Ground roll: Rejection using adaptive phase‐matched filters

Geophysics ◽  
1990 ◽  
Vol 55 (6) ◽  
pp. 776-781 ◽  
Author(s):  
R. B. Herrmann ◽  
D. R. Russell
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Iterative Process ◽  
Spatial Sampling ◽  
Matched Filtering ◽  
Data Sets ◽  
Matched Filters ◽  
Wide Range ◽  
Ground Roll ◽  
Band Pass

The technique of phase‐matched filtering dispersive surface waves is extended to permit an adaptive, iterative process by which the signal itself in a seismic trace designs a filter to remove the surface wave. The technique is robust and well‐behaved and requires the specification of only simple parameters for its operation. The technique is applied to data sets from three regions, representing a wide range in the ratio of surface‐wave noise to exploration signal. The technique works very well with poor data sets and also improves good data sets. Since the technique is applied to individual traces, it works in situations for which f‐k filtering might not be feasible due to poor spatial sampling. The technique is computationally more intensive than recursive digital band‐pass filtering of individual traces, but is less intensive than filtering in the f‐k domain.

Surface wave attenuation of seismic records with the co-core trace transform filter

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V115-V128 ◽  
Author(s):  
Ning Wu ◽  
Yue Li ◽  
Baojun Yang
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Field Data ◽  
Wave Attenuation ◽  
Seismic Events ◽  
Data Sets ◽  
Transform Domain ◽  
Recent Developments ◽  
Seismic Records ◽  
Trace Transform

To remove surface waves from seismic records while preserving other seismic events of interest, we introduced a transform and a filter based on recent developments in image processing. The transform can be seen as a weighted Radon transform, in particular along linear trajectories. The weights in the transform are data dependent and designed to introduce large amplitude differences between surface waves and other events such that surface waves could be separated by a simple amplitude threshold. This is a key property of the filter and distinguishes this approach from others, such as conventional ones that use information on moveout ranges to apply a mask in the transform domain. Initial experiments with synthetic records and field data have demonstrated that, with the appropriate parameters, the proposed trace transform filter performs better both in terms of surface wave attenuation and reflected signal preservation than the conventional methods. Further experiments on larger data sets are needed to fully assess the method.

THREE‐DIMENSIONAL SEISMIC MODEL STUDIES

Geophysics ◽  
1955 ◽  
Vol 20 (1) ◽  
pp. 19-32 ◽  
Author(s):  
F. K. Levin ◽  
H. C. Hibbard
Keyword(s):  
Wave Propagation ◽  
Surface Wave ◽  
Elastic Wave ◽  
Surface Waves ◽  
Shear Waves ◽  
Three Dimensional ◽  
Seismic Model ◽  
Model Studies ◽  
Ground Roll ◽  
Detector Configurations

Elastic wave propagation in a two‐layer section has been studied with a solid two‐bed model and records resembling seismograms obtained for the four possible source‐detector configurations. Numerous events are identified. Among these, the shear waves are found to be surprisingly prominent. The amplitude of the ground roll falls off approximately as [Formula: see text] This is the amplitude‐range dependence expected for a surface wave. The ability of two in‐line detectors to reduce surface waves has been demonstrated.

Multi-Window Weighted Stacking of Surface-Wave Dispersion

Geophysics ◽  
2020 ◽  
pp. 1-53
Author(s):  
Sylvain Pasquet ◽  
Wei Wang ◽  
Po Chen ◽  
Brady A. Flinchum
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Real Data ◽  
Wave Dispersion ◽  
Lateral Resolution ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Shallow Subsurface ◽  
Depth Of Investigation

Surface wave (SW) methods are classically used to characterize shear (S-) wave velocities ( VS) of the shallow subsurface through the inversion of dispersion curves. When targeting 2D shallow structures with sharp lateral heterogeneity, windowing and stacking techniques can be implemented to provide a better description of VS lateral variations. These techniques, however, suffer from the trade-off between lateral resolution and depth of investigation, well-known when using multichannel analysis of surface waves (MASW). We propose a novel methodology aimed at enhancing both lateral resolution and depth of investigation of MASW results through the use of multi-window weighted stacking of surface waves (MW-WSSW). MW-WSSW consists in stacking dispersion images obtained from data segments of different sizes, with a wavelength-based weight that depends on the aperture of the data selection window. In that sense, MW-WSSW provides additional weight to short wavelengths in smaller windows so as to better inform shallow parts of the subsurface, and vice versa for deeper velocities. Using multiple windows improves the depth of investigation, while applying wavelength-based weights enhances shallow lateral resolution. MW-WSSW was implemented within the open-source package SWIP, and applied to the processing of synthetic and real data sets. In both cases we compared it with standard windowing and stacking procedures that are already implemented in SWIP. MW-WSSW provided convincing results with optimized lateral extent, improved shallow resolution, and increased depth of investigation.

