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Although seismic industry has been investigating decades on solving the first break picking problems automatically, there are still enormous challenges during the investigation. Even till today, there are not solid solutions to avoid human labors to manually pick data by geophysicists. With the raise of deep learning and powerful hardware, many of those challenges can be overcome. In this work, we propose a deep semi-supervised neural network to achieve automatic picking for the first break in seismic data. The network is designed to perform with both unlabeled data and a limited amount of real data with labels. Initial feature representation is learning in a discriminative unsupervised manner on real datasets without labels. Since no assumptions are made with regard to the difference of underlying distributions between the synthetic and real data, our model has more marginal gain to compensate for the distribution drifting compare to the supervised learning models. In addition, the network is capable of updating itself through continuous learning. The system is able to identify labeling anomalies onsite and update the model through active learning. In simulation, we show our proposed deep semi-supervised neural network can achieve high accuracy on first break picking. Comparing with the supervised neural networks, our proposed network shows the advantage on using both labeled and unlabeled data set to achieve higher accuracy. 

Deep learning leverages multi-layer neural networks architecture and demonstrates superb power in many machine learning applications. The deep denoising autoencoder technique extracts better coherent features from the seismic data. The technique allows us to automatically extract low-dimensional features from high dimensional feature space in a non-linear, data-driven, and unsupervised way. A properly trained denoising autoencoder takes a partially corrupted input and recovers the original undistorted input. In this paper, a novel autoencoder built upon the deep residual network is proposed to perform noise attenuation on the seismic data. We evaluate the proposed method with synthetic datasets and the result confirms the effective denoising performance of the proposed approach. 

In this paper, we will explore the possibility of synthesizing the low-frequency data from the high-frequency data. The synthesized low-frequency data are used to improve the full-waveform inversion (FWI). Unlike all previously methods, to the best of our knowledge, this is the first attempt to utilize a data driven approach to solve the problem. We propose to learn the low wavenumber information in FWI via the Deep Inception based Convolutional Networks. Once the deep learning network is sufficiently trained, the network can be used to predicted the low-frequency data with high accuracy on a completely different unknown velocity model. In the end, we validate the quality of the predicted low-frequency data and the robustness of this deep learning approach.