Ground Roll Research Articles

Adaptive ground-roll attenuation using local nonlinear filtering

Source-generated coherent noise suppression is a long-standing issue in land seismic data processing. Traditional methods—such as f–k filtering, τ-p transform, and median filtering—often fail to remove coherent noise in irregular data, especially in the case that the noise highly overlaps with the signal. To address this issue, we developed a method based on local nonlinear filtering (LNF) to attenuate coherent noise in seismic data. In the proposed method, a novel similarity coefficient algorithm is presented to determine the optimal velocity of coherent noise. An initial model of coherent noise is constructed using a mixed median–mean filter and is then refined by the proposed weighting correction algorithm. After modeling the noise, coherent noise in seismic data can be then suppressed by adaptively subtracting the estimated noise from the raw seismic data. To verify the effectiveness of the proposed method, we applied it to regular and irregular synthetic data and a 3D field dataset containing strong ground roll. The results demonstrate that the new method successfully attenuates the ground roll and obtains better results in ground-roll attenuation and the preservation of useful signals compared with the three commonly used methods. Synthetic and real data examples indicate that the method is applicable to the attenuation of coherent noise in irregular data.

Measurement of aircraft ground roll distance during takeoff and landing on a grass runway

The aim of this study was to develop a method for airplane ground roll distance measurement. This method is based on measurements of aircraft accelerations, magnetic field vector, as well as Global Positioning System (GPS) position. A convolution neural network was developed in this study and it recognizes characteristic moments of flight during takeoff: longitudinal acceleration, nose wheel up (rotation), and liftoff, as well as during landing: approach, touchdown, rollout, and stop. The developed neural network includes two filters that enable collection of input data from both the total dataset as well as from data portions for information about the occurrence of a peak at the time of touchdown and the change of acceleration at the time of takeoff.The distance traveled by the plane on the ground during takeoff or landing is then calculated using the GPS coordinates, measured simultaneously. The method uses a sensor system consisting of accelerometers, magnetometers, and a GPS receiver; a prototype hardware was based on a single-board miniature computer. The hardware is small in size and fits anywhere in the aircraft cockpit. The method has been practically examined by means of flight tests and it was concluded that it performs well: the mean difference in the measured take-off and landing distances from the results obtained with the reference method was 2,92 % and 6,31 % respectively. The method was not sensitive to airplane design type: low wing vs. high wing. Also, the method has been tested for two different pilots and no significant difference in the results of measurement was observed.

Bolide impact effects on the West Florida Platform, Gulf of Mexico: End Cretaceous and late Eocene

Abstract This study documents seismic reflection evidence that two different bolide impacts significantly disrupted stratigraphic and depositional processes on the West Florida Platform (Gulf of Mexico). The first impact terminated the Late Cretaceous Epoch (Chicxulub impact, Mexico; ca. 66 Ma; end-Maastrichtian age). The second took place in the late Eocene (Chesapeake Bay impact, Virginia, USA, portion of the Chesapeake Bay; ca. 35 Ma; Priabonian age). Both impacts produced far-reaching seismic shaking and ground roll followed by an impact-generated tsunami, the effects of which are evident in the seismostratigraphic record. The Chicxulub seismic shaking caused collapse and shoreward retreat of the Florida Escarpment and widely disrupted (faulting, folding, slumping) normal flat-lying shelf beds. The associated tsunami currents redistributed these shelf deposits and mixed them together with collapse debris from the escarpment to form a thick wedge of sediments along the base of the escarpment. The Chesapeake Bay impact created a mounded sedimentary deposit near the outer edge of the late Eocene ramp slope. This deposit also has a bipartite origin. A lower layer is marked by en echelon faulting created in situ by seismic shaking, whereas an upper layer represents sediments redistributed from the late Eocene shelf and upper ramp slope by tsunami-driven bottom currents (debris flows, contour currents, slumps). This is the first report of seismic effects from the Chesapeake Bay impact in the Gulf of Mexico. These results further demonstrate that large-scale marine bolide impacts have widespread effects on the stratigraphic and depositional record of Earth.

