Semantic Segmentation and Augmentation of salt in seismic images using Deep Learning

Abstract

Salt segmentation refers to the process of separating salt from an image or a video stream.This process is often used in computer vision which is a python library application to extract specific features or objects from an image or video and can be used for various purposes, such as object recognition, tracking, and classification. The salt segmentation process typically involves segmenting the image into regions based on colour, texture, or other visual features, and then applying a threshold to separate the salt regions from the rest of the image. This can be done with various deep learning techniques like Unet, ResNet-18 and ResNet-34, VGG16 and InceptionV3, and DeeplabV3Plus. Through the ensemble approach of these models, we can achieve an accurate prediction of the model.For locating the salt zone in the seismic pictures, two models are used. The main model is an amalgam of ResNet-18 and ResNet-34 with UNET. By combining Ensemble UNET with ResNet-34, VGG16, Inceptionv3, and DeeplabV3Plus, the secondary model produces segmentation results. Besides, Data augmentation is performed to generate new training data from existing data by applying a set of transactions or modifications to the original data. This technique is commonly used to increase the size and diversity of the training data which can improve the accuracy and robustness of the trained models.