Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective

Abstract

Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this paper, first, we summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine learning algorithms.

References in corpus (9)

• LSTM: A Search Space Odyssey
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
• Unsupervised Learning of Visual Structure using Predictive Generative Networks
• PixelNet: Towards a General Pixel-level Architecture
• Content-adaptive non-parametric texture similarity measure

Cited by in corpus (3)

• Semi-supervised Sequence Modeling for Elastic Impedance Inversion
• Learning to Label Seismic Structures with Deconvolution Networks and Weak Labels
• Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation