Forward Stratigraphic Modelling to Generate Synthetic Seismic Training Dataset for Deep Learning: A Case Study to Predict Shelf-Edge Trajectories

Seismic data forms the backbone of what we understand in the subsurface, and seismic data interpretation is still usually done by hand. Automatic seismic interpretation with deep learning is very promising, but there the problem is a lack of labelled training data. In this study, we use forward stratigraphic modelling and show how forward modelling can be advantageously used in deep learning.

Specifically, we focus on shelf-edge trajectories as the geological representations of lateral and vertical shifts in sediments’ position through time. They provide continuous tracks of changes in relative sea-level as well as sediment stacking patterns and depositional geometries. Mapping these trajectories and measuring their changing angles help in quantifying the sequence stratigraphic analysis and predicting ancient depositional environments.

Here, we evaluate the ability of deep learning models, trained on synthetic seismic data, to identify clinoforms and their rollover points for shelf-edge trajectories mapping. The synthetic training dataset generated using geological processed-based forward modelling represents different depositional slope scenarios. Controlling the different parameters that govern shelf-edges and shelf-edge trajectories (such as bathymetry, sediment supply, eustatic sea-level changes and subsidence) gave us a better chance to mimic realistic and diverse depositional setting, which helps in generalizing the deep learning model. In addition, the ground truth (labels) for the created synthetic seismic data is automatically generated by the forward model, without the need of manual labelling seismic data.

Higher accuracy score on both validation and testing datasets demonstrates the power and effectiveness of using synthetic as training dataset. This study shows that synthetic data can play a major role in bridging the gap between traditional seismic interpretation and automating the process using machine learning. It also shows that forward modelling is a powerful technique to combine with data modelling, such as machine learning.