Transformer assisted dual U-net for seismic fault detection

Abstract

Automatic seismic fault identification for seismic data is essential for oil and gas resource exploration. The traditional manual method cannot accommodate the needs of processing massive seismic data. With the development of artificial intelligence technology, deep learning techniques based on pattern recognition have become a popular research area for seismic fault identification. Despite the progress made with U-shaped neural networks (Unet), they still fall short in meeting the stringent requirements of fault prediction in complex structures. We propose a novel approach by combining a standard Unet with a transformer Unet to create a parallel dual Unet model, called Dual Unet with Transformer. To improve the accuracy of fault prediction, we compare six loss functions (including Binary Cross Entropy loss, Dice coefficient loss, Tversky loss, Local Tversky loss, Multi-scale Structural Similarity and Intersection over Union loss) using synthetic data, based on three evolution metrics involving Dice coefficient, Sensitivity and Specificity, find that the binary cross entropy loss function is the most robust one. An example comparing the prediction performance of different Unet models on synthetic data demonstrates the superior performance of our Dual Unet model, verifying the practical application value. To further validate the practical feasibility of our proposed method, we use real seismic data with a complex fault system and find that our proposed model is more accurate in predicting the fault system compared to well-developed Unet models such as the classical Unet and classical coherence cube algorithm, without transfer learning. This confirms the potential for wide-scale application of our proposed model.