A Deep Transfer Learning Framework for Seismic Data Analysis: A Case Study on Bright Spot Detection

Bright spots, strong indicators of the existence of hydrocarbon accumulations, have been primarily used by geophysicists in oil and gas exploration. Recently, machine-learning algorithms, adopted to automate bright spot detection, have mainly relied on feature extraction and shallow classification workflows to achieve an 85.4% F1 score at best, on 2-D seismic data. Deep neural networks have proved their effectiveness in image classification applications, outperforming humans in some instances, but have not been applied to bright spot detection yet. However, their data-hungry nature poses a challenge in domains suffering from expensive data acquisition, such as seismic data analysis problems; they generally require millions of training samples before achieving good performance. In this article, we implement SeisNet, a convolutional neural network with a “butterfly” architecture that overcame the limited data challenge by implementing data augmentation and inductive transfer-learning techniques. Moreover, we adopt a novel formulation that allows us to detect bright spots and estimate their volume. Our approach was tested against different pretraining and transfer-learning methods and was shown to outperform other approaches in the literature by achieving a 95.6% F1 score on bright spot detection. Our model accurately predicted the volume of the bright spot with an average absolute error that is not more than 0.04% of the total volume of the seismic image. This article is an important step in establishing pretrained networks for other seismic applications such as earthquake prediction; our domain-specific pretrained network, proven to outperform state-of-the-art pretrained networks on bright spot detection, may be used to jump-start the training of deep models on other seismic problems.