Search Results (7)

The Qiangtang Basin (Tibetan Plateau) poses significant geophysical challenges for seismic exploration due to near-surface widespread permafrost and steeply dipping Mesozoic strata induced by the Cenozoic Indo-Eurasian collision. These seismic geological conditions considerably contribute to lower signal-to-noise ratios (SNRs) with complex wavefields, to some extent reducing the reliability of conventional seismic imaging and structural interpretation. To address this, the common-reflection-surface (CRS) stack method, derived from optical paraxial ray theory, is implemented to transcend horizontal layer model constraints, offering substantial improvements in high-SNR prestack gather generation and prestack depth migration (PSDM) imaging, notably for permafrost zones. Using 2D seismic data from the basin, we detailedly compare the CRS stack with conventional SNR enhancement techniques—common midpoint (CMP) FlexBinning, prestack random noise attenuation (PreRNA), and dip moveout (DMO)—evaluating both theoretical foundations and practical performance. The result reveals that CRS-processed prestack gathers yield superior SNR optimization and signal preservation, enabling more robust PSDM velocity model building, while comparative imaging demonstrates enhanced diffraction energy—particularly at medium (20–40%) and long (40–60%) offsets—critical for resolving faults and stratigraphic discontinuities in PSDM. This integrated validation establishes CRS stacking as an effective preprocessing foundation for the depth-domain imaging of complex permafrost geology, providing critical improvements in seismic structural resolution and reduced interpretation uncertainty for hydrocarbon exploration in permafrost-bearing basins. Full article

The proposed 3D stitching of spatially constrained Common Midpoint (CMP) 1D inversions is a method that integrates 1D laterally constrained inversion of marine Controlled-Source Electromagnetic (CSEM) data in the CMP domain to generate a cube of resistivities from 3D surveys. An interpretive model is built in the form of a set of columns of homogeneous cells that form a 3D resistivity grid. The proposed methodology is an iterative process that updates the entire model until it minimizes the data misfit between the real and synthetic data. We connect the model cells by smoothing regularization applied in all three directions, which generates stable solutions. Additionally, we evaluate the inversion result constrained by hard information, for example, well log resistivity data. Two applications to synthetic data and the inversion of a real data set illustrate the method. The synthetic data were generated from 3D models, one with two resistive targets at different depths and a second with a target inside a conductive layer over a resistive basement. The real data were gathered off the southeast coast of Brazil, in an area of gas hydrate accumulation. The results indicate that the method can generate useful approximations to the resistivity structures under the survey area in a much shorter time than that of a full 3D inversion. Full article

We implement an inversion-based deblending method in the common midpoint gathers (CMP) as an alternative to the standard common receiver gather (CRG) domain methods. The primary advantage of deblending in the CMP domain is that reflections from dipping layers are centred around zero offsets. As a result, CMP gathers exhibit a simpler structure compared to common receiver gathers (CRGs), where these reflections are apex-shifted. Consequently, we can employ a zero-offset hyperbolic Radon operator to process CMP gathers. This operator is a computationally more efficient alternative to the apex-shifted hyperbolic Radon required for processing CRG gathers. Sparse transforms, such as the Radon transform, can stack reflections and produce sparse models capable of separating blended sources. We utilize the Radon operator to develop an inversion-based deblending framework that incorporates a sparse model constraint. The inclusion of a sparsity constraint in the inversion process enhances the focusing of the transform and improves data recovery. Inversion-based deblending enables us to account for all observed data by incorporating the blending operator into the cost function. Our synthetic and field data examples demonstrate that inversion-based deblending in the CMP domain can effectively separate blended sources. Full article

In many regions, particularly coastal areas, population growth, overuse of water, and climate change have put quality and availability of water under threat. While accurate, predictive groundwater flow models are essential for effective water resource management, the precision of these models relies on the ability to determine the geometries of geological structures and hydrogeologic systems accurately. In complex geological settings or with deep aquifers, the drilling of observation wells becomes too costly and shallow seismic surveys become the method of choice. Common-Reflection-Surface stacking is being used by major oil companies for hydrocarbon exploration but can serve also as an advanced imaging method for near-surface seismic data. Its spatial stacking aperture covers a whole group of neighboring common midpoint gathers and, as such, a multitude of traces contribute to every single stacking process. Since the level of noise suppression is proportional to the number of contributing traces, Common-Reflection-Surface stacking generates a large increase in signal-to-noise ratio. In addition, the data-driven velocity analysis is a statistical process and is, as such, the more stable the more input traces it has. At the beginning, however, the spatial operator was only used for stacking, not for velocity analysis, since limiting computational demand was mandatory to obtain results within a reasonable time frame. Today’s computing facilities are thousands of times faster and even large efficiency gains do not justify the loss of effectiveness anymore that comes with a truncated velocity analysis. We show that this is particularly true for near-surface data with low signal-to-noise ratio and modest common midpoint fold. For the spatial velocity analysis, we present two options: (1) as reference, a global search of all three parameters of the Common-Reflection-Surface operator, and (2) as a quicker solution, a strategy that uses the two-parameter Common-Diffraction-Surface operator to obtain initial values for a local three-parameter optimization. For shallow P-wave data from a hydrogeological survey, we show that the computational cost of option (2) is one order of magnitude smaller than the cost of option (1), while the stack and corresponding normal-moveout velocities are very similar. Comparing the results of the spatial velocity analysis to those of preceding, computationally lighter, strategies, we find a significant improvement, both in stack section resolution and stacking parameter accuracy. Full article

