Common Midpoint Research Articles

High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data

Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency–wavenumber–scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency–wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time–space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.

Novel method to determine bedrock from a single sensor based seismic reflection signal using Haar wavelet transform

The determination of bedrock depth is crucial across various earth sciences and related fields. Geophysical techniques, notably the continuous wavelet transform, are increasingly employed to map subsurface bedrock structures. This method allows the transformation of 1D time domain seismic reflection signals into a 2D image, providing time–frequency representations for feature extraction. Leveraging the advantages of wavelet theory, particularly the Haar wavelet known for its simplicity and compact support, this study utilizes continuous Haar wavelet local maxima lines to enhance bedrock analysis. Seismic reflection data is acquired using an extremely sensitive geophone as a receiver along with a high dynamic range data logger. The Common Midpoint (CMP) approach, coupled with optimized offset distances, is utilized to ensure robust data quality and signal strength. Simple sources, such as a sledgehammer, generate sound waves for data collection prior to borehole drilling, correlating acquired data with lithogeological information. This research emphasizes the significance of adjusting parameters like sampling frequency, choice of wavelet, and optimal offset distances. The methodology employed, involving a single geophone, a one-channel data recorder, and a basic sledgehammer as a sound source, offers an inexpensive, straightforward, and non-invasive approach. The study demonstrates the effectiveness of Haar wavelet local maxima lines in accurately determining bedrock depth, producing results closely aligned with both predicted and actual depths.

Inversion-Based Deblending in Common Midpoint Domain Using Time Domain High-Resolution Radon

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.

Ultra-dense nodal seismic acquisition for automated geohazard analysis

In this paper, we address the problem of shallow geohazard mapping by developing an integrated workflow consisting of nodal seismic acquisition and automated data analysis. Densely spaced point-receiver land nodes and point sources are adopted as an alternative to receiver arrays or groups conventionally used in seismic exploration. The recently developed methodology of surface-consistent decomposition of the transmitted wavefield is applied to the nodal data. Ultra-resolution mapping of surface-consistent attributes in amplitude and phase is obtained, together with the generation of high-energy signal beams at the common midpoint positions, forming virtual (shot) supergathers (VSGs). The VSG preserves frequency and true-amplitude content thanks to the surface-consistent corrections applied before beam forming. Traveltimes, waveforms, and surface waves are then analyzed in the high signal-to-noise ratio VSG, leading to inversion of the subsurface parameters. Human-intensive interpretation of surface-wave dispersion curves in the f-k spectra is facilitated by the introduction of efficient machine learning algorithms for the unsupervised and supervised paradigms. The ultra-dense nodal acquisition and automated data analysis are effectively applied to the analysis of several cases of near-surface geohazards.

Reconstruction and denoising of high-dimensional seismic data via Frobenius-nuclear mixed norm constraints

Abstract The seismic data acquisition design with ‘two-wide and one-high’ geometry effectively improves the imaging quality of seismic records. However, when data are acquired in the field, complex near surface conditions and environmental factors can introduce a variety of noises and gaps in seismic data, impacting the accuracy of seismic imaging. Currently, the method of low-rank matrix/tensor completion is commonly employed for data reconstruction after normal moveout (NMO). In a complex subsurface medium, common midpoint data processed with NMO may not satisfy the linear or quasi-linear assumptions within local data windows. Therefore, this paper exploits the inherent low-rank structure of high-dimensional data to propose a high-dimensional tensor completion method under the Frobenius-nuclear mixed norm constraint (FN-TC). This method unfolds the 4D data tensor into the frequency-space domain along its modes (m, n) and subsequently imposes a non-convex Frobenius-nuclear mixed norm constraint on the unfolded approximate matrices. This approach closely approximates the rank function of the factor matrices, thereby enhancing the accuracy of data modeling. Theoretical and practical studies demonstrate that the novel FN-TC approach can effectively reconstruct high-dimensional seismic data and suppress noise, thereby providing data support for subsequent high-precision seismic imaging.

