural Network for Elastic Impedance Inversion in Onshore Clastic Reservoirs, Interpretation, March 2025, Society of Exploration Geophysicists,
DOI: 10.1190/int-2024-0026.1.
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