Transforming 3D UHR Seismic Data Processing with Machine Learning Solutions

The field of geophysical exploration, particularly in seismic data acquisition and processing, is undergoing a profound transformation through the integration of machine learning (ML) technologies. While seismic data processing has traditionally relied on computationally intensive and manually tuned methods, machine learning offers the promise of accelerated workflows, improved resolution, and consistency across datasets. This work focuses on the application of ML techniques specifically to 3D ultra-high-resolution seismic (UHRS) data, highlighting the potential of artificial intelligence to both enhance quality and significantly reduce processing time. Although UHRS datasets are generally smaller in size than conventional seismic datasets, the increasing scale of modern surveys, such as those exceeding 500 square kilometers as cited by Caselitz et al. (2024), has made computational efficiency and turnaround time critical considerations. Even for smaller surveys, the typical bottleneck lies in the testing and parameterization phase of processing, which can be dramatically streamlined through ML approaches. Machine learning's core advantages in seismic data processing include eliminating complex parameterization steps, reducing turnaround time, and delivering consistent results across surveys. Currently, ML applications for UHRS data are primarily focused on denoising and deghosting. However, the potential extends further into velocity model building and even the restructuring of processing workflows into integrated, end-to-end models.