Stacking of surface waves

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V51-V58 ◽  
Author(s):  
Boriszláv Neducza
Keyword(s):  
Data Quality ◽  
Surface Waves ◽  
Signal To Noise Ratio ◽  
Dispersion Curves ◽  
Horizontal Resolution ◽  
Coherent Noise ◽  
Data Set ◽  
Near Surface ◽  
Advantages And Disadvantages

The seismic surface wave method (SWM) is a powerful means of characterizing near-surface structures. Although the SWM consists of only three steps (data acquisition, determination of dispersion curves, and inversion), it is important to take considerable care with the second step, determination of the dispersion curves. This step is usually completed by spectral analysis of surface waves (SASW) or multichannel analysis of surface waves (MASW). However, neither method is ideal, as each has its advantages and disadvantages. SASW provides higher horizontal resolution, but it is very sensitive to coherent noise and individual geophone coupling. MASW is a robust method able to separate different wave types, but its horizontal resolution is lower. Stacking of surface waves (SSW) is a good compromise between SASW and MASW. Using a reduced number of traces increases the horizontal resolution of MASW, and utilizing other shot records with the same receivers compensates for the decreased signal-to-noise ratio. The stacking is realized by summing the [Formula: see text] amplitude spectra of windowed shot records, where windowing produces higher horizontal resolution and stacking produces improved data quality. Mixing is applied between the stacks derived with different parameters, as different frequency ranges require different windowing. SSW was tested and corroborated on a deep seismic data set. Horizontal resolution is validated by [Formula: see text] plots at different frequencies, and [Formula: see text] plots present data quality.

Refraction wavefield migration

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. Q27-Q37
Author(s):  
Yang Shen ◽  
Jie Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Surface Velocity ◽  
Data Set ◽  
Near Surface ◽  
Depth Migration ◽  
Reflection Data ◽  
Imaging Tool ◽  
Imaging Results ◽  
Migration Method ◽  
Single Method

Refraction methods are often applied to model and image near-surface velocity structures. However, near-surface imaging is very challenging, and no single method can resolve all of the land seismic problems across the world. In addition, deep interfaces are difficult to image from land reflection data due to the associated low signal-to-noise ratio. Following previous research, we have developed a refraction wavefield migration method for imaging shallow and deep interfaces via interferometry. Our method includes two steps: converting refractions into virtual reflection gathers and then applying a prestack depth migration method to produce interface images from the virtual reflection gathers. With a regular recording offset of approximately 3 km, this approach produces an image of a shallow interface within the top 1 km. If the recording offset is very long, the refractions may follow a deep path, and the result may reveal a deep interface. We determine several factors that affect the imaging results using synthetics. We also apply the novel method to one data set with regular recording offsets and another with far offsets; both cases produce sharp images, which are further verified by conventional reflection imaging. This method can be applied as a promising imaging tool when handling practical cases involving data with excessively weak or missing reflections but available refractions.

Two robust imaging methodologies for challenging environments: Wave-equation dispersion inversion of surface waves and guided waves and supervirtual interferometry + tomography for far-offset refractions

2018 ◽  
Vol 6 (4) ◽  
pp. SM27-SM37 ◽  
Author(s):  
Jing Li ◽  
Kai Lu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Wave Equation ◽  
Velocity Distribution ◽  
Surface Waves ◽  
Guided Waves ◽  
Velocity Model ◽  
Dispersion Curves ◽  
P Wave ◽  
Head Wave ◽  
Near Surface ◽  
Velocity Models

Two robust imaging technologies are reviewed that provide subsurface geologic information in challenging environments. The first one is wave-equation dispersion (WD) inversion of surface waves and guided waves (GW) for the shear-velocity (S-wave) and compressional-velocity (P-wave) models, respectively. The other method is traveltime inversion for the velocity model, in which supervirtual refraction interferometry (SVI) is used to enhance the signal-to-noise ratio of far-offset refractions. We have determined the benefits and liabilities of both methods with synthetic seismograms and field data. The benefits of WD are that (1) there is no layered-medium assumption, as there is in conventional inversion of dispersion curves. This means that 2D or 3D velocity models can be accurately estimated from data recorded by seismic surveys over rugged topography, and (2) WD mostly avoids getting stuck in local minima. The liability is that WD for surface waves is almost as expensive as full-waveform inversion (FWI) and, for Rayleigh waves, only recovers the S-velocity distribution to a depth no deeper than approximately 1/2 to 1/3 wavelength of the lowest-frequency surface wave. The limitation for GW is that, for now, it can estimate the P-velocity model by inverting the dispersion curves from GW propagating in near-surface low-velocity zones. Also, WD often requires user intervention to pick reliable dispersion curves. For SVI, the offset of usable refractions can be more than doubled, so that traveltime tomography can be used to estimate a much deeper model of the P-velocity distribution. This can provide a more effective starting velocity model for FWI. The liability is that SVI assumes head-wave first arrivals, not those from strong diving waves.

