Imaging and suppressing near-receiver scattered surface waves

Geophysics ◽  
2005 ◽  
Vol 70 (2) ◽  
pp. V21-V29 ◽  
Author(s):  
Xander H. Campman ◽  
Kasper van Wijk ◽  
John A. Scales ◽  
Gérard C. Herman
Keyword(s):  
Surface Waves ◽  
Single Channel ◽  
Laboratory Data ◽  
Body Waves ◽  
Surface Model ◽  
Imaging Method ◽  
Sampled Data ◽  
Impedance Function ◽  
Near Surface ◽  
Integral Equation Formulation

When traveling through a complex overburden, upcoming seismic body waves can be disturbed by scattering from local heterogeneities. Currently, surface-consistent static and amplitude corrections correct for rapid variations in arrival times and amplitudes of a reflector, but these methods impose strong assumptions on the near-surface model. Observations on synthetic and laboratory experiments of near-surface scattering with densely sampled data suggest that removing noise from near-receiver scattering requires multichannel approaches rather than single-channel, near-surface corrections. In this paper we develop a wavefield-based imaging method to suppress surface waves scattered directly beneath the receivers. Using an integral-equation formulation, we account for near-surface heterogeneities by a surface impedance function. This impedance function is used to model scattered surface waves, excited by upcoming wavefronts. The final step in our algorithm is to subtract the scattered surface waves. We successfully apply this method to laboratory data of scattered surface waves, excited and monitored with a noncontacting acquisition system.

Tracking of velocity variations at depth in the presence of surface velocity fluctuations

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. U1-U8 ◽  
Author(s):  
Benoit de Cacqueray ◽  
Philippe Roux ◽  
Michel Campillo ◽  
Stefan Catheline
Keyword(s):  
Surface Waves ◽  
Body Waves ◽  
Surface Velocity ◽  
Small Scale ◽  
Beam Forming ◽  
Near Surface ◽  
Velocity Fluctuations ◽  
Wave Separation ◽  
Double Beam ◽  
Velocity Variations

We tested a small-scale experiment that is dedicated to the study of the wave separation algorithm and to the velocity variations monitoring problem itself. It handles the case in which velocity variations at depth are hidden by near-surface velocity fluctuations. Using an acquisition system that combines an array of sources and an array of receivers, coupled with controlled velocity variations, we tested the ability of beam-forming techniques to track velocity variations separately for body waves and surface waves. After wave separation through double beam forming, the arrival time variations of the different waves were measured through the phase difference between the extracted wavelets. Finally, a method was tested to estimate near-surface velocity variations using surface waves or shallow reflection and compute a correction to isolate target velocity variations at depth.

scholarly journals Numerical modeling of elastic-wave scattering by near-surface heterogeneities

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. T199-T217 ◽  
Author(s):  
Abdulaziz M. Almuhaidib ◽  
M. Nafi Toksöz
Keyword(s):  
Surface Waves ◽  
Wave Scattering ◽  
Attenuation Factor ◽  
Surface Characteristics ◽  
Body Waves ◽  
Incident Wavelength ◽  
Near Surface ◽  
Seismic Image ◽  
Weak Surface

In land seismic data, scattering from surface and near-surface heterogeneities adds complexity to the recorded signal and masks weak primary reflections. To understand the effects of near-surface heterogeneities on seismic reflections, we simulated seismic-wave scattering from arbitrary-shaped, shallow, subsurface heterogeneities through the use of a perturbation method for elastic waves and finite-difference forward modeling. The near-surface scattered wavefield was modeled by looking at the difference between the calculated incident (i.e., in the absence of scatterers) and the total wavefields. Wave propagation was simulated for several earth models with different near-surface characteristics to isolate and quantify the influence of scattering on the quality of the seismic signal. The results indicated that the direct surface waves and the upgoing reflections were scattered by the near-surface heterogeneities. The scattering took place from body waves to surface waves and from surface waves to body waves. The scattered waves consisted mostly of body waves scattered to surface waves and were, generally, as large as, or larger than, the reflections. They often obscured weak primary reflections and could severely degrade the image quality. The results indicated that the scattered energy depended strongly on the properties of the shallow scatterers and increased with increasing impedance contrast, increasing size of the scatterers relative to the incident wavelength, decreasing depth of the scatterers, and increasing attenuation factor of the background medium. Also, sources deployed at depth generated weak surface waves, whereas deep receivers recorded weak surface and scattered body-to-surface waves. The analysis and quantified results helped in the understanding of the scattering mechanisms and, therefore, could lead to developing new acquisition and processing techniques to reduce the scattered surface wave and enhance the quality of the seismic image.