Seismic deconvolution based on SNR-weighted stacking

We propose a seismic deconvolution algorithm based on signal-to-noise ratio (SNR)-weighted stacking. First, we applied the time-frequency analysis to decompose the common midpoint (CMP) or common reflection point (CRP) gather. It is considered that there are differences in noise between different traces in the frequency domain profile. Then, we employ an SNR-weighted stacking strategy in each frequency-domain profile. Finally, we construct the resolution-enhancement amplitude spectrum, apply a linear inversion approach to calculate the overall weights of each frequency-domain profile, perform weighted stacking between the frequency-domain profiles, and obtain the resolution-enhancement post-stack data. The frequency decomposition of the pre-stack gather is efficiently utilized for the primary data of the proposed method. The SNR-weighted stacking adaptively selects the components with high SNR at each frequency. In addition, the resolution-oriented stacking weights are determined by the linear inversion approach. This algorithm can be adaptively weighted by considering the SNR of each frequency component, which can effectively overcome the problem of low SNR in spectral simulation and obtain high-resolution seismic data. The algorithm does not include wavelet extraction and regularization; thus, the resolution-enhancement data is more reliable. Applications to synthetic and field seismic datasets demonstrate that the proposed resolution-enhancement methodology is of great benefit to the succeeding structural interpretation and thin-bed identification.

Using word evolution to predict drug repurposing

BackgroundTraditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.ResultsWord embeddings, which represent a word’s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug’s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.ConclusionsThe annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique’s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

Near offset reconstruction for marine seismic data using a convolutional neural network

AbstractMarine seismic data is often missing near offset information due to separation between the source and receiver cables. To solve this problem, a convolutional neural network is trained on synthetic seismic data to reconstruct the near offset gap. The synthetic data is created using a two‐dimensional finite difference method within a heterogeneous velocity model. These synthetics are generated with a source‐over‐receiver acquisition geometry so that they contain complete near offset data. The convolutional neural network is then trained on input‐target synthetic pairs where the inputs are common midpoint gathers with the near offset section removed, and the targets are the same gathers with the near offset section retained. Following training, the robustness of the method is investigated with regards to common midpoint data sorting, normal moveout correction and changes in the velocity model. It is found that training on common midpoint‐sorted data results in 2.8 times lower error than training on shot gathers, that normal moveout correction of the training data makes no significant difference in error levels, and that the model can reconstruct realistic near offsets on synthetic data generated 10 km away within the heterogeneous velocity model. In field data testing, first a dataset with source‐over‐cable acquisition geometry from the Barents Sea is used to compare the reconstructed wavefields to ground truth values. Although the reconstructed amplitudes require minor scaling to match the true values, predictions on this dataset yield 2.5 times lower near offset reconstruction error compared to a simple Radon transform interpolation method. Furthermore, amplitude versus offset gradient and intercept sections from the Barents Sea dataset are estimated with half the error when including the convolutional neural network‐predicted near offset data, compared to only using the conventionally‐acquirable portion of the data (beyond 112.5 m of offset). In a secondary field data test, a conventional northern North Sea dataset is used to demonstrate how the method may be applied in practice. Here, the convolutional neural network generates more realistic predictions than the Radon method, and the gradient and intercept sections calculated using the convolutional neural network‐predicted traces have higher signal‐to‐noise ratios than the sections calculated using only the original data. The combination of high‐quality synthetic training data and interpolation in the common midpoint domain enables near offset reconstruction at significant depth (1 s of two‐way traveltime or more), which is demonstrated in both synthetic and field examples.

Comparative Study of Ground Penetrating Radar Systems with Time Delay Methods for Accurate Pavement Thickness and Dielectric Constant Estimation

