Tsunami bring great disasters to human beings. The focus of the research is to predict the feasibility of tsunami early warning by microwave radar scheme. In this article, the electromagnetic (EM) scattering characteristics of the sea surface from the calm sea to the submarine earthquake are studied. Based on the high-order Boussinesq wave model combined with the up and down movement of the two submarine plates, the 3-D geometric propagation model of the tsunami sea surface is established. The geometric structure and propagation characteristics of the sea surface under different earthquake magnitudes are analyzed. The higher the earthquake magnitude, the more obvious the effect on the sea wave height and sea surface slope. Second, the influence of wind velocity disturbance on sea surface roughness caused by tsunami is deduced. The EM scattering distribution of tsunami-affected sea surface is solved by combining the wind disturbance theory with the EM scattering method based on the surface element model. In addition, the distribution of EM scattering coefficients of tsunami sea surface under different radar parameters, background wind speed, and earthquake magnitude are also discussed, providing an important theoretical basis for the rapid warning of tsunami by microwave radar.

We propose a robust seismic noise attenuation method by an alternative denoising procedure using variational mode decomposition (VMD) combined with the Schrodinger equation thresholding, referred as VMD-Schrodinger interval-thresholding method. In the proposed method, VMD is used as a subband-like filter to get a series of uncorrelated intrinsic mode functions (IMFs) from low frequency to high frequency. For IMFs having strong correlations with the original signal, the Schrodinger equation thresholding-based denoising is used to obtain the denoised IMFs. For IMFs existing weak correlations with the original signal, the noise extracted from these IMFs is used to generate several different versions of noise by altering the sample positions. These different versions of noise combined with the information extracted from these IMFs are used to reconstruct different noisy versions of the original IMFs existing weak correlations. Then the Schrodinger equation thresholding-based denoising is applied to these different noisy data. The denoised IMFs existing weak correlations are the average of these denoised noisy versions signals. Finally, the denoised seismic data is the sum of these denoised IMFs. Compared with the wavelet thresholding method, iterative EMD interval-thresholding method and quantum mechanics-based signal denoising method, the proposed method can more effectively extract weak signals from the noise-dominant components of seismic data, while protecting both the low-frequency and high-frequency components of the seismic trace, especially exhibiting good performance for complex dipping events. The synthetic and field data applications illustrate the robustness and the superiority of the proposed method.

As a common seismic facies visualization analysis method, self-organizing map (SOM) projects the waveform or seismic attribute vectors into a two-dimensional topological plane in a nonlinear way, which can effectively and efficiently discover the topological structure of a dataset. SOM does not need to set the number of classes in prior and has friendly visualization characteristics and excellent generalization, which are conducive to seismic facies interpretation using unlabeled data. However, due to the competitive learning used in SOM and the imbalance of data distribution in real world, the samples from majority classes are expanded on the topological plane and the minority classes are compressed. As a result, the plane cannot accurately describe the global structure of data distribution. To improve the visualization precision by modifying the topological relationship of the prototype vectors of SOM, we utilize uniform manifold approximation and projection (UMAP), a novel manifold learning technique for dimension reduction, to correct the prototype vectors generated by SOM. By combining the advantages of SOM and UMAP in the representation of data topological structure, the global relationship between seismic data samples can be properly established, and the internal relative spatial structure of majority class samples can be retained as much as possible, resulting in a more reliable classification. Meanwhile, the framework maintains the advantages of SOM in visualization. In the modeling tests and real data experiments, we have demonstrated the effectiveness and rationality of UMAP-SOM on the spatial structure representation of three-dimensional seismic data.