Ground Roll Attenuation of Multicomponent Seismic Data with the Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) Method

Multicomponent seismic exploration provides more wavefield information for imaging complex subsurface structures and predicting reservoirs. Ground roll is strongly coherent noise in land multicomponent seismic data and exhibits similar features, which are strong energy, low frequency, low velocity and dispersion, in each component. Ground roll attenuation is an important step in seismic data processing. In this study, we utilized multivariate empirical mode decomposition to multicomponent seismic data for attenuating ground roll. By adding extra components containing independent white noise, noise-assisted multivariate empirical mode decomposition is adopted to overcome the mode-mixing effect in standard empirical mode decomposition. This method provides a more robust analysis than the standard empirical mode decomposition EMD method performed separately for each component. Multicomponent seismic data are decomposed into different intrinsic mode functions in frequency scale. According to different frequency scales of seismic reflection wave and ground roll, intrinsic mode functions with low frequency are eliminated to suppress ground roll, and the remaining are reconstructed for seismic reflection waves. Synthetic and field data tests show that the proposed approach performs better than the traditional attenuation method.

Nonhyperbolic normal moveout stretch correction with deep learning automation

Normal-moveout (NMO) correction is a fundamental step in seismic data processing. It consists of mapping seismic data from recorded traveltimes to corresponding zero-offset times. This process produces wavelet stretching as an undesired by-product. We have addressed the NMO stretching problem with two methods: (1) an exact stretch-free NMO correction that prevents the stretching of primary reflections and (2) an approximate post-NMO stretch correction. Our stretch-free NMO produces parallel moveout trajectories for primary reflections. Our post-NMO stretch correction calculates the moveout of stretched wavelets as a function of offset. Both methods are based on the generalized moveout approximation and are suitable for application in complex anisotropic or heterogeneous environments. We use new moveout equations, modify the original parameter functions to be a constant over the primary reflections, and then interpolate the seismogram amplitudes at the calculated traveltimes. For fast and automatic modification of the parameter functions, we use deep learning. We design a deep neural network (DNN) using convolutional layers and residual blocks. To train the DNN, we generate a set of 40,000 synthetic NMO-corrected common-midpoint gathers and the corresponding desired outputs of the DNN. The data set is generated using different velocity profiles, wavelets, and offset vectors, and it includes multiples, ground roll, and band-limited random noise. The simplicity of the DNN task — a 1D identification of primary reflections — improves the generalization in practice. We use the trained DNN and find successful applications of our stretch-correction method on synthetic and different real data sets.

Ground-roll extraction using the Karhunen-Loeve transform in the time-frequency domain

Ground-roll suppression is critical for seismic reflection data processing. Many standard methods, i.e., f-k fan filtering, fail when spatially aliased surface wave interference is present in the data. Spatial aliasing is a common problem; receiver spacing is often not dense enough to extract wavenumbers of low-velocity surface waves. It has long been known that the Karhunen-Loeve (KL) transform can be used to suppress aliased ground roll. However, the ground roll should be flattened before suppression, which is challenging due to the dispersion of surface wave velocities. I propose to solve this problem via the time-frequency domain. I apply the S-transform, which was previously shown to perform well in the multichannel analysis of surface waves. A simple complex-valued constant phase shift is a suitable model of surface wave propagation in common-frequency S-transform gathers. Therefore, it is easy to flatten the corresponding S-transform narrow-band frequency surface wave packet and extract it from the data by principal component analysis of the corresponding complex-valued data-covariance matrix. As the result, our S-transform KL (SKL) method filters the aliased ground roll without damaging the reflection amplitudes. The advantages of SKL filtering have been confirmed by synthetic- and field-data processing.

Robust Ground Roll Noise Suppression Based on Dictionary Learning and Bandpass Filtering

Ground roll noise is a common type of seismic noise arising from land acquisition. Suppressing ground roll noise has long been a challenging problem due to the strong coupling between ground rolls and reflection signals. The easiest way to suppress ground roll noise is by a bandpass filter, which removes the low-frequency ground roll and retains the high-frequency signals. Due to the coupling issue, the bandpass filter, however, is prone to cause the removal of low-frequency signals and the existence of high-frequency noise. To combat this problem, we propose a novel dictionary learning (DL) method for decoupling the ground rolls and reflection signals in the low-frequency band. We first filter the seismic shot gather using a strong low-pass filter, by which we remove all ground rolls. Then, we apply the DL method to retrieve the leaked low-frequency reflection signals. The dictionary atoms are obtained from the high-frequency reflection signals and then used to code the low-frequency mixture between ground rolls and signals. As a result, the reflection signals in the low-frequency part are easily separated by the sparse coding process and are added back to the high-frequency signals. The final output of the proposed algorithm is the summation between the high-frequency reflections and the retrieved low-frequency reflections. We apply the proposed method to both synthetic and real shot gathers containing ground roll noise and demonstrate its promising results.