Surface-wave methods for anomaly detection

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. EN29-EN42 ◽  
Author(s):  
J. Tyler Schwenk ◽  
Steven D. Sloan ◽  
Julian Ivanov ◽  
Richard D. Miller
Keyword(s):  
Surface Wave ◽  
Anomaly Detection ◽  
Rayleigh Wave ◽  
Surface Waves ◽  
3D Visualization ◽  
Velocity Estimation ◽  
Instantaneous Amplitude ◽  
Amplitude Analysis ◽  
Attribute Analysis ◽  
Wide Range

Perimeter-defense operations, geohazard assessment, and engineering characterization require the detection and localization of subsurface anomalies. Seismic waves incident upon these discontinuities generate a scattered wavefield. We have developed various surface-wave techniques, currently being fielded, that have consistently delivered accurate and precise results across a wide range of survey parameters and geographical locations. We use the multichannel analysis of surface waves approach to study the multimode Rayleigh wave, the backscatter analysis of surface waves (BASW) method to detect anomalies, 3D visualization for efficient seismic interpretation, BASW correlation for attribute analysis, and instantaneous-amplitude integration in the complex BASW method. Discrete linear moveout functions and [Formula: see text]-[Formula: see text] filter designs are optimized for BASW considering the fundamental and higher mode dispersion trends of the Rayleigh wave. Synthetic and field data were used to demonstrate multimode BASW and mode separation, which accentuated individual scatter events, and ultimately increased confidence in points of interest. Simple correlation algorithms between fundamental and higher-mode BASW data offer attribute analysis that limits the subjective interpretation of BASW images. Domain sorting and Hilbert transforms allow for 3D visualization and rapid interpretation of an anomaly’s wavefield phenomena within an amplitude cube. Furthermore, instantaneous-amplitude analysis can be incorporated into a more robust complex BASW method that forgives velocity-estimation inaccuracies, while requiring less rigorous preprocessing. Our investigations have suggested that a multifaceted surface-wave analysis provides a valuable tool for today’s geophysicists to fulfill anomaly-detection survey requirements.

Multichannel analysis of surface waves

Geophysics ◽  
1999 ◽  
Vol 64 (3) ◽  
pp. 800-808 ◽  
Author(s):  
Choon B. Park ◽  
Richard D. Miller ◽  
Jianghai Xia
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Dispersion Curve ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Frequency Component ◽  
Multichannel Recording ◽  
Phase Velocities ◽  
Ground Roll

The frequency‐dependent properties of Rayleigh‐type surface waves can be utilized for imaging and characterizing the shallow subsurface. Most surface‐wave analysis relies on the accurate calculation of phase velocities for the horizontally traveling fundamental‐mode Rayleigh wave acquired by stepping out a pair of receivers at intervals based on calculated ground roll wavelengths. Interference by coherent source‐generated noise inhibits the reliability of shear‐wave velocities determined through inversion of the whole wave field. Among these nonplanar, nonfundamental‐mode Rayleigh waves (noise) are body waves, scattered and nonsource‐generated surface waves, and higher‐mode surface waves. The degree to which each of these types of noise contaminates the dispersion curve and, ultimately, the inverted shear‐wave velocity profile is dependent on frequency as well as distance from the source. Multichannel recording permits effective identification and isolation of noise according to distinctive trace‐to‐trace coherency in arrival time and amplitude. An added advantage is the speed and redundancy of the measurement process. Decomposition of a multichannel record into a time variable‐frequency format, similar to an uncorrelated Vibroseis record, permits analysis and display of each frequency component in a unique and continuous format. Coherent noise contamination can then be examined and its effects appraised in both frequency and offset space. Separation of frequency components permits real‐time maximization of the S/N ratio during acquisition and subsequent processing steps. Linear separation of each ground roll frequency component allows calculation of phase velocities by simply measuring the linear slope of each frequency component. Breaks in coherent surface‐wave arrivals, observable on the decomposed record, can be compensated for during acquisition and processing. Multichannel recording permits single‐measurement surveying of a broad depth range, high levels of redundancy with a single field configuration, and the ability to adjust the offset, effectively reducing random or nonlinear noise introduced during recording. A multichannel shot gather decomposed into a swept‐frequency record allows the fast generation of an accurate dispersion curve. The accuracy of dispersion curves determined using this method is proven through field comparisons of the inverted shear‐wave velocity ([Formula: see text]) profile with a downhole [Formula: see text] profile.

