Applied Geophysics最新文献

The verification of nuclear test ban necessitates the classification and identification of infrasound events. The accurate and effective classification of seismic and chemical explosion infrasounds can promote the classification and identification of infrasound events. However, overfitting of the signals of seismic and chemical explosion infrasounds easily occurs during training due to the limited amount of data. Thus, to solve this problem, this paper proposes a classification method based on the mixed virtual infrasound data augmentation (MVIDA) algorithm and multiscale squeeze-and-excitation ResNet (MS-SE-ResNet). In this study, the effectiveness of the proposed method is verified through simulation and comparison experiments. The simulation results reveal that the MS-SE-ResNet network can effectively determine the separability of chemical explosion and seismic infrasounds in the frequency domain, and the average classification accuracy on the dataset enhanced by the MVIDA algorithm reaches 81.12%. This value is higher than those of the other four types of comparative classification methods. This work also demonstrates the effectiveness and stability of the augmentation algorithm and classification network in the classification of few-shot infrasound events.

The propagation of seismic waves in viscous media, such as the loess plateau and shallow gas regions, alters their amplitude, frequency, and phase due to absorption attenuation, resulting in reductions in the resolution and fidelity of seismic profiles and the inaccurate identification of subtle structure and lithology. Q modeling and Q migration techniques proposed in this paper are used to compensate for the energy and frequency attenuation of seismic waves, obtain high-quality depth imaging results, and further enhance structural imaging to address the aforementioned problem. First, various prior information is utilized to construct an initial Q model. Q tomography techniques are employed to further optimize the precision of the initial Q model and build a high-precision Q model. Subsequently, Q prestack depth migration technology is employed to compensate for absorption and attenuation in the three-dimensional space along the seismic wave propagation path and correct the travel times, realizing the purposes of amplitude compensation, frequency recovery, and phase correction, which can help improve the wave group characteristics while enhancing the resolution. Model data and practical application results demonstrate that high-precision Q modeling and Q migration techniques can substantially improve the imaging quality of underground structures and formations in the loess plateau region with extremely complex surface and near-surface conditions. The resolution and fidelity of seismic data, as well as the capability to identify reservoirs, can be improved using these techniques.

Many traditional denoising methods, such as Gaussian filtering, tend to blur and lose details or edge information while reducing noise. The stationary wavelet packet transform is a multi-scale and multi-band analysis tool. Compared with the stationary wavelet transform, it can suppress high-frequency noise while preserving more edge details. Deep learning has significantly progressed in denoising applications. DnCNN, a residual network; FFDNet, an efficient, flexible network; U-NET, a codec network; and GAN, a generative adversative network, have better denoising effects than BM3D, the most popular conventional denoising method. Therefore, SWP_hFFDNet, a random noise attenuation network based on the stationary wavelet packet transform (SWPT) and modified FFDNet, is proposed. This network combines the advantages of SWPT, Huber norm, and FFDNet. In addition, it has three characteristics: First, SWPT is an effective feature-extraction tool that can obtain low- and high-frequency features of different scales and frequency bands. Second, because the noise level map is the input of the network, the noise removal performance of different noise levels can be improved. Third, the Huber norm can reduce the sensitivity of the network to abnormal data and enhance its robustness. The network is trained using the Adam algorithm and the BSD500 dataset, which is augmented, noised, and decomposed by SWPT. Experimental and actual data processing results show that the denoising effect of the proposed method is almost the same as those of BM3D, DnCNN, and FFDNet networks for low noise. However, for high noise, the proposed method is superior to the aforementioned networks.

The deep learning algorithm, which has been increasingly applied in the field of petroleum geophysical prospecting, has achieved good results in improving efficiency and accuracy based on test applications. To play a greater role in actual production, these algorithm modules must be integrated into software systems and used more often in actual production projects. Deep learning frameworks, such as TensorFlow and PyTorch, basically take Python as the core architecture, while the application program mainly uses Java, C#, and other programming languages. During integration, the seismic data read by the Java and C# data interfaces must be transferred to the Python main program module. The data exchange methods between Java, C#, and Python include shared memory, shared directory, and so on. However, these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks. Considering the large volume of seismic data and the need for network support for deep learning, this paper proposes a method of transmitting seismic data based on Socket. By maximizing Socket’s cross-network and efficient longdistance transmission, this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system. Furthermore, the actual production application shows that this method effectively solves the shortage of data transmission in shared memory, shared directory, and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.

Qingshankou shale (Gulong area, China) exhibits strong acoustic anisotropy characteristics, posing significant challenges to its exploration and development. In this study, the five full elastic constants and multipole response law of the Qingshankou shale were studied using experimental measurements. Analyses show that the anisotropy parameters ϵ and γ in the study region are greater than 0.4, whereas the anisotropy parameter δ is smaller, generally 0.1. Numerical simulations show that the longitudinal and transverse wave velocities of these strong anisotropic rocks vary significantly with inclination angle, and significant differences in group velocity and phase velocity are also present. Acoustic logging measures the group velocity in dipped boreholes; this differs from the phase velocity to some extent. As the dip angle increases, the longitudinal and SH wave velocities increase accordingly, while the qSV-wave velocity initially increases and then decreases, reaching its maximum value at a dip of approximately 40°. These results provide an effective guide for the correction and modeling of acoustic logging time differences in the region.