Seismic Denoising Based on Time-Varying Filtering and Empirical Mode Decomposition in the fx Domain

Ground roll is a type of coherent noise with low velocity, low frequency and high energy, which negatively affects the quality of the seismic data. The contamination of the ground roll is a persistent problem in the seismic processing field. For this reason, we considered the predictability of useful signals in the fx domain, introduced the time-varying filtering based on the empirical mode decomposition (TVFEMD) into the fx domain, and therefore proposed a novel algorithm (Ada-fx-TVFEMD). Similar to the Variational Mode Decomposition (VMD), the TVFEMD algorithm consists in adaptive decomposition of multi-component signals. The TVFEMD algorithm is more suitable for nonstationary signals because of its time-varying characteristic. The proposed Ada-fx-TVFEMD algorithm performs decomposition of each frequency slice and reconstruction of partial Intrinsic Mode Functions (IMFs). Moreover, it allows to accurately select the IMFs that contain ground roll based on automatic ground roll identification. A presented synthetic example illustrates the superiority of the Ada-fx-TVFEMD algorithm in ground roll attenuation. Applying the proposed method on field data further demonstrates its potential in industrial applications, compared with frequency-wavenumber (FK) filtering and fx-VMD.

Spatial-Domain Synchrosqueezing Wavelet Transform and Its Application to Seismic Ground Roll Suppression

Spatial-Domain Synchrosqueezing Wavelet Transform and Its Application to Seismic Ground Roll Suppression

Near Surface Characterization by Seismic Surface Wave Inversion

Surface waves are usually treated as unwanted coherent noise and steps are taken to remove them using geophone receiver arrays during seismic survey acquisition. During early data processing, attempts are made to further remove the ground roll. Recently, there has been a growing interest in using surface waves for estimating the near surface velocity model. Surface waves propagate in the shallow subsurface and can be used to derive shear wave velocities of the near surface.

Noise suppression using a near-source wavelet

Denoising becomes a nontrivial task when the noise and signal overlap in multiple domains, such as time, frequency, and velocity. Fortunately, signal and noise waveforms, in general, tend to remain morphologically different and such differences can be used to separate body-wave signals from other waveforms such as ground roll and cultural noise. The key in denoising using a near-source wavelet is to find a wavelet that is a close approximation of the true source signature and remains uncontaminated by the Green’s function in any significant manner. An inverse filter designed using such a wavelet selectively compresses the body waves that can be extracted using median and low-pass filters. The overall phenomenon is explained with a synthetic example. The idea is also tested on a land data set generated using a large weight-drop source in which the wavelet recorded approximately 3 m from the source location fulfills the criteria set in our method. The results suggest that the incremental effort of recording an extra trace close to the source location during acquisition may provide previously unavailable denoising opportunities during processing, although the trace itself may be redundant for imaging.

Unsupervised dual learning for seismic data denoising in the absence of labelled data

ABSTRACTThe training and updating of the supervised deep learning model are both dependent on labelled data. Unfortunately, the data‐labelling process is usually expensive and time consuming, which makes obtaining labelled data a huge challenge, especially in the field of geophysics. Because of the above‐mentioned factors, some seismic data denoising methods based on supervised deep learning use synthetic seismic data for network training. Since synthetic seismic data are more or less different from real seismic data, the denoising results may have some amount of residual noise and some false signals when dealing with real seismic data. In response to the above problems, this paper introduces an unpaired domain‐to‐domain translation method based on the framework of two‐way generative adversarial networks, which does not need to use labelled data for training, and effectively solves the problem of lacking labelled data in the seismic data denoising tasks. We use an unpaired mixed training set containing synthetic seismic data and real seismic data to train the network, which effectively improves the denoising ability of the network for the real seismic data. The experimental results show that compared with the state‐of‐the‐art denoising methods such as denoising convolutional neural network, the proposed method can suppress the random noise and ground roll more thoroughly, and the denoising results basically would have no false signals.

