Mapping The Layers of The Ocean Floor With a Convolutional Neural Network
This addresses a computationally expensive challenge in oil exploration, but it is incremental as it applies existing neural network methods to a specific domain.
The study tackled the problem of mapping ocean floor layers for the oil industry by using convolutional neural networks, specifically UNet, to predict velocity models from seismic shots, achieving Sørensen-Dice coefficient values above 70%.
The mapping of ocean floor layers is a current challenge for the oil industry. Existing solution methods involve mapping through seismic methods and wave inversion, which are complex and computationally expensive. The introduction of artificial neural networks, specifically UNet, to predict velocity models based on seismic shots reflected from the ocean floor shows promise for optimising this process. In this study, two neural network architectures are validated for velocity model inversion and compared in terms of stability metrics such as loss function and similarity coefficient, as well as the differences between predicted and actual models. Indeed, neural networks prove promising as a solution to this challenge, achieving Sørensen-Dice coefficient values above 70%.