Reconnaissance 3-D Seismic as a Modern Exploration Tool

1988 ◽  
Vol 6 (2) ◽  
pp. 118-125
Author(s):  
D.J. Norris
Keyword(s):  
Data Quality ◽  
Accurate Determination ◽  
Considerable Improvement ◽  
Critical Parameters ◽  
Near Surface ◽  
Line Spacing ◽  
Line Separation ◽  
Exploration Tool ◽  
Reliable Interpretation

TCPL has recently carried out ‘reconnaissance or exploration’ 3-D surveys, in three different blocks, each designed to solve a different type of problem. In each case a considerable improvement in data quality and the resulting structural/stratigraphic interpretation was achieved. The Kupe South structure is a wrench-induced feature cross-cut by numerous small-medium faults. Stratigraphic changes across the prospect produce a variable quality seismic event at the top reservoir level. The Pataka Prospect comprises a narrow horst block trend within the Oakura fault zone, offshore New Plymouth. Accurate determination of potential reserves required a reliable interpretation of the fault configuration, and the amount of displacement of the reservoir horizon by the critical faults. The Waitara Prospect is affected by a ‘no-data’ zone possibly associated with volcanics in the near surface. It was necessary to define the extent and nature of the no-data zone and to calculate the effect of the interpreted volcanics on the time structure map. Prior to the surveys we modelled the effects of such critical parameters as sail-line separation, final interpolation spacing and the dimensions of the 3-D grids using existing 2-D data. Good results were obtained with a wider line spacing than is strictly required for true 3-D. Whilst the Reconnaissance 3-D method has not removed all of the difficulties with interpretation, a considerable improvement was obtained in data quality and ease of interpretation.

Random objective waveform inversion of surface waves

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. EN49-EN61
Author(s):  
Yudi Pan ◽  
Lingli Gao
Keyword(s):  
Objective Function ◽  
Surface Waves ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Initial Model ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Synthetic Test ◽  
Ground Penetrating

Full-waveform inversion (FWI) of surface waves is becoming increasingly popular among shallow-seismic methods. Due to a huge amount of data and the high nonlinearity of the objective function, FWI usually requires heavy computational costs and may converge toward a local minimum. To mitigate these problems, we have reformulated FWI under a multiobjective framework and adopted a random objective waveform inversion (ROWI) method for surface-wave characterization. Three different measure functions were used, whereas the combination of one measure function with one shot independently provided one of the [Formula: see text] objective functions ([Formula: see text] is the total number of shots). We have randomly chose and optimized one objective function at each iteration. We performed a synthetic test to compare the performance of the ROWI and conventional FWI approaches, which showed that the convergence of ROWI is faster and more robust compared with conventional FWI approaches. We also applied ROWI to a field data set acquired in Rheinstetten, Germany. ROWI successfully reconstructed the main geologic feature, a refilled trench, in the final result. The comparison between the ROWI result and a migrated ground-penetrating radar profile further proved the effectiveness of ROWI in reconstructing the near-surface S-wave velocity model. We also ran the same field example by using a poor initial model. In this case, conventional FWI failed whereas ROWI still reconstructed the subsurface model to a fairly good level, which highlighted the relatively low dependency of ROWI on the initial model.