The applicability of using ground penetrating radar (GPR) as a non-destructive technology for estimating the thickness and dielectric constant of pavement layers has been extensively documented in the literature. Time delay methods (TDMs) have been the most commonly used by researchers. However, various studies have reported different results with respect to the suitability of different GPR systems and TDMs for dielectric constant and thickness estimation. Thus, this study aims to compare the use of ground-coupled GPR and air-coupled GPR (AC-GPR) for estimating the dielectric constant of pavement materials. In the context of TDMs, the study examines three methodologies for assessing the traveled distance in the investigated layer. The study involves laboratory experiments using asphalt and dry sand. Also, it comprises a sensitivity analysis to examine the effect of different experimentation setups. The study employed two GSSI antennas: 51600S (GC-51600S) and GSSI 42000S (AC-42000S). Using the manufacturer’s recommended operation setups, the results generally indicated the possibility of simplifying the TDM calculation by considering the 42000S and 51600S antennas as monostatic for pavement materials with different dielectric constants (≥3) with an error rate within 5%. Moreover, the study indicates that using AC-GPR may be inappropriate for common midpoint analysis using the default mounting setups. These findings provide valuable insights into the suitability and limitations of different GPR systems and calculation methods for accurately estimating pavement’s dielectric constant and thickness.

GPR velocity correction method in transversely heterogeneous media based on CMP data

AbstractDetermining ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium, and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44%, and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications.

Geologic and Geophysical Evidence of Repeated Normal Faulting on the Itozawa Fault—the Source of the Mw 6.6 Iwaki Earthquake Triggered by the Mw 9.0 Tohoku-Oki Megathrust Earthquake, Northeast Japan

ABSTRACT The 2011 Mw 9.0 Tohoku-Oki earthquake ruptured the 500 km long and 200 km wide convergent plate margin between the North American and Pacific plates, and changed the crustal stress field and triggered widespread seismic activity in northeast Japan. In particular, many crustal earthquakes struck the southern Fukushima area. The largest inland normal-faulting earthquake with Mw 6.6 occurred on the northwest-trending Yunodake fault and the north-northwest-trending Itozawa fault on 11 April 2011. The coseismic surface ruptures appeared along the previously identified active and presumed active faults. To investigate the near-surface structure of the Itozawa fault, we conducted ground-penetrating radar (GPR) profiling across the fault, and two drilling surveys on its hanging-wall and footwall blocks. The GPR survey line about 50 m long was located near the previous trenching site. The GPR two-way travel-time sections were collected by common-offset modes by 50 and 100 MHz GPR systems. We acquired common midpoint ensembles on both sides of the surface rupture for depth conversion of the time sections. The resulting GPR sections show detailed subsurface structures to a depth of about 7 m. We identified event horizons for the 2011, penultimate, antepenultimate, and preantepenultimate earthquakes, indicating that the Itozawa fault has ruptured repeatedly as a normal fault. The vertical displacements during these earthquake events, obtained from the GPR sections, were about 0.8, 0.3, 0.4, and 0.7 m, respectively. These observations suggest that the coseismic surface offset amount on the Itozawa fault varies greatly. The recurrence intervals of surface-rupturing earthquakes on the Itozawa fault, calculated from the previous research and this study, are much longer than those of giant interplate earthquakes along the Japan trench, such as the 2011 Tohoku-Oki earthquake. We conclude that not every giant earthquake along the Japan trench triggers a rupture of the Itozawa fault.

Geomorphic mapping and analysis of neotectonic structures in the piedmont alluvial zone of Haryana state, NW-India: a remote-sensing and GPR based approach

The Himalayan Frontal Thrust (HFT) and the surrounding piedmont alluvial region represent a zone of active deformation in the Indo-Gangetic plains. We investigated the Piedmont zone between the Ghaggar and Yamuna River basins in Haryana, India, for geomorphic signatures of active tectonics using remotely sensed data and validated by Geophysical Ground Penetrating Radar (GPR) surveys. The possible locations and the types of active tectonic features such as sub-surface fault, ridges, lineaments and warps were identified based on the presence of geomorphic signatures such as drainage gradient anomalies, abrupt change in flow direction, river offset, compressed meanders, paleochannels and topographic breaks. We used various optical satellite imageries to detect and map the temporal changes in the flow pattern of rivers in the study area. GPR investigations were done at selected test sites to locate and verify the continuity of subsurface fault. The GPR profiles were taken in the North-South direction using the common midpoint technique with 40 and 100 MHz antennae. Low frequency bi-static GPR scanning confirmed a number of dipping reflectors due to fault planes and warp surfaces in the study area. It is concluded that the piedmont zone of Haryana is actively deforming and could become a future seismic hazard zone.

