Complex topography is a challenging issue in onshore seismic exploration. The rugged terrain and lateral change of near-surface velocity pose a significant obstacle to the accurate imaging of seismic data. The adaptive focused beam migration method retains the good applicability of the ray methods for calculating the seismic wavefield under complex surface conditions. It can effectively solve the contradiction between the imaging accuracy of deep and shallow strata in traditional Gaussian beam migration. We extend the adaptive focused beam migration approach to the deep domain imaging of seismic data under complex surface conditions. First, the basic principles of the adaptive focused beam are reviewed. Then, Green’s functions of the seismic source and the receiving point of rugged topography are characterized by the adaptive focused beam, and an adaptive focused beam prestack depth migration method based on cross correlation imaging is proposed. The full-wave-arrival imaging strategy is applied to image all wave arrivals of subsurface imaging points. A single input seismic trace is adopted for imaging, which can directly emit the focused beam from the receiving point of rugged topography for wave field continuation, thus avoiding multiple focusing. As a result, the applicability of the migration approach to complex surface conditions was improved, and the imaging accuracy of the migration method was also effectively enhanced. The numerical model migration test of different rugged topography conditions and tectonic forms verified that the proposed method was an effective prestack depth migration applicable to accurately imaging seismic data under the rugged topography condition.

This paper proposes a method of gravity inversion based on an adaptive mesh-free approach by using a modified radial basis function (RBF). We parametrize the density distribution by using a mesh-free approach. Scattered points are introduced in most mesh-free methods to discretize the given equations. The subsurface space is generally discretized into regular grid cells, while mesh-free methods can avoid the expensive mesh generation and manipulation required in traditional approaches. To deal with the problem of unstructured nodal discretization, we use a mesh-free discretization strategy to establish a mapping of subsurface grid cells to a cloud of discrete points. The nodes are adaptively refined during the inversion process to better recover abnormal bodies. In addition, the hybrid basis function and the modified radial basis function are used to improve the accuracy and stability of the solution. We verify the effectiveness of the proposed method by using several synthetic and real tests.

The deep reflection seismic technique is essential to detect the basement of petroliferous basins and the fine structure of lithosphere. The traditional stacking or time migration is usually adopted for imaging deep reflection seismic data, but the accuracy is too limited to acquire precise imaging profiles. Development of an effective prestack depth migration method for deep reflection seismic data is the urgent requirement in the field of lithosphere structure detection. The Gaussian beam summation (GBS) migration requires no local slant stacking or phase approximation. It has higher imaging precision and better flexibility and applicability to the acquisition system, which can be well applied to imaging long-array and large-trace-interval deep reflection seismic data. In this letter, the GBS migration is extended into depth-domain imaging of deep reflection seismic data. Firstly, the basic principles of GBS migration are reviewed. Then, the validity of the method is verified by a migration test on a simple crustal-scale model. Finally, according to the crust-mantle structure of typical areas, a crustal model of western Sichuan and eastern Tibet and a classical collision model are established for migration experiments, and accurate deep-domain images are obtained. We mainly conducted numerical tests for the GBS migration of deep reflection seismic data in this letter. It was verified through imaging studies by the typical crust-mantle structure model that the proposed method was a prestack depth migration method suitable for the precise imaging of deep reflection seismic data, which provided a numerical basis for its subsequent imaging application in field data.

Imaging PS-wave is essential for converted wave exploration, especially in tilted transversely isotropic (TTI) media. Compared with the relatively low imaging accuracy of anisotropic time migration for complex structures, an accurate anisotropic PS-wave depth migration approach would be preferable. As an effective depth migration technique, Gaussian beam summation (GBS) migration can provide high-precision imaging for complex geological structures. In this letter, we extend the GBS to PS-waves imaging in anisotropic media and present an angle-domain GBS migration method for converted waves in 2-D TTI media. We first introduce the anisotropic ray-tracing-based angle-domain GBS imaging condition of PS-waves in TTI media, in which the sign of the incidence angle of P-waves is applied to decide the PS-wave polarity. After calculating the propagation angles at imaging points by using the data of real-value travel time of anisotropic Gaussian beams, we can get the P-wave incidence angles and then extract the corresponding PS-wave angle-domain common-image gathers (ADCIGs). The performance of our method is verified by two numerical tests, indicating that it is an effective migration algorithm for accurately imaging converted waves in 2-D TTI media.

Processing pressure shear (PS)-wave data is more challenging than PP-wave data because of the asymmetry of the source-to-receiver ray paths, particularly for irregular surfaces and sparse acquisition. Gaussian beam summation (GBS) migration is an effective method for imaging seismic data from irregular surfaces. In this letter, we introduce a converted PS-wave migration method for irregular surfaces using GBS, in which a scalar wavefield is used for wavefield propagation imaging. Cross correlation imaging is performed using forward-continued source wavefields calculated with the P-wave and reverse-continued wavefields calculated using the S-waves from the receivers. The corresponding Green's function is constructed as a superposition integral of the Gaussian beams emitted from the source and the receivers on the irregular surfaces, respectively. Numerical tests demonstrate that the method is a flexible and effective alternative for accurate imaging of converted PS-wave data from irregular surfaces.

In this letter, two machine learning algorithms are improved, including a sample-compressed neural network algorithm for magnetotelluric (MT) inversion and an adaptive-clustering analysis algorithm for boundary demarcation. MT is widely used in deep geological structure exploration; however, data processing and interpretation still need to be further improved. Inverting the underground electrical structure model from the surface electromagnetic response is a highly nonlinear optimization problem. Common quasi-linear algorithms rely on the initial model and are easy to converge to a local minimum. In addition, demarcating the boundary and attributes of the abnormal bodies according to the inversion results is often manual, inefficient, and haphazard. The validity of the above two machine learning methods is proved by using the simulated data and the actual data. The new algorithms can improve the efficiency and automation of MT data inversion imaging.