Internal multiple prediction remains a high-priority problem in seismic data processing, such as subsurface imaging and quantitative amplitude analysis and inversion, particularly in the common-midpoint (CMP) gathers, which contain multicoverage reflection information of the subsurface. Internal multiples, generated by unknown reflectors in complex environments, can be reconstructed with certain combinations of seismic reflection events using the inverse scattering series internal multiple prediction algorithm, which is usually applied to shot records in source–receiver coordinates. The computational overhead is one of the major challenges limiting the strength of the multidimensional implementation of the prediction algorithm, even in the coupled plane-wave domain. In this paper, we first comprehensively review the plane-wave domain inverse scattering series internal multiple prediction algorithm, and we propose a new scheme of achieving 2D multiple attenuation using a 1.5D prediction algorithm in the CMP domain, which significantly reduces the computational burden. Moreover, we quantify the difference in behavior of the 1.5D prediction algorithm for the shot/receiver and the CMP gathers on tilted strata. Numerical analysis of prediction errors shows that the 1.5D algorithm is more capable of handling dipping generators in the CMP domain than in the shot/receiver gathers, and it is able to predict the accredited traveltimes of internal multiples caused by dipping reflectors with small inclinations. For more complex cases with large inclination, using the 1.5D prediction algorithm, internal multiple predictions fail both in the CMP domain and in the shot/receiver gathers, which require the full 2D prediction algorithm. To attenuate internal multiples in the CMP gathers generated by large-dipping strata, a modified version is proposed based on the full 2D plane-wave domain internal multiple prediction algorithm. The results show that the traveltimes of internal multiples caused by dipping generators seen in the simple benchmark example are correctly predicted in the CMP domain using the modified 2D prediction algorithm. Full article

The velocity of near-surface materials is one of the most important for Ground-Penetrating Radar (GPR). In the study, we evaluate the options for determining the GPR velocity to measure the accuracy of velocity approximations from the acquired GPR data at an experimental site in Hangzhou, China. A vertical profile of interval velocities can be estimated from each common mid-point (CMP) gather using velocity spectrum analysis. Firstly, GPR data are acquired and analyzed using the popular method of hyperbola fitting which generated surprisingly high subsurface signal velocity estimates while, for the same profile, the Amplitude variation with offset (AVO) analysis of the GPR data (using the same hyperbola fitting method) generate a more reasonable subsurface signal velocity estimate. Several necessary processing steps are applied both for CMP and AVO analysis. Furthermore, experimental analysis is conducted on the same test site to get velocities of samples based on dielectric constant measurement during the drilling process. Synthetic velocities generated by AVO analysis are validated by the experimental velocities which confirmed the suitability of velocity interpretations. Full article

China is strongly dependent on railway transportation, but the frost heaving of the subgrade in cold regions has seriously affected the safety and comfort of trains. Moisture content is an essential parameter in the subgrade frost heave. Non-destructive and efficient geophysical methods have great potential in measuring the moisture content of railway subgrade. In this paper, we use the common mid-point (CMP) measurement of ground penetrating radar (GPR) to estimate the propagation velocity of electromagnetic waves in a subgrade application. We establish a synthetic model to simulate the railway subgrade structure. The synthetic CMP gathers acquired from shallow and thin layers are seriously disturbed by multiple waves and refraction waves, which make the routine velocity analysis unable to provide accurate velocities. Through the analysis of numerical simulation results, it is found that the primary reflection waves, multiple waves, and refraction waves are dominant in different offset ranges of CMP gather. Therefore, we propose a solution of the optimal gather at a certain range of offset dominated by the primary reflection wave to calculate the velocity spectrum and extract the accurate velocities for the subgrade model. The relative dielectric constants of the corresponding layers are calculated after the stacking velocities are converted into the interval velocities. Then, the moisture content is obtained by the Topp formula, which expresses the relationship between dielectric constant and moisture content. Finally, we apply the optimal gather scheme and the above interpretation process to the GPR data acquired at the railway site, and we form a long moisture content profile of the railway subgrade. Compared with the polarizability measured by the induced polarization (IP) method, it is found that the regions with high moisture content correspond to polarizability anomalies with different strengths. The comparison shows the reliability of GPR results to some extent. Full article