The appraisal of surface orbital vibrators with buried geophone array for permanent reservoir monitoring

ABSTRACTTime‐lapse seismic is one of the main methods for monitoring changes in reservoir conditions caused by production or injection of fluids. One approach to time‐lapse seismic is through permanent reservoir monitoring, whereby seismic sources and/or receivers are permanently deployed. Permanent reservoir monitoring can offer a more cost‐effective and environmentally friendly solution than traditional campaign‐based surveys that rely on temporarily deployed equipment while facilitating more frequent measurements. At the CO2CRC Otway Project, surface orbital vibrators were coupled to a buried geophone array to form a permanent reservoir monitoring system. These are fixed position seismic sources that provide both P and S waves using induction motor‐driven eccentric masses. After an initial injection of CO2 in February 2016, five months of continuous seismic data were acquired, and reflection imaging was used to assess the system performance. Analysis of the data showed the effects of weather variations on the near‐surface conditions and the sweep signatures of surface orbital vibrators. Data processing flows of the continuous data was adapted from Vibroseis four‐dimensional data processing flows. Ground roll proved a significant challenge to data processing. In addition, variations in the surface wave pattern were linked to major rainfall events. For the appraisal of surface orbital vibrators in imaging, a Vibroseis four‐dimensional monitor survey data with similar geometry was also processed. Surface orbital vibrators are observed to be reliable sources with a potential to provide a repeatable signal, especially if the ground roll should fall outside the target window of interest. To guide future permanent reservoir monitoring applications, a repeatability analysis was performed for the various key data processing steps.

Physics-constrained deep learning for ground roll attenuation

We have developed a method to combine unsupervised and supervised deep-learning approaches for seismic ground roll attenuation. The method consists of three components that have physical meaning and motivation. The first component is a convolutional neural network (CNN) to separate a seismic record into ground roll and signal, while minimizing the residual between the sum of the generated signal and ground roll from two subnetworks and the input seismic record. The second component creates a maximum separation of signal and ground roll in the f- k domain, by training a supervised classifier. The third component is a CNN mapping signal to ground roll, which overcomes the problem of finding appropriate masks in traditional methods. Each component in our method is closely related to and motivated by the wave characteristics of the ground roll. Test results on field seismic records demonstrate the effectiveness of combining these components in preventing signal leakage and removing ground roll from seismic data.

Simulation of scattered seismic surface waves on mountainous onshore areas: Understanding the “ground roll energy cone”

The accurate simulation of seismic surface waves on complex land areas requires elastic models with realistic near-surface parameters. The SEAM Phase II Foothills model, proposed by the SEG Advanced Modeling (SEAM) Corporation, is one of the most comprehensive efforts undertaken by the geophysics community to understand complex seismic wave propagation in foothills areas. However, while this model includes a rough topography, alluvial sediments, and complex geologic structures, synthetic data from the SEAM consortium do not reproduce the qualitative characteristics of the scattering energy that is generally interpreted as the “ground roll energy cone” on shot records of real data. To simulate the scattering, a near-surface elastic model in mountainous areas ideally must include the following three elements: (1) rough topography and bedrock, (2) low-velocity layer, and (3) small-scale heterogeneities (size approximately Rayleigh wavelength). The SEAM Foothills model only includes element (1) and, to a lesser extent, element (2). We represent a heterogeneous near surface as a random medium with two parameters: the average size of the heterogeneities and fractional fluctuation. A parametric analysis shows the influence of each parameter on the synthetic data and how similar it is compared to real data acquired in a foothills area in Colombia. We perform the analysis in the shot gather panel and dispersion image. Our study shows that it is necessary to include the low-velocity layer and small-scale distributed heterogeneities in the shallow part of the SEAM model to get synthetic data with realistic scattered surface-wave energy.

In-situ biofilm detection in field settings using multichannel seismic

Biofilm aids a variety of geoengineering applications but a general lack of in-situ detection and monitoring methods severely limits its field usage. In this pioneering study, we show that in-situ field detection of biofilm may be possible through multichannel surface seismic. Our study was conducted at a landfill site in Norman, Oklahoma, where an ongoing interaction of a leachate plume with a fluctuating water table has created optimal conditions for biofilm growth. We acquired multichannel seismic data along a 130 m long profile and inverted the ground roll and refractions respectively for S-wave (VS) and P-wave (VP) velocity structures of the shallow (0–5 m) subsurface. We find that within ~1.2 m–~3.0 m depth, which is the water table fluctuation zone (WTFZ), VS increases by ~50% over the background but without any appreciable changes in the VP. Environmental scanning electron microscopy of soil samples from various depths along a core shows the presence of biofilm exclusively within the WTFZ. A VS-only increase due to biofilm growth is consistent with laboratory studies of similar nature and can be realized through a mechanistic model where biofilm is simultaneously present in two morphologies: grain-cementing and pore-filling. Our findings open doors to detecting biofilm in a variety of settings such as hyporheic zones and contaminant plume fringe and for a range of purposes from soil remediation to carbon sequestration to detecting life in other planets.

