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