Seismic Facies Segmentation Using Ensemble of Convolutional Neural Networks

Abstract

The use of machine learning for seismic interpretation is a growing area of interest for researchers. Manual interpretation demands time and specialized effort. The use of machine learning model will expedite the process. The Convolutional Neural Networks (CNNs) are a class of deep learning algorithms used for images. In this paper, seismic facies segmentation using encoder-decoder architecture of CNNs is proposed. The proposed method filled the gap using a multimodel approach for seismic interpretation. The novelty of the model is that it is not limited to the current dataset and semantic segmentation models. The encoder-decoder architecture input and output size is the same, and it allows the labelling of each pixel of the image. Four models are trained on the open-sourced F3 block Netherlands dataset. Images of $128\times 128$ were extracted from the data. Data augmentation is used in two of the models to increase the data size for better model learning. Results of individual models and their ensemble are compared. Ensemble is performed by taking the average of the probabilities of the classes obtained from the trained models. Ensemble gave the superior results. Seven classes are segmented with a global pixel accuracy (GPA) of 98.52%, mean class accuracy (MCA) of 96.88%, and mean intersection over union (MIoU) of 93.92%.