Initial sizing of a roadable personal air vehicle using design of experiments for various engine types

Purpose The purpose of this paper is to analyze and compare the performances of novel roadable personal air vehicle (PAV) concepts that meet established operational requirements with different types of engines. Design/methodology/approach The vehicle configuration was devised considering the dimensions and operational restrictions of the roads, runways and parking lots in South Korea. A folding wing design was adopted for road operations and parking. The propulsion designs considered herein use gasoline, diesel and hybrid architectures for longer-range missions. The sizing point of the roadable PAV that minimizes the wing area was selected, and the rate of climb, ground roll distance, cruise speed and service ceiling requirements were met. For various engine types and mission profiles, the performances of differently sized PAVs were compared with respect to the MTOW, wing area, wing span, thrust-to-weight ratio, wing loading, power-to-weight ratio, brake horsepower and fuel efficiency. Findings Unlike automobiles, the weight penalty of the hybrid system because of the additional electrical components reduced the fuel efficiency considerably. When the four engine types were compared, matching the total engine system weight, the internal combustion (IC) engine PAVs had better fuel efficiency rates than the hybrid powered PAVs. Finally, a gasoline-powered PAV configuration was selected as the final design because it had the lowest MTOW, despite its slightly worse fuel efficiency compared to that of the diesel-powered engine. Research limitations/implications Although an electric aircraft powered only by batteries most capitalizes on the operating cost, noise and emissions benefits of electric propulsion, it also is most hampered by range limitations. Air traffic integration or any safety, and noise issues were not accounted in this study. Practical implications Aircraft sizing is a critical aspect of a system-level study because it is a prerequisite for most design and analysis activities, including those related to the internal layout as well as cost and system effectiveness analyses. The results of this study can be implemented to design a PAV. Social implications This study can contribute to the establishment of innovative PAV concepts that can alleviate today’s transportation problems. Originality/value This study compared the sizing results of PAVs with hybrid engines with those having IC engines.

Ground roll separation method via threshold filtering and constraint of seismic wavelet support in curvelet domain

Ground roll separation method via threshold filtering and constraint of seismic wavelet support in curvelet domain

A combined method using singular spectrum analysis and instantaneous frequency for the ground-roll filtering

SUMMARY The noise attenuation is a fundamental step in seismic data processing, especially when ground-roll suppression remains a challenge. Rank-reduction methods have become quite popular in recent decades, as they promote significant improvements in the quality of data, highlighting reflections in seismograms. We present a methodology for ground-roll filtering, which combines the application of a recursive-iterative singular spectrum analysis method, in the time domain, as a particular way to decompose seismic data, with the computation of the average instantaneous frequency of the signal components. This combination allows for a precise estimation and filtering of the ground-roll noise. The frequency values are used for determining, in each component, the low-frequency parts associated with the ground roll. For every single component, the ground roll is attenuated by zeroing, and stacking the data components, where the average instantaneous frequency values match the ground-roll bandwidth of frequency. Also, in order to enhance the lateral coherence of the reflectors, we present an extension of the recursive-iterative algorithm for a multichannel case. The multichannel algorithm is applicable on a shot, or common mid-point family of seismic traces, after the normal moveout correction. The numerical results using real data show the effectiveness of the proposed methodology for ground-roll attenuation and for improving the velocity analysis.

Seismic ground roll attenuation in DWT domain by split Bregman iteration algorithm

Noise reduction of seismic data is one of the most substantial steps in seismic signal processing. Goudarzi and Riahi presented wavelet domain ground roll analysis (WDGA), a technique based on the data. The results of this method showed a good improvement of the signal-to-noise ratio, but the WDGA did not work well in the data with a high level of random noise because this method uses a comparison-based search algorithm. This problem requires additional research to find a way to overcome this limitation. The wavelet domain split Bregman (WDSB) method presented in this paper is based on the use of the split Bregman algorithm in a discrete wavelet domain. The goal is to simultaneously use the capabilities of the F-K filter and the split Bregman iteration algorithm in the discrete wavelet domain, to overcome the problem of high levels of random noise in ground roll attenuation. The results of using WDSB for synthetic and real seismic data show improvement in ground roll attenuation while protecting seismic signals. The new algorithm is studied in time and wavelet domains.