The Challenge

During the construction of structural and stratigraphic models under complex geological conditions, there’s often a need for a more detailed interpretation of the section and the delineation of boundaries of thinner bodies. However, the resolution of seismic data frequently falls short of facilitating this.

Presently, there are a limited number of analytical methods for addressing this challenge. However, they all come with constraints and do not consistently provide a significant enhancement in resolution. Moreover, these methods tend to require substantial computation time.

Our Solution

Geoplat AI enables the conditioning of original seismic data using machine learning methods. The algorithm, like other our conditioning techniques, was trained on synthetic data. However, a distinctive feature here is the utilization of data with varying frequencies.

This trained neural network is capable of enhancing the resolution of seismic data, enabling a more detailed observation of thin layers. These thin layers become more pronounced through this conditioning, resulting in a more detailed representation of the structural characteristics of the surveyed area.