scholarly journals Interferometric ground-roll removal: Attenuation of scattered surface waves in single-sensor data

Geophysics ◽  
2010 ◽  
Vol 75 (2) ◽  
pp. SA15-SA25 ◽  
Author(s):  
David F. Halliday ◽  
Andrew Curtis ◽  
Peter Vermeer ◽  
Claudio Strobbia ◽  
Anna Glushchenko ◽  
...  
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Seismic Data ◽  
Sensor Data ◽  
Seismic Survey ◽  
Seismic Interferometry ◽  
Near Surface ◽  
Reflection Data ◽  
Ground Roll ◽  
Single Sensor

Land seismic data are contaminated by surface waves (or ground roll). These surface waves are a form of source-generated noise and can be strongly scattered by near-surface heterogeneities. The resulting scattered ground roll can be particularly difficult to separate from the desired reflection data, especially when this scattered ground roll propagates in the crossline direction. We have used seismic interferometry to estimate scattered surface waves, recorded during an exploration seismic survey, between pairs of receiver locations. Where sources and receivers coincide, these interreceiver surface-wave estimates were adaptively subtracted from the data. This predictive-subtraction process can successfully attenuate scattered surface waves while preserving the valuable reflected arrivals, forming a new method of scattered ground-roll attenuation. We refer to this as interferometric ground-roll removal.

scholarly journals Interferometric surface-wave isolation and removal

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. A69-A73 ◽  
Author(s):  
David F. Halliday ◽  
Andrew Curtis ◽  
Johan O. A. Robertsson ◽  
Dirk-Jan van Manen
Keyword(s):  
Surface Wave ◽  
Data Quality ◽  
Surface Waves ◽  
Survey Data ◽  
Seismic Data ◽  
Body Wave ◽  
Seismic Survey ◽  
Seismic Interferometry ◽  
Wave Component ◽  
Ground Roll

The removal of surface waves (ground roll) from land seismic data is critical in seismic processing because these waves tend to mask informative body-wave arrivals. Removal becomes difficult when surface waves are scattered, and data quality is often impaired. We apply a method of seismic interferometry, using both sources and receivers at the surface, to estimate the surface-wave component of the Green’s function between any two points. These estimates are subtracted adaptively from seismic survey data, providing a new method of ground-roll removal that is not limited to nonscattering regions.

Application of constrained polarization filtering for surface-wave mitigation: Three case studies

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. V169-V181 ◽  
Author(s):  
Mamadou S. Diallo ◽  
Warren S. Ross ◽  
Andrew P. Shatilo ◽  
Inmaculada Dura-Gomez ◽  
Gary C. Szurek
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Case Studies ◽  
Data Sets ◽  
Velocity Amplitude ◽  
Time Frequency ◽  
Frequency Information ◽  
Straightforward Application ◽  
F Region ◽  
Polarization Filtering

We applied constrained polarization filtering (CPF) to surface-wave mitigation on data sets from different geologic settings. The method derives from the application of polarization filtering in the time-frequency (t-f) domain and introduces new constraints to effectively detect and mitigate surface waves while protecting the signal. We use these constraints that we derive from velocity, amplitude, time, and frequency information to delineate the t-f region dominated by surface-wave noises. Then, we restrict the application of polarization filtering to this region to avoid damaging the signal. Straightforward application of polarization filtering without these constraints results in ineffective filtering or damage to the signal, due to the complexity of surface-wave wavetrains. The performance of CPF with these various data sets is demonstrably superior compared to the unconstrained approach. There are some of the issues that may affect performance of the CPF, but they can be overcome.

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

Geophysics ◽  
2021 ◽  
pp. 1-22
Author(s):  
Aleksander S. Serdyukov
Keyword(s):  
Surface Wave ◽  
Data Processing ◽  
Surface Waves ◽  
Frequency Domain ◽  
Band Frequency ◽  
Time Frequency ◽  
S Transform ◽  
Ground Roll ◽  
Karhunen Loeve Transform ◽  
Complex Valued

Ground roll suppression is critical for seismic reflection data processing. Many standard methods, i.e., FK 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 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, the proposed S-transform Karhunen-Loeve (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.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data
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