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.

Application

— Enhancement of resolution of the seismic data

— Removing irregular noise

— Improving the signal to noise ratio

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.

Application

— Highlighting structural features and fault zones

— Removing irregular noise

— Improving the signal to noise ratio

Geoplat AI calculates image segmentation based on previously trained neural network datasets.

Our new generation convolutional neural network is trained on data samples covering multiple fault segments. During the training process, the neural network generates a fault database that includes presence signs and identifies presence patterns in geological structures.

The initial seismic volume is automatically divided into sets of segments by Geoplat AI. The probability of fault presence at each segment point is automatically calculated based on a set of features generated during the training process.

There is also an option to customise the neural network by training it through manual fault labeling. User's input is being added to the training set to retrain the network which changes the resulting model.

Finally, the algorithm automatically acquires surface groups using the calculated probability field, enabling a user to further customise the fault extraction parameters.

Application

— Obtaining a detailed fault model framework.

— Generating a fault probability field from the data that highlights fault distribution for all disconnected types within the entire seismic dataset.

— Automatically extracting fault surfaces.

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