Inverse Scattering Series Internal Multiple Attenuation in the Common-Midpoint Domain

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.

Optimal transmit apodization for the maximization of lag-one coherence with applications to aberration delay estimation

Phase aberration is one of the major sources of image degradation in medical ultrasound imaging. One of the earliest and simplest techniques to correct for phase aberration involves nearest-neighbor cross correlation to estimate delays between neighboring receive channels and the compensation of aberration delays in a delay-and-sum beamformer. The main challenge is that neighboring receive channels may not have sufficient signal correlation to accurately estimate the aberration delays. Although algorithms such as the translating transmit aperture or the common midpoint gather are designed to perfectly maximize signal correlations between received signals, these algorithms require the use of different transmit apertures for each received signal. Instead, this work proposes the use of a single globally-applicable transmit apodization function that optimizes the lag-one coherence based on the van Cittert–Zernike theorem. For the application to phase aberration correction, it is shown across 20 different zero-mean Gaussian-random aberrators that the proposed optimal apodization function reduces the estimation error in the aberration delay profile from 22.85% to 15.72%.

Automatic velocity analysis with physics-constrained optimal surface picking

Stacking velocity is generally obtained by picking the energy peaks in velocity semblance which can be time consuming when performed manually. Numerous automatic methods have been proposed for accelerating the velocity picking but often generate physically unreasonable picking results when strong and spatially consistent energy anomalies (due to noise and multiples) appear in the semblance map. We develop a constrained optimal surface picking method to automatically pick a 2D velocity field from a 3D semblance volume with high efficiency and robustness. This method is improved from the 2D dynamic programming algorithm by incorporating vertical physical constraints in the time direction and lateral smoothness constraints in the common-midpoint (CMP) direction. The time-direction physical constraint ensures that the picked velocity is positive when converted to an interval velocity, whereas the CMP-direction smoothness constraint ensures that the picked 2D velocity field is laterally continuous. Tests on the Marmousi-2 model indicate that our constrained optimal surface picking algorithm improves the spatial structure consistency of the picked velocity field and is able to robustly avoid picking the strong and consistent energy features generated in the semblance volume by multiples. We further determine the robustness of our method in a 2D real data example by comparing our automatic picking from a 3D semblance volume with a result that is manually picked from individual 2D semblance slices. The comparison indicates that the general trend of our result is consistent with the manual picking and our result looks geologically more reasonable in detail and generates a better stacking image with improved, focused, and more continuous reflections.

Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering

The velocity of seismic data can initially be established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy clusters currently relies on manual work, with low efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth, and high-density seismic exploration technology, the amount of seismic data has increased significantly, greatly increasing the cost of manual labor and time. In this paper, an intelligent velocity picking method based on the Chan–Vese (CV) model and mean-shift clustering algorithm was proposed. It can be divided into three steps. First, a velocity trend band is set up on the velocity spectrum by experts to avoid multiples and other noises. Then, the velocity trend band is applied to the Chan–Vese model as the initial time condition to segment the velocity spectrum and obtain the velocity candidate region. Finally, mean-shift clustering is adopted to cluster the useful energy clusters retained in the candidate region derived from the Chan–Vese model. When implementing the mean-shift clustering algorithm, the Gaussian kernel function and the energy of the velocity spectrum are utilized to control the efficiency and accuracy of the cluster. The tests of the model and real data prove that the proposed method can dramatically improve the accuracy and efficiency of velocity picking compared with the K-means and manual picking method.

