Learnable Gabor Filters in Attention Unet for Prestack Seismic Inversion

Abstract

Amplitude variation with angle (AVA) prestack seismic inversion plays a critical role in oil and gas exploration and mineral resource assessment. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have been widely adopted for seismic inversion. However, many of these methods, especially supervised learning, struggle with poor generalization and noise resistance. Seismic data contains rich texture information that can be used as prior to constrain the convolutional kernels of the network. Gabor functions have long been used for seismic data representation, and learnable Gabor filters improve upon this by dynamically extracting latent seismic data information via adaptively updating Gabor filter parameters. In this letter, we propose a multitask AVA inversion method using learnable Gabor filters within a 2-D multitask attention U-Net. We equip the network’s first layer with learnable Gabor filters for latent seismic data feature extraction to enhance both generalization and noise resistance. An adaptive weight update method (AWUM) is employed to balance multitask learning efficiency and generalization performance. By creating a training dataset that combines synthetic and field seismic data with corresponding labels, we integrate field samples into the network training. Experiments for both synthetic and field datasets demonstrate that the proposed method exhibits superior generalization and stability compared to several existing approaches.