Salt Segment Identification in Seismic Images Using UNet with ResNet

Salt segmentation is the process of identifying whether a subsurface target is a salt or not. There are several places on Earth where there are significant amounts of salt as well as oil and gas. For businesses engaged in oil and gas development, finding the exact locations of significant salt deposits is crucial. Also, lands that have been impacted by salt are not useful for farming. The absorption capacity of the plant reduces due to the presence of salt in the soil solution, which results in a reduction in growth rate. So, to identify the land that contains salt, salt segmentation is being done. The seismic image of a particular pixel is analyzed to classify it either as salt or sediment. TGS Salt Identification Challenge dataset is used which consists of 4,000 seismic image patches of size (101 × 101 × 3) and corresponding segmentation masks of size (101 × 101 × 1) in the training set. 18,000 seismic image patches are present in the test set which are used for the evaluation of the model. The model used is a combination of UNet with ResNet-18 and ResNet-34. Using this architecture, the salt region can be determined from the seismic data and display the value of the entire salt region. IoU is used as a performance metric to evaluate the model. The outcomes demonstrate that the ensemble model outperforms individual network models and achieves better segmentation results.