Abstract

This research paper aims to provide an overview and application of transfer learning in convolutional neural networks, exploring its underlying principles and applications. Transfer learning involves reusing pre-trained models, which have already been trained on large-scale datasets, and fine-tuning them to new tasks or domains with limited labeled data. In this paper, we have a pre-trained CNN U-Net model, and we have implemented the concept of transfer learning to test the scope of improvement in the performance of our fault detection model using seismic stack data. Various experiments and fine-tuning were carried out, and the best-performing model was used to predict the fault likelihood in 3D space to aid in further fault interpretation.