The application of a new machine learning paradigm based on pre-training and fine-tuning, StorSeismic, on field seismic data

We often apply a number of processing modules to a field dataset to perform various tasks, like denoising, first arrival picking, velocity estimation, and so on. Selecting the architecture, collecting labels, and tuning a deep learning network for a specific seismic processing task have been common practice in the field, yet these, often independent, networks do not take advantage of the fact that most processing tasks utilize the same features embedded in the seismic data that actually can be stored in a common pre-trained network in a self-supervised manner, we refer to as StorSeismic. In this framework, we utilize a Bidirectional Encoder Representations from Transformers (BERT) model to promote pre-training for storing the features of a seismic dataset and then efficiently fine-tune it to adapt to a wide spectrum of seismic processing tasks. Here, we combine field data, with synthetic generated data, in the self-supervised pre-training step to store the seismic features. Then, we use the labeled synthetic data to fine tune the pre-trained network in a supervised fashion to perform various seismic processing tasks, like denoising, low frequency extrapolation, first arrival picking, and velocity estimation, with satisfactory results.