Search Results (9)

Seismic data migration is a critical step for accurate subsurface imaging. While Ocean Bottom Cable (OBC) surveys provide high-quality seismic data, reliance on primary reflections alone leads to significant illumination gaps. Receiver-side ghost waves can mitigate these gaps; however, conventional mirror migration suffers from low resolution and amplitude inaccuracy. To address these limitations, this study introduces a high-resolution mirror migration framework based on Point Spread Function (PSF)-guided inversion imaging. The methodology involves first separating the OBC wavefield to isolate ghost-wave components, followed by applying standard mirror migration to produce an initial, blurred image. Subsequently, the PSFs of down-going ghost waves are estimated to characterize imaging distortions, and image-domain least squares migration (LSM) is implemented via PSF deconvolution to reconstruct high-resolution reflectivity. Numerical experiments on complex models demonstrate that the proposed method preserves the additional illumination provided by this wavefield, substantially improves the spatial resolution of imaging targets, and enhances lateral continuity. Quantitative analysis confirms this enhancement through a significant extension of the effective vertical wavenumber bandwidth and the recovery of higher-frequency content. The framework provides a robust and computationally efficient solution for high-fidelity OBC imaging, enabling more reliable subsurface interpretation. Full article

The inversion of shallow underground vibration fields primarily relies on signals collected by numerous sensors deployed on the surface. However, the accuracy of inversion is affected by the spatial distribution of these sensors. Therefore, under limited measurement points, signal reconstruction at unknown locations remains a critical challenge. To address this problem, we developed an SC-Net-based self-supervised interpolation method for missing wavefield data in shallow subsurface applications. This study utilizes incomplete seismic data acquired in real-world scenarios to train a neural network for seismic data interpolation, thereby expanding the sampled signals required for inversion. Since available seismic data samples are often scarce in practice, we adopt a hybrid training strategy combining simulated and real data. Specifically, a large number of numerically simulated samples are jointly trained with a limited set of real-world measurements. Furthermore, to enhance the robustness of network outputs, we integrate the Mean Teacher model framework and propose a self-supervised learning approach for missing data. Additionally, to enable the network to effectively capture long-range dependencies in both frequency and spatial domains of seismic data, we introduce a dual-branch feature fusion network that jointly models channel-wise and spatial relationships. Finally, in our actual field explosion experiments conducted at the test site, we demonstrated improved accuracy of our method through comparative analysis with several typical interpolation neural networks. Three ablation studies are also designed to demonstrate the effectiveness of the proposed approach. Full article

Exploration for hydrocarbon reservoirs is currently focused on increasingly difficult targets and geological structures, thus stimulating a growing requirement for new measurement methods and techniques that can provide more detailed information about lithology and reservoir parameter distribution in the vicinity of the target zone. This publication presents a method for increasing the resolution of the recorded surface seismic wavefield in the vicinity of example borehole Well-1 (western Poland) for reservoir horizons of the Rotliegend and Zechstein formations. The main stage of the research was the introduction of frequencies from vertical seismic profiling (VSP) into seismic traces. The shape filter deconvolution procedure was applied based on the operator calculated from VSP data, which was applied to seismic profiles extracted from 3D data. The procedure applied allowed for the reconstruction of higher-frequency spectrum necessary for a detailed imaging of the geological framework of the analyzed reservoir formations. In the next stage, seismic inversion calculations were conducted, both on VSP data (corridor stack and VSP-CDP transformation) and on surface seismic time sections. The results obtained as an acoustic impedance distribution enabled a more comprehensive structural interpretation and detailed analysis of the variability of reservoir properties in the analyzed well area. Full article

Balancing efficiency and accuracy is often challenging in the numerical solution of three-dimensional (3D) point source acoustic wave equations for layered media. To overcome this, an efficient solution method in the spatial-wavenumber domain is proposed, utilizing the Non-Uniform Fast Fourier Transform (NUFFT) to achieve arbitrary non-uniform sampling. By performing a two-dimensional (2D) Fourier transform on the 3D acoustic wave equation in the horizontal direction, the 3D equation is transformed into a one-dimensional (1D) space-wavenumber-domain ordinary differential equation, effectively simplifying significant 3D problems into one-dimensional problems and significantly reducing the demand for memory. The one-dimensional finite-element method is applied to solve the boundary value problem, resulting in a pentadiagonal system of equations. The Thomas algorithm then efficiently solves the system, yielding the layered wavefield distribution in the space-wavenumber domain. Finally, the wavefield distribution in the spatial domain is reconstructed through a 2D inverse Fourier transform. The correctness of the algorithm was verified by comparing it with the finite-element method. The analysis of the half-space model shows that this method can accurately calculate the wavefield distribution in the air layer considering the air layer while exhibiting high efficiency and computational stability in ultra-large-scale models. The three-layer medium model test further verified the adaptability and accuracy of the algorithm in calculating the distribution of acoustic waves in layered media. Through a sensitivity analysis, it is shown that the denser the mesh node partitioning, the higher the medium velocity, and the lower the point source frequency, the higher the accuracy of the algorithm. An algorithm efficiency analysis shows that this method has extremely low memory usage and high computational efficiency and can quickly solve large-scale models even on personal computers. Compared with traditional FEM, the algorithm has much higher advantages in terms of memory usage and efficiency. This method provides a new approach to the numerical solution of partial differential equations. It lays an essential foundation for background field calculation in the scattering seismic numerical simulation and full-waveform inversion of acoustic waves, with strong theoretical significance and practical application value. Full article