АНАЛИТИЧЕСКИЕ РЕШЕНИЯ ЗАДАЧ КОМПЕНСАЦИИ НЕОДНОРОДНОСТИ ВЕРХНЕЙ ЧАСТИ РАЗРЕЗА В СЕЙСМОРАЗВЕДКЕ МОВ

The solution of the problem of compensation of the near-surface heterogeneity (NSH) is an obligatory component of the digital processing technology of CDP data. The main result in the theory of solving this problem was determined by the equation of the normal traveltime curve of the common mid point with an additive component describing static corrections at the points of shotpoint and geophone. The substantiation of methods for compensation of heterogeneity based on the structure of high-speed model of the NSH is carried out in the present paper. Three variants of the NSH model corresponding to the zone of low velocities, heterogeneity of underlying bedrock deposits and variable relief of daytime surface are considered. The research subject is analytical methods for solving the problem of field continuation aimed at eliminating the influence of NSH on the kinematic parameters of wave field: method of static corrections and parametric method. Applications of methods are determined by the ratio of near-surface section velocities and underlying deposits. The requirements to the methodology of field observations and digital data processing ensuring the uniqueness of problem solution are considered for the compensation problem of heterogeneity of low-velocity zone.

MUSIC-Based Super-Resolution CMP Velocity-Depth Analysis for Multilayer Cases

This letter proposes a multiple signal classification (MUSIC)-based algorithm to generate a super-resolution velocity-depth spectrum for precise subsurface multilayer analysis, which cannot be achieved by conventional Common MidPoint (CMP) velocity-depth analysis. A self-adaptive peak detection process and a MUSIC algorithm with a modified steering matrix are the key components of the proposed method. Both numerical simulation and experimentation are used to verify the feasibility of this study. Regarding both accuracy and resolution, the results show that the suggested method outperforms the conventional SAR-based method, as the layer information on the spectra is correctly located and strongly focused. Moreover, the proposed approach can distinguish between the layers that cause weak reflections and those that cause strong reflections. Compared to previous studies, the proposed method enables self-adaptive super-resolution imaging in ground penetrating radar (GPR) CMP subsurface layer analysis, and it has a great potential for usage in other GPR subsurface applications.

Reduction of normal-moveout stretch using nonstationary scaling transformation in time-frequency domain

The normal-moveout (NMO) stretch causes decrease in the dominant frequency of seismic wavelet after conventional NMO correction and severely damages the quality of the stacked data for shallower reflectors at far offsets. Muting, which is commonly used to handle this problem, reduces seismic fold and negatively affects results of the amplitude-variation-with-offset analysis within the stretched area. We found a novel approach to reduce the stretching phenomenon through compensating the lost frequencies by increasing the dominant frequency of the seismic wavelet before applying the NMO correction. The added so-called compensated frequencies are defined according to the difference between the dominant frequency of the original seismic wavelet and the assumed stretched wavelet after the NMO correction. The corresponding procedure considers frequency content of each time sample along each trace in the time-frequency domain using the Gabor transform. As such, the dominant frequency of the seismic wavelets is increased in a nonstationary manner. Performance of our method is evaluated by applying it on the synthetic and field data examples. The obtained results suggest that this approach provides common-midpoint (CMP) gathers with reduced stretching effect, with the potential to be considered as another alternative for nonstretch NMO correction. However, it should be noted that the presented method resolves neither the problem of intersecting events nor the multiples in the CMP gather. This method also cannot handle highly contaminated noise data and does not contribute in removing multiples during the frequency compensation process.

A modified common midpoint approach for GPR radars

Ground Penetrating Radar (GPR) plays an important role among the non-destructive methods used to analyze, measure and collect data from pavement layers, building structures and archaeological sites. A GPR device consists of a radar able to get an image, technically called radargram, of the subsurface. Due to the decreasing costs of calculation power, it is now possible to analyze and interpret radargrams more efficiently than in past. While technological advancements and improvements in the post-processing of data have increased the power of this process, it is also important to consider survey technique as a key factor in nondestructive testing. The aim of this paper is to propose a modification of a well-known survey technique called Common Mid-Point (CMP). The conventional CMP survey technique requires that the transmitter and receiver of the GPR device be physically separated to survey a site. This provides the benefit of determining the speed of light in the host medium from the slant of the hyperbola reflection of a buried target. The CMP process unifies the inspection technology into a single device, making the use of GPR more accessible to robots on construction sites. The method documented in this paper is based on a mathematical model for adapting the conventional CMP for conventional GPR radar devices, in which transmitter and receiver cannot be separated physically.