Deep learning empowers the next generation of seismic interpretation




Shi, Yunzhi

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With the ever developing data acquisition techniques, seismic processing deals with massive amount of high quality 3-D data with greater pressure to interpret the data more efficiently. Currently, seismic interpretation such as fault analysis and salt detection is a tedious, manual, and time-consuming process. Modern interpretive tools still rely on interpreter while only utilizing the data qualitatively as a backdrop or indirect guide. Therefore, the seismic analysis iterations could take multiple months with human expertise. The advancements in computer technology creates opportunities to develop automated tools for seismic interpretation that only a few years ago would have been prohibitively expensive. In this dissertation, I address the problem by investigating efficient seismic interpretation tools, designing related algorithms, and show the feasibility and effectiveness of applying them to various demanding interpretation problems on 2D/3D datasets. The tools are based on deep neural networks and employ convolutional layers to achieve artificial visual understanding of the datasets. First, I formulate salt detection as an image segmentation problem and develop a CNN to solve this problem with high efficiency and accuracy. CNNs with encoder-decoder architecture and skip-connections allows for extracting essential information from training data, thus results in high accuracy and great generalization across different type of datasets. Further extending from the segmentation end-to-end network framework, I introduce a recurrent style network for tracking irregular geobodies. The improvement is two-fold: the tracking algorithm allows for instance separation during segmentation, and the atomic design allows for more interaction on the user side to control the model application on various datasets. Apart from these supervised learning frameworks, I found that unsupervised learning provides even more powerful tools in other interpretation tasks. In the following chapter, I investigate the possibility to exploit the deep CNN architecture itself as a model parameterization method and perform image enhancing tasks. The deep network is optimized iteratively and can constrain the space of solutions to admissible models. Inspired by automatic recommendation system, in the next chapter, I propose a network that transforms seismic waveforms into a latent space in which they are aligned by similarities. Waveforms that belong to the same horizon, which are more similar to each other, can be extracted from the latent space more easily. In the last chapter, I propose a network architecture, plane-wave neural networks (PWNN), combining plane-wave destruction (PWD) filters and CNN into a single architecture. CNN can extract nonlinear features from spatial information, however, lacks the ability to understand spectral information. On the other hand, PWD filter, a local plane-wave model tailored specifically for representing seismic data, is effective to extract signals aligned along dominant seismic events. Finally, I discuss known limitations and suggest possible future research topics.


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