3D Seismic

3D seismic analysis aims to create detailed subsurface images from seismic reflection data for geological interpretation and reservoir characterization. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, diffusion models, and generative adversarial networks (GANs) to address challenges such as data interpolation, noise reduction (e.g., footprint removal), and automated feature extraction (e.g., first arrival picking and facies classification). These advancements improve the accuracy and efficiency of seismic interpretation, enabling better prediction of subsurface structures like gas traps and facilitating more informed decision-making in hydrocarbon exploration and other geoscientific applications. The development of foundation models and semi-supervised learning techniques further enhances the scalability and applicability of these methods to diverse datasets and geological settings.