Velocity model inversion is one of the most important tasks in seismic exploration. Full-waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion methods, but it heavily depends on initial models and is computationally expensive. In recent years, a large number of deep-learning (DL)-based velocity model inversion methods have been proposed. One critical component in those DL-based methods is a large training set containing different velocity models. We have developed a method to construct a realistic structural model for the DL network. Our compressional-wave velocity model building method for creating dense-layer/fault/salt body models can automatically construct a large number of models without much human effort, which is very meaningful for DL networks. Moreover, to improve the inversion result on these realistic structural models, instead of only using the common-shot gather, we also extract features from the common-receiver gather as well. Through a large number of realistic structural models, reasonable data acquisition methods, and appropriate network setups, a more generalized result can be obtained through our proposed inversion framework, which has been demonstrated to be effective on the independent testing data set. The results of dense-layer models, fault models, and salt body models that we compared and analyzed demonstrate the reliability of our method and also provide practical guidelines for choosing optimal inversion strategies in realistic situations.

Seismic full waveform inversion is a common technique that is used in the investigation of subsurface geology. Its classic implementation involves forward modeling of seismic wavefield based on a certain type of wave equation, which reflects the physics nature of subsurface seismic wavefield propagation. However, obtaining a good inversion result using traditional seismic waveform inversion methods usually comes with a high computational cost. Recently, with the emerging popularity of deep learning techniques in various computer vision tasks, deep neural network (DNN) has demonstrated an impressive ability in dealing with complex nonlinear problems, including seismic velocity inversion. Now, extensive efforts have been made in developing a DNN architecture to tackle the problem of seismic velocity inversion, and promising results have been achieved. However, due to the dependence of a labeled dataset, i.e., the barely accessible true velocity model corresponding to real seismic data, the current supervised deep learning inversion framework may suffer from limitations on generalization. One possible solution to mitigate this issue is to impose the governing physics into this kind of purely data-driven method. Thus, following the procedures of traditional seismic full waveform inversion, we propose a seismic waveform inversion network, namely SWINet, based on wave-equation-based forward modeling network cells. By treating the single-shot observation data and its corresponding shot position as training data pairs, the inverted velocity model can be obtained as the trainable network parameters. Moreover, since the proposed seismic waveform inversion method is performed in a neural-network way, its implementation and inversion effect could benefit from some built-in tools in Pytorch, such as automatic differentiation, Adam optimizer and minibatch strategy, etc. Numerical examples indicate that the SWINet method may possess great potential in resulting a good velocity inversion effect with relatively fast convergence and lower computation cost.

The tunnel seismic ahead-prospecting method is used to estimate adverse geology ahead of the tunnel face. Accurately realizing this geology helps to guarantee safety during construction using a tunnel boring machine (TBM). The paper proposes a three-dimensional (3D) seismic ahead-prospecting method by optimizing a filtering method and an imaging algorithm. In this paper, a united filtering method based on $f–k$ and $\tau –p$ filtering is introduced. First, the waves from the interfaces behind the tunnel face are removed and P- and S-waves are separated. Then the interference waves are further eliminated by $\tau –p$ filtering. Mirrored artifacts resulting from the equitravel time plane algorithm are suppressed by the directional weighting integrated into the algorithm. Imaging results indicate that the new method suppresses mirrored artifacts better and produces accurate results. Moreover, the improved algorithm produces reflections that are more consistent with the interfaces than those produced by the original algorithm without directional weighting. Field studies using the 3D seismic ahead-prospecting method have accurately identified and located the main fractured zones, proving the reliability and superiority of the method.

Random noise attenuation is an essential step to improve the signal-to-noise ratio (SNR) of seismic data. Deep learning for seismic data denoising is dominated by supervised methods that require noise-free data as training targets. It is usually time-consuming and laborious to obtain such clean seismic data, and the effectiveness of the noise attenuation is difficult to be guaranteed. Therefore, we propose a novel unsupervised learning method that learns from noisy data. The method is based on two salient features of seismic data: 1) valid signals of adjacent seismic traces that are spatially correlated and 2) random noise that is spatially independent and unpredictable. An end-to-end deep convolutional neural network (CNN) was constructed to solve the denoising task. Adjacent traces of seismic data, which contain similar seismic phases and interface features, were used as the inputs and labels of the training set. The mapping of spatial correlation can be learned by the CNN so that valid signals from raw seismic data are predicted, while random noise is suppressed for unpredictability. Synthetic and field data were applied to the proposed CNN denoising model. The experimental results demonstrate the effectiveness of random noise attenuation while preserving amplitude compared with two commonly used state-of-the-art denoising methods.