Multiwave inversion: A key step for depth model building — Examples from the Sultanate of Oman

2021 ◽  
Vol 40 (8) ◽  
pp. 610-618
Author(s):  
Daniela Donno ◽  
Mohammad Sheryar Farooqui ◽  
Mostafa Khalil ◽  
David McCarthy ◽  
Didier Solyga ◽  
...  
Keyword(s):  
Surface Waves ◽  
Model Building ◽  
Sand Dunes ◽  
P Wave ◽  
Surface Model ◽  
Sultanate Of Oman ◽  
Surface Wave Dispersion ◽  
Low Frequencies ◽  
Near Surface ◽  
Velocity Models

The near surface in the Middle East, particularly in the Sultanate of Oman, is characterized by very shallow high-velocity carbonates and anhydrites interleaved by slow-velocity clastic layers, resulting in sharp velocity inversions in the first few hundred meters below the surface. In addition, the surface is characterized by features such as unconsolidated materials within dry riverbeds (known as “wadis”), small jebels, and sand dunes, which cause distortions in the underlying shallow and deeper seismic images. This work presents the building of a near-surface model by using multiwave inversion that jointly inverts information from P-wave first breaks and surface-wave dispersion curves. The use of surface waves in combination with first breaks captures the lateral and vertical velocity variations, especially in the shallowest parts of the near surface. This paper focuses on the analysis of two drawbacks of this technology: the picking of the input data information, which can be cumbersome and time consuming, and the limited penetration depth of surface waves at the typical frequencies of active data. To overcome these issues, an innovative workflow is proposed that combines the use of an unsupervised machine learning technique to guide the pick extraction phase and the reconstruction of ultra-low-frequency surface waves (0.5 to 1.5 Hz) through an interferometry process using information from natural and ambient noise. Deeper near-surface P- and S-wave velocity models can be obtained with multiwave inversion using these ultra-low frequencies. The integration of a near-surface model into the velocity model building workflow brings a major improvement in depth imaging from shallow to deep structures, as demonstrated on two data sets from the Sultanate of Oman.

scholarly journals Seismic Characterization of the Nevada National Security Site Using Joint Body Wave, Surface Wave, and Gravity Inversion

2019 ◽  
Vol 110 (1) ◽  
pp. 110-126
Author(s):  
Leiph Preston ◽  
Christian Poppeliers ◽  
David J. Schodt
Keyword(s):  
Surface Wave ◽  
National Security ◽  
Surface Waves ◽  
Body Wave ◽  
Wave Dispersion ◽  
The Body ◽  
Body Waves ◽  
Gravity Inversion ◽  
Surface Wave Dispersion ◽  
Near Surface

ABSTRACT As a part of the series of Source Physics Experiments (SPE) conducted on the Nevada National Security Site in southern Nevada, we have developed a local-to-regional scale seismic velocity model of the site and surrounding area. Accurate earth models are critical for modeling sources like the SPE to investigate the role of earth structure on the propagation and scattering of seismic waves. We combine seismic body waves, surface waves, and gravity data in a joint inversion procedure to solve for the optimal 3D seismic compressional and shear-wave velocity structures and earthquake locations subject to model smoothness constraints. Earthquakes, which are relocated as part of the inversion, provide P- and S-body-wave absolute and differential travel times. Active source experiments in the region augment this dataset with P-body-wave absolute times and surface-wave dispersion data. Dense ground-based gravity observations and surface-wave dispersion derived from ambient noise in the region fill in many areas where body-wave data are sparse. In general, the top 1–2 km of the surface is relatively poorly sampled by the body waves alone. However, the addition of gravity and surface waves to the body-wave dataset greatly enhances structural resolvability in the near surface. We discuss the methodology we developed for simultaneous inversion of these disparate data types and briefly describe results of the inversion in the context of previous work in the region.

Shallow tunnel detection using converted surface waves

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Shelby L. Peterie ◽  
Julian Ivanov ◽  
Erik Knippel ◽  
Richard D. Miller ◽  
Steven D. Sloan
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Body Wave ◽  
Seismic Source ◽  
Body Waves ◽  
Detection Methods ◽  
Imaging Method ◽  
Real World Data ◽  
Processing Methods ◽  
Novel Processing