Geophone array formation and semblance evaluation

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. Q1-Q8 ◽  
Author(s):  
Mitchell S. Craig ◽  
Ronald L. Genter
Keyword(s):  
Data Quality ◽  
Surface Waves ◽  
Seismic Data ◽  
Permian Basin ◽  
Trapped Waves ◽  
Data Set ◽  
Array Design ◽  
West Texas ◽  
Scattered Energy ◽  
Array Performance

The performance of a variety of areal geophone arrays was evaluated using seismic data recorded on a dense receiver grid in a walkaway survey conducted in the Permian Basin of west Texas. Surface waves, trapped waves, and scattered energy have long been recognized as a significant noise problem in this area. Arrays were formed by extracting sets of traces from the main data set and stacking them to produce individual traces of a receiver gather. We calculated semblance of each receiver gather to evaluate array performance. High values of semblance indicate that an array effectively removes surface waves while preserving reflections. Differences in data quality associated with variations in geophone-array design are often subtle and difficult to discern through simple inspection of field records. By calculating frequency-dependent semblance, we were able to detect and quantify differences in array performance.

Predictive removal of scattered noise

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Gérard C. Herman ◽  
Colin Perkins
Keyword(s):  
Mathematical Model ◽  
Wave Propagation ◽  
Surface Wave ◽  
Seismic Data ◽  
Coherent Noise ◽  
Data Set ◽  
Near Surface ◽  
Unique Data ◽  
Surface Wave Propagation ◽  
Petroleum Development

Land seismic data can be severely contaminated with coherent noise. We discuss a deterministic technique to predict and remove scattered coherent noise from land seismic data based on a mathematical model of near-surface wave propagation. We test the method on a unique data set recorded by Petroleum Development of Oman in the Qarn Alam area (with shots and receivers on the same grid), and we conclude that it effectively reduces scattered noise without smearing reflection energy.

Full-waveform matching of VP and VS models from surface waves

2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Real Data ◽  
Test Site ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface ◽  
Velocity Models

SUMMARY The analysis of surface wave dispersion curves (DCs) is widely used for near-surface S-wave velocity (VS) reconstruction. However, a comprehensive characterization of the near-surface requires also the estimation of P-wave velocity (VP). We focus on the estimation of both VS and VP models from surface waves using a direct data transform approach. We estimate a relationship between the wavelength of the fundamental mode of surface waves and the investigation depth and we use it to directly transform the DCs into VS and VP models in laterally varying sites. We apply the workflow to a real data set acquired on a known test site. The accuracy of such reconstruction is validated by a waveform comparison between field data and synthetic data obtained by performing elastic numerical simulations on the estimated VP and VS models. The uncertainties on the estimated velocity models are also computed.

Biological crystals formation studied by HTREM and image analysis

1996 ◽  
Vol 54 ◽  
pp. 74-75
Author(s):  
P. Houlle ◽  
F.J.G. Cuisinier ◽  
J.C. Voegel ◽  
P. Schutz
Keyword(s):  
Image Analysis ◽  
Signal To Noise Ratio ◽  
High Resolution Electron Microscopy ◽  
Data Set ◽  
Simulation Techniques ◽  
Resolution Electron ◽  
Human Dentine ◽  
Simulated Images

High resolution electron microscopy (HREM) allows the determination of the molecular crystal structure by comparing HREM images with simulated images. Direct comparison was not possible for small crystalline areas such as nanometer-sized particles or for thin crystals with weak image contrast. To overcome these problems, we used numerical image analysis to gain access to the structure informations within these minute crystals. We used this approach for the characterization of the initial mineralization steps during human amelogenesis, chicken bone and human dentine crystals growth.Our method consists in HREM associated with both numerical image analysis and image simulation techniques (EMS) Image analysis was performed using the IMAGIC statistical. Nanoparticle subimages, 128 × 128 pixels in size, were extracted from the original micrographs by interactive selection. A circular mask with a radius of 50 pixels was applied. The mean intensity of each subimage was set to zero and its internal variance was normalized to 10. Double direct alignment procedures were used to align the images in rotation and translation against an alignment reference extracted from the data set. An average image was finally calculated to improve the signal to noise ratio.

scholarly journals Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S197-S205 ◽  
Author(s):  
Zhaolun Liu ◽  
Abdullah AlTheyab ◽  
Sherif M. Hanafy ◽  
Gerard Schuster
Keyword(s):  
Surface Waves ◽  
Field Data ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
3D Data ◽  
Strong Surface ◽  
Migration Method ◽  
Recorded Data

We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

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