Kirchhoff beam migration is a kind of ray-based seismic imaging method boasting the advantages of both high efficiency and high accuracy. In this paper, anisotropic ray tracing will be applied to extend it to an anisotropic seismic imaging method. However, two problems exist in this method for imaging calculation: Firstly, the conventional local plane wave decomposition method has a low computational precision; secondly, the original beam propagator diverges quickly when the propagation distance increases. These two problems can both increase imaging noise, and finally reduce the resolution of the final imaging results. In this paper, the high-resolution anisotropic Kirchhoff dynamic focused beam migration is proposed to solve these two problems. Local plane wave decomposition method considered from the perspective of inversion is employed to obtain higher-quality slant stack data and dynamic focused beam propagator is adopted to control the divergence of beams. Both the anisotropic layer model and Hess model are adopted to test the imaging ability of the new method.

The essential task of reservoir characterization is to predict elastic/petrophysical parameters or facies from observed seismic data and evaluate their uncertainty. Deep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning methods that use seismic data as the only input lead to difficult training and unstable inversion results (i.e., transverse discontinuity or geologic unreliability). In such circumstances, it is beneficial if prior knowledge of the model domain can be incorporated into the deep learning framework. Therefore, we have developed prior-based loss functions in the learning step of the deep learning models to ensure that the predictions find low errors on the training sets and that their results are consistent with the known prior. In addition, the Monte Carlo dropout (MC-dropout) technique is introduced for the quantitative assessment of the uncertainty of the prediction results. We determine the effectiveness of our framework in the application of prestack seismic inversion, in which the initial model built from well-log interpolation is used to design the prior-based loss function. We first perform extensive experiments on the synthetic data and find that our method can yield more stable and reliable results compared with traditional methods. Combined with the transfer learning strategy, the application to real data further demonstrates that our deep learning framework obtains more reasonable inversion results with more horizontal continuity and greater geologic reliability than traditional approaches.

Seismic inversion and petrophysical inversion are the most common methods used in exploration geophysics to obtain elastic and petrophysical parameters, which are essential for reservoir characterization. However, they are commonly ill-posed problems and both are usually performed independently. Recently, deep learning has been successfully applied to the solution of inverse problems (e.g., seismic inversion and petrophysical inversion) by using large amounts of labeled training data to establish a mapping relationship between the input and the target. On the one hand, the performance of deep learning-based inversion depends heavily on diversity of the training dataset. However, the number of wells in actual production is limited, which greatly limits the application of deep learning-based inversion methods. On the other hand, deep learning-based inversion methods usually calculate elastic and petrophysical parameters independently, which lacks clear physical meaning and leads to large computational errors. To overcome these problems, by considering the spatial variability of elastic and petrophysical parameters from well-log data, a large amount of realistic pseudo-well-log and post-stack seismic data are first generated based on geostatistics to obtain the diversity of data required for network training. Meanwhile, we propose a fusion neural network architecture to build a physically meaningful network to simultaneously estimate acoustic impedance and porosity within the reservoir conditioned to post-stack seismic and limited well data. The fusion neural network consists of two subnetworks based on the spatio-temporal neural network. The method is validated by the application of real data and compared to traditional inversion approaches.

Prediction of elastic parameters (e.g., P-, S-wave velocity, and density) from observed seismic data is one of the most common means of reservoir characterization. Recently, deep learning (DL), as a data-driven approach, has been attracting increasing interest in seismic inversion. DL is proven to have the potential to learn complex systems and solve inverse problems efficiently. One of the most key components of DL is the training dataset, and an effective training dataset is a prerequisite for the success of DL-based methods. In seismic inversion, the training dataset needs to be artificially expanded due to the limited number of actual training data pairs. Traditional approaches of using the exact Zoeppritz equation (EZE) or its approximations for training dataset construction have limitations, principally, the single interface assumption and the neglect of wave propagation effects. Alternatively, the analytical solution of the 1D wave equation (i.e., reflectivity method-RM) can simulate the full-wave, including transmission losses and internal multiples, and can be executed in a target-oriented manner. Inspiration from this, we develop a data-driven elastic parameter prediction method based on waveform formulation. The method uses RM to construct training dataset, which both compensates for the inadequate training dataset in data-driven seismic inversion and improves the accuracy of the inversion results. We implement the method in a synthetic model as well as field data. The results are compared with model-driven methods (EZE and RM) and data-driven method based on EZE, and it is shown that the proposed method outperforms these three methods.

A new Gaussian beam (GB) Born modeling method is presented in this letter to simulate the wavefields of single scattering events in visco-acoustic media. This approach allows the accurate computation of synthetic seismograms or waveforms for the visco-acoustic media. Our method relies on the GB summation method, in which the attenuation effects are taken into account by incorporating a frequency-dependent dissipation function into beam calculation. The key to efficiency is that the Born modeling kernel is evaluated in the time domain based on the filter-bank technique. The proposed method is tested with both 2-D and 3-D numerical examples and the results demonstrate its capability to simulate wavefields in complex structures, as well as its marked advantage in efficiency over finite-difference wave equation modeling.

Geophysical exploration is important for road construction and maintenance. Before road construction, geophysical exploration is required to detect the geological structure to ensure the construction of the road; after the road is completed, geophysical exploration is still required to detect diseases in time. As a traditional geophysical exploration method, seismic exploration is important in road detection for its large detection depth and high resolution. The seismic attribute information obtained from seismic data can reflect many hidden information. As an important seismic attribute extraction method, time-frequency analysis method can simultaneously describe the energy density and intensity of seismic signals at different times and frequencies, which is of great significance to geological interpretation. However, traditional time-frequency analysis methods are low resolution and insufficient focusing. In this paper, on the basis of linear time-frequency analysis, the L1 norm constraint will be introduced, and the time-frequency analysis method will be implemented from the perspective of inversion, so as to reduce the influence of the multi-solution of the method and improve the method’s resolution and focusing. In this paper, two numerical simulation data and one real seismic data of road detection are employed to test the proposed new method.