Geoplat AI 22.0 seismic Fault interpretation Geoplat AI 2023
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Geoplat AI 22.0 seismic Fault interpretation Geoplat AI 2023
Geoplat AI enables the conditioning of seismic data using machine learning techniques. The training algorithm of the convolutional neural network closely resembles the earlier-described approach for structural noise reduction. In this neural network, a substantial amount of unique synthetic data was also used for training. An important distinction lies in the intermediate step – the construction of the specified seismic horizon interpretation volume under the hood – LGT volume – and hence helps to improve fault visibility.
By incorporating this volume into the neural network training, we’ve managed to preserve and accentuate structural features during calculations. This normalization of amplitudes and enhancement of fault zones are achieved. This conditioning algorithm significantly reduces the time required for identifying fault trajectories and facilitates both manual and automatic correlation of seismic horizons.
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.