In seismic interpretation, horizon picking is a very essential but time-consuming and challenging task. Most existing auto-picking algorithms have been proposed to improve the horizon interpretation efficiency. Recently, deep learning approaches have shown promising performance in horizon identification. However, feeding directly seismic time series or images into a deep learning network only uses the amplitude information of seismic signal, which limits the classification accuracy. In this letter, we propose to learn more distinctive characteristics in the time–frequency domain from the continuous wavelet transform (CWT) coefficients. More importantly, we develop a novel two-branch convolutional neural network (TB-CNN) for horizon picking: a CWT branch can mine the time–frequency features in 2-D CWT coefficients of seismic time series. At the same time, a spatial branch further explores the local spatial features in seismic images. The features of the two branches are then fused to perform classification. The output is the class scores of voxels being horizon or background. Finally, we extract the horizon surface by finding all voxels with the highest score values of the horizon class in the vertical temporal direction. We conduct experiments on both synthetic and field data. The results show that the proposed method can effectively fuse the spatial features and time–frequency features to yield higher performance than the traditional 3-D auto-tracking method.

We propose a stable seismic Q estimation approach by employing quantum mechanics-based signal representation. For Q estimation, we project a seismic trace onto a specific basis composed of wave functions constructed through the resolution of the Schroedinger equation of non-relativistic quantum mechanics at first. Then, based on the specific basis, we derive the quantum mechanics-based Q estimation approach in the local frequency-projection coefficient domain. The Planck constant and the control factor are the two key factors for the quantum mechanics-based Q estimation method. Compared with the traditional methods, the quantum mechanics-based Q estimation approach shows more stability and noise robustness. The synthetic and field data applications illustrate the effectiveness and the superiority of the proposed method. The quantum mechanics-based Q estimation method offers a new field and provides a complementary way for measuring seismic attenuation.

We propose a stable Q-estimation approach based on the compensation of amplitude spectra in the time-frequency domain after a synchrosqueezed wavelet transform (SSWT). SSWT employing a post-processing frequency reallocation method to the original representation of a continuous wavelet transform (CWT) for improving its readability provides the sharper time-frequency representation of a signal when compared with the other traditional time-frequency methods such as CWT or S-transform. For Q-estimation, we transform a seismic trace into the time-frequency domain using SSWT at first. Then, we derive the amplitude compensation in the SSWT domain. By searching the predetermined Q range, the comparison between the compensated amplitude spectrum and the reference one in the SSWT domain is carried out. The output optimized Q-factor estimation is evaluated by the minimum of the mean square error. For the robust and fast stabilization form of the amplitude compensation in the SSWT domain with noise amplification damping, the seismic pulse is truncated with a limited length and the obtained time-frequency maps using an SSWT are smoothed. The synthetic vertical seismic profiling data and the real stacked seismic data applications illustrate the effectiveness and the ability of the proposed method.

In orthogonal frequency‐division multiplexing (OFDM)‐based full‐duplex (FD) systems, the terminal is allowed to transmit and receive signals simultaneously in the same frequency band. This leads to new challenges to pilot for channel estimation. The conventional pilot design methods are no longer effective for such systems. In this study, the authors propose a new practical pilot design method for OFDM‐based FD systems and its performance is analysed in terms of the self‐interference cancellation capability. Unlike the existing OFDM‐based FD pilot design schemes, which require the channel to be constant during multiple continuous OFDM symbols, the authors' proposed scheme can be implemented in the block fading channel (i.e. channels that vary from one OFDM symbol to another). Besides, the optimal pilot pattern of the proposed scheme is studied. Their simulation results demonstrate the efficiency of the proposed pilot design.

A novel hydrocarbon detection technique named the variational mode decomposition (VMD)-based instantaneous centroid method is proposed in this letter. It reveals frequency-dependent amplitude anomalies that may reflect some details deeply buried within the intrinsic mode functions (IMFs) in particular frequency bands. Instantaneous amplitude and instantaneous frequency information from each IMF are used to generate each IMF instantaneous centroid. A weighted correlation scheme is employed to generate the VMD-based instantaneous centroid volume for a seismic trace. Model testing and field data from a carbonate reservoir in China illustrate that the VMD-based instantaneous centroid method can provide a better hydrocarbon-prone interpretation with a higher resolution and accuracy. Comparisons between the VMD-based instantaneous centroid method and the short-time Fourier transform, and continuous wavelet transform and prestack wave impedance inversion technology indicate that the proposed method is more convenient and can effectively target gas reservoirs. This letter presents a complementary approach to current methods used to extract frequency-dependent amplitude anomaly information.