A reliable inspection of the tree trunk internal defects is often considered vital in the health condition assessment for the living tree. There has been a desire to reconstruct the internal structure quantitatively using a non-destructive testing technology. This paper intends to apply wavefield reconstruction inversion (WRI) to obtain high-precision information from tree trunk detection using ground penetrating radar data. The variational projection method and the grouped multi-frequency strategy are adopted to strengthen the algorithm stability and adaptability by inverting frequency components sequentially. Through an irregular trunk model test, the influence of the penalty parameter, initial model, frequency strategy, and grid generation methods are investigated on WRI. Additionally, the comparison between full waveform inversion and WRI is discussed in detail. This synthetic case indicates that WRI is efficient and for a reasonable result, a proper multi-frequency strategy and an accurate mesh closer to reality are important. Furthermore, a field case of a historical tree is used to prove the validity and reliability of the algorithm. The success in this case indicates that our algorithm can characterize the distribution of media parameters of tree trunks accurately, which could provide data support for the rejuvenation and maintenance of living trees. Full article

In the presented study, multi-parameter inversion in the presence of attenuation is used for the reconstruction of the P- and the S- wave velocities and the density models of a synthetic shallow subsurface structure that contains a dipping high-velocity layer near the surface with varying thicknesses. The problem of high-velocity layers also complicates selection of an appropriate initial velocity model. The forward problem is solved with the finite difference, and the inverse problem is solved with the preconditioned conjugate gradient. We used also the adjoint wavefield approach for computing the gradient of the misfit function without explicitly build the sensitivity matrix. The proposed method is capable of either minimizing the least-squares norm of the data misfit or use the Born approximation for estimating partial derivative wavefields. It depends on which characteristics of the recorded data—such as amplitude, phase, logarithm of the complex-valued data, envelope in the misfit, or the linearization procedure of the inverse problem—are used. It showed that by a pseudo-viscoelastic time-domain full-waveform inversion, structures below the high-velocity layer can be imaged. However, by inverting attenuation of P- and S- waves simultaneously with the velocities and mass density, better results would be obtained. Full article

High-precision detection of the underground pipelines is an indispensable part of the development and construction of cities. At present, the inversion technology for ground-penetrating radar (GPR) data is an effective means of realizing shallow-underground-space visualization in the field of geophysical exploration. However, the traditional full-waveform inversion (FWI) method usually faces the problems of strong nonlinearity of the objective function, high dependence on the initial model, and huge calculation cost. For improving the accuracy and efficiency of GPR data inversion, a wavefield reconstruction inversion (WRI) strategy is used for GPR data imaging to reduce the nonlinearity of the inversion problem and the dependence on the initial model. Then, the frequency weighting strategy and the multi-scale method are adopted to avoid the high-frequency component data dominating the optimization process and enhance the stability of inversion. In this paper, two numerical experiments of pipeline models with different materials and spacing or buried depths verified that the proposed method can effectively reconstruct the subsurface pipelines, and further performance of our algorithm on the field data verified the reliability of high-precision imaging of urban underground pipelines, which shows great potential of application in the field of high-precision detection of the urban underground pipelines. Full article

Recent progress in rotational sensor technology has made it possible to directly measure rotational ground-motion induced by seismic waves. When combined with conventional inertial seismometer recordings, the new sensors allow one to locally observe six degrees of freedom (6DOF) of ground-motion, composed of three orthogonal components of translational motion and three orthogonal components of rotational motion. The applications of such 6DOF measurements are manifold—ranging from wavefield characterization, separation, and reconstruction to the reduction of non-uniqueness in seismic inverse problems—and have the potential to revolutionize the way seismic data are acquired and processed. However, the seismological community has yet to embrace rotational ground-motion as a new observable. The aim of this paper is to give a high-level introduction into the field of 6DOF seismology using illustrative examples and to summarize recent progress made in this relatively young field. It is intended for readers with a general background in seismology. In order to illustrate the seismological value of rotational ground-motion data, we provide the first-ever 6DOF processing example of a teleseismic earthquake recorded on a multicomponent ring laser observatory and demonstrate how wave parameters (phase velocity, propagation direction, and ellipticity angle) and wave types of multiple phases can be automatically estimated using single-station 6DOF processing tools. Python codes to reproduce this processing example are provided in an accompanying Jupyter notebook. Full article

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations. Full article