Reduction of near‐surface scattering effects in seismic data

1998 ◽  
Vol 17 (6) ◽  
pp. 759-759 ◽  
Author(s):  
Fabian Ernst ◽  
Gerard Herman ◽  
Bastian Blonk
Keyword(s):  
Seismic Data ◽  
Surface Scattering ◽  
Near Surface ◽  
Scattering Effects

Removal of scattered guided waves from seismic data

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1240-1248 ◽  
Author(s):  
Fabian E. Ernst ◽  
Gérard C. Herman ◽  
Auke Ditzel
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Guided Waves ◽  
Wave Theory ◽  
Surface Scattering ◽  
Coherent Noise ◽  
Near Surface ◽  
Scattering Effects

Near‐surface scattered waves form a major source of coherent noise in seismic land data. Most current methods for removing these waves do not attenuate them adequately if they come from other than the inline direction. We present a wave‐theory‐based method for removing (scattered) guided waves by a prediction‐and‐removal algorithm. We assume that the near surface consists of a laterally varying medium, in which heterogeneities are embedded that act as scatterers. We first estimate the dispersive and laterally varying phase slowness field by applying a phase‐based tomography algorithm on the direct groundroll wave. Subsequently, the near‐surface heterogeneities are imaged using a least‐squares criterium. Finally, the scattered guided waves are modeled and subtracted adaptively from the seismic data. We have applied this method to seismic land data and found that near‐surface scattering effects are attenuated.

Inverse scattering of surface waves: A new look at surface Consistency

Geophysics ◽  
1994 ◽  
Vol 59 (6) ◽  
pp. 963-972 ◽  
Author(s):  
Bastian Blonk ◽  
Gérard C. Herman
Keyword(s):  
Inverse Scattering ◽  
Seismic Data ◽  
Scattering Theory ◽  
Synthetic Data ◽  
Surface Scattering ◽  
Frequency Filtering ◽  
Near Surface ◽  
Scattering Model ◽  
Static Corrections

A method is presented for eliminating near‐surface scattered noise from seismic data. Starting from an appropriately chosen background model, a surface‐consistent scattering model is determined using linearized elastodynamic inverse scattering theory. This scattering model does not necessarily equal the actual scatterer distribution, but it enables one to calculate, approximately, the near‐surface scattered part of the data. The method honors at least some of the complexity of the near‐surface scattering process and can be applied in cases where traditional methods, like wavenumber‐frequency filtering techniques and methods for static corrections, are ineffective. From a number of tests on synthetic data, we conclude that the method is rather robust; its main sensitivity is because of errors in the determination of the background Rayleigh‐wave velocity.

Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. U67-U76 ◽  
Author(s):  
Robert J. Ferguson
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Real Data ◽  
Structural Data ◽  
Irregular Sampling ◽  
Data Set ◽  
Near Surface ◽  
Damped Least Squares ◽  
Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

Passive near-surface seismic data where all else fails

2013 ◽  
Vol 32 (3) ◽  
pp. 308-314
Author(s):  
Phil Sirles ◽  
Jacob Sheehan ◽  
Nicole Pendrigh
Keyword(s):  
Seismic Data ◽  
Near Surface

Near Surface Research and Excitation Horizon Prediction Based on Old River Course of Xiaoqing River in Shan Dong

2013 ◽  
Vol 664 ◽  
pp. 94-98
Author(s):  
Guang De Zhang
Keyword(s):  
Seismic Data ◽  
Practical Significance ◽  
Cone Penetration ◽  
Surface Deposition ◽  
Near Surface ◽  
Sedimentary Characteristics ◽  
The Difference ◽  
Exploration Area ◽  
Data Requirements ◽  
Static Cone Penetration Test

Following deepened exploration and development in Shengli exploration area, seismic data requirements are also getting higher and higher. However, in recent years the difference of Xiaoqing river on both sides have made us know that the importance of this problem. In view of the above, this task is aimed at quaternary shallow of old river course within Xiaoqing River. Our analysis of lithology and sedimentary characteristics are using static cone penetration test and rock core exploration method, and we want to reappear near surface deposition of old river course within Xiaoqing River. The research is close combined with the exploration demand and theoretical study, so it has important theoretical and practical significance.

Physical properties and spatial distribution of the sea ice surface layer (SSL/snow) during the autumn phase of the MOSAiC expedition

2021 ◽  
Author(s):  
Ruzica Dadic ◽  
Martin Schneebeli ◽  
Henna-Reeta Hannula ◽  
Amy Macfarlane ◽  
Roberta Pirazzini
Keyword(s):  
Surface Layer ◽  
Spatial Distribution ◽  
Sea Ice ◽  
Physical Properties ◽  
Surface Scattering ◽  
Climate Feedback ◽  
Energy Fluxes ◽  
Scattering Layer ◽  
Near Surface ◽  
Ice Surface

<p>Snow cover dominates the thermal and optical properties of sea ice and the energy fluxes between the ocean and the atmosphere, yet data on the physical properties of snow and its effects on sea ice are limited. This lack of data leads to two significant problems: 1) significant biases in model representations of the sea ice cover and the processes that drive it, and 2) large uncertainties in how sea ice influences the global energy budget and the coupling of climate feedback. The  MOSAiC research initiative enabled the most extensive data collection of snow and surface scattering layer (SSL) properties over sea ice to date. During leg 5 of the MOSAiC expedition, we collected multi-scale (microscale to 100-m scale) measurements of the surface layer (snow/SSL) over first year ice (FYI) and MYI on a daily basis. The ultimate goal of our measurements is to determine the spatial distribution of physical properties of the surface layer. During leg 5 of the MOSAiC expedition, that surface layer changed from the  surface scattering layer (SSL),   characteristic for the melt season, to an early autumn snow pack. Here,  we will present data showing both a) the physical properties and the spatial distribution of the SSL during the late melt season and b) the transition of the sea ice surface from the SSL to the fresh autumn snowpack. The structural properties of this transition period are poorly documented, and this season is critical  for the initialization of sea ice and snow models. Furthermore, these data are crucial to interpret simultaneous observations of surface energy fluxes, surface optical and remote sensing data (microwave signals in particular), near-surface biochemical activity, and to understand the sea ice  processes that occur as the sea ice transitions from melting to freezing.</p>

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

2022 ◽  
Vol 41 (1) ◽  
pp. 40-46
Author(s):  
Öz Yilmaz ◽  
Kai Gao ◽  
Milos Delic ◽  
Jianghai Xia ◽  
Lianjie Huang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Surface Modeling ◽  
P Wave ◽  
Borehole Data ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Wave Inversion ◽  
Full Wave ◽  
Elastic Inversion

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

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