Seismic surface waves that were likely converted from incident body waves were used to detect a 3 m deep tunnel using two novel processing methods. In data acquired at a tunnel test site, a unique forward-propagating wave (travelling away from both the tunnel and seismic source) was identified as an early-arriving surface wave converted at the tunnel from an incident body wave. To our knowledge, this paper represents the first time converted surface waves have been documented as originating from a tunnel. We developed two novel processing methods targeting this unique wavefield component for detecting tunnels, cavities, or other shallow anomalies. The first is a time-domain imaging method that takes advantage of the unique kinematic characteristics of converted surface waves to produce a cross-section with a coherent, high-amplitude signature originating from the horizontal location of the tunnel. The second method uses frequency-domain analysis of surface-wave amplitudes, which reveals increased amplitudes (primarily from converted surface waves) at locations expected for the tunnel. These proposed approaches for analysis of converted surface waves were successfully used to detect the tunnel and accurately interpret its horizontal location in real world data. These novel methods could be key for detecting shallow tunnels or other subsurface anomalies and complement existing seismic detection methods.

scholarly journals Ray-map migration of transmitted surface waves

2016 ◽  
Vol 4 (4) ◽  
pp. SQ33-SQ40 ◽  
Author(s):  
Jing Li ◽  
Gerard T. Schuster
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Fault Plane ◽  
The Other ◽  
Normal Faults ◽  
Imaging Method ◽  
Near Surface ◽  
Surface Normal ◽  
Surface Projection ◽  
Map Migration

Near-surface normal faults can sometimes separate two distinct zones of velocity heterogeneity, where the medium on one side of the fault has a faster velocity than on the other side. Therefore, the slope of surface-wave arrivals in a common-shot gather should abruptly change near the surface projection of the fault. We present ray-map imaging method that migrates transmitted surface waves to the fault plane, and therefore it roughly estimates the orientation, depth, and location of the near-surface fault. The main benefits of this method are that it is computationally inexpensive and robust in the presence of noise.

DISPERSION IN SEISMIC SURFACE WAVES

Geophysics ◽  
1951 ◽  
Vol 16 (1) ◽  
pp. 63-80 ◽  
Author(s):  
Milton B. Dobrin
Keyword(s):  
Surface Waves ◽  
Water Waves ◽  
Seismic Refraction ◽  
Wave Theory ◽  
Wave Dispersion ◽  
Surface Zone ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Arrival Times ◽  
Ground Roll

A non‐mathematical summary is presented of the published theories and observations on dispersion, i.e., variation of velocity with frequency, in surface waves from earthquakes and in waterborne waves from shallow‐water explosions. Two further instances are cited in which dispersion theory has been used in analyzing seismic data. In the seismic refraction survey of Bikini Atoll, information on the first 400 feet of sediments below the lagoon bottom could not be obtained from ground wave first arrival times because shot‐detector distances were too great. Dispersion in the water waves, however, gave data on speed variations in the bottom sediments which made possible inferences on the recent geological history of the atoll. Recent systematic observations on ground roll from explosions in shot holes have shown dispersion in the surface waves which is similar in many ways to that observed in Rayleigh waves from distant earthquakes. Classical wave theory attributes Rayleigh wave dispersion to the modification of the waves by a surface layer. In the case of earthquakes, this layer is the earth’s crust. In the case of waves from shot‐holes, it is the low‐speed weathered zone. A comparison of observed ground roll dispersion with theory shows qualitative agreement, but it brings out discrepancies attributable to the fact that neither the theory for liquids nor for conventional solids applies exactly to unconsolidated near‐surface rocks. Additional experimental and theoretical study of this type of surface wave dispersion may provide useful information on the properties of the surface zone and add to our knowledge of the mechanism by which ground roll is generated in seismic shooting.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

Computer Application in Geosciences: Imaging of the Subsurface by Structural Coupled Inversion of Potential Field Data

2014 ◽  
Vol 644-650 ◽  
pp. 2670-2673
Author(s):  
Jun Wang ◽  
Xiao Hong Meng ◽  
Fang Li ◽  
Jun Jie Zhou
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Computer Application ◽  
Magnetic Data ◽  
Imaging Method ◽  
Inversion Algorithm ◽  
Constant Property ◽  
Near Surface ◽  
Potential Field Data

With the continuing growth in influence of near surface geophysics, the research of the subsurface structure is of great significance. Geophysical imaging is one of the efficient computer tools that can be applied. This paper utilize the inversion of potential field data to do the subsurface imaging. Here, gravity data and magnetic data are inverted together with structural coupled inversion algorithm. The subspace (model space) is divided into a set of rectangular cells by an orthogonal 2D mesh and assume a constant property (density and magnetic susceptibility) value within each cell. The inversion matrix equation is solved as an unconstrained optimization problem with conjugate gradient method (CG). This imaging method is applied to synthetic data for typical models of gravity and magnetic anomalies and is tested on field data.

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