Introduction to this special section: Surface-wave applications

2021 ◽  
Vol 40 (8) ◽  
pp. 566-566
Author(s):  
Steve Sloan ◽  
Sarah L. Morton

The use of surface waves can be found at all levels of seismology, from the near surface to exploration to earthquakes. Once the scourge of reflection processing, the need to remove this coherent noise source led to a variety of in-field techniques to minimize its impact, including the application of geophone arrays, low-cut analog filters, and high-frequency geophones, not to mention the multitude of processing schemes devised to reduce surface-wave amplitudes after data collection. Although commonly labeled as noise, surface waves have proven to be versatile and resilient over the years, lending themselves to engineering applications in the shallow subsurface and developing velocity models to improve complex imaging in the oil and gas sector. This special section includes six papers that are equally representative, running the gamut from the first few meters of the surface to hundreds of meters deep. It includes examples of active and passive acquisition, modeling and field data, as well as geophones and fiber-optic sensors.

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Dmitry Borisov ◽  
Ryan Modrak ◽  
Fuchun Gao ◽  
Jeroen Tromp

Full-waveform inversion (FWI) is a powerful method for estimating the earth’s material properties. We demonstrate that surface-wave-driven FWI is well-suited to recovering near-surface structures and effective at providing S-wave speed starting models for use in conventional body-wave FWI. Using a synthetic example based on the SEG Advanced Modeling phase II foothills model, we started with an envelope-based objective function to invert for shallow large-scale heterogeneities. Then we used a waveform-difference objective function to obtain a higher-resolution model. To accurately model surface waves in the presence of complex tomography, we used a spectral-element wave-propagation solver. Envelope misfit functions are found to be effective at minimizing cycle-skipping issues in surface-wave inversions, and surface waves themselves are found to be useful for constraining complex near-surface features.


2018 ◽  
Vol 6 (4) ◽  
pp. SM27-SM37 ◽  
Author(s):  
Jing Li ◽  
Kai Lu ◽  
Sherif Hanafy ◽  
Gerard Schuster

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.


Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. EN77-EN90 ◽  
Author(s):  
Paolo Bergamo ◽  
Laura Valentina Socco

Surface-wave (SW) techniques are mainly used to retrieve 1D velocity models and are therefore characterized by a 1D approach, which might prove unsatisfactory when relevant 2D effects are present in the investigated subsurface. In the case of sharp and sudden lateral heterogeneities in the subsurface, a strategy to tackle this limitation is to estimate the location of the discontinuities and to separately process seismic traces belonging to quasi-1D subsurface portions. We have addressed our attention to methods aimed at locating discontinuities by identifying anomalies in SW propagation and attenuation. The considered methods are the autospectrum computation and the attenuation analysis of Rayleigh waves (AARW). These methods were developed for purposes and/or scales of analysis that are different from those of this work, which aims at detecting and characterizing sharp subvertical discontinuities in the shallow subsurface. We applied both methods to two data sets, synthetic data from a finite-element method simulation and a field data set acquired over a fault system, both presenting an abrupt lateral variation perpendicularly crossing the acquisition line. We also extended the AARW method to the detection of sharp discontinuities from large and multifold data sets and we tested these novel procedures on the field case. The two methods are proven to be effective for the detection of the discontinuity, by portraying propagation phenomena linked to the presence of the heterogeneity, such as the interference between incident and reflected wavetrains, and energy concentration as well as subsequent decay at the fault location. The procedures we developed for the processing of multifold seismic data set showed to be reliable tools in locating and characterizing subvertical sharp heterogeneities.


Geophysics ◽  
2020 ◽  
pp. 1-53
Author(s):  
Sylvain Pasquet ◽  
Wei Wang ◽  
Po Chen ◽  
Brady A. Flinchum

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.


2020 ◽  
Vol 39 (5) ◽  
pp. 310-310
Author(s):  
Steve Sloan ◽  
Dan Feigenbaum

This special section on near-surface imaging and modeling was intended originally to focus on improving deeper imaging for exploration purposes through more accurate representations of the near surface, the highly variable zone that energy must traverse through on the way down and back up again to be recorded at the surface. However, as proposed manuscript topics started coming in, it became clear that this section would cover a wider range, from kilometers down to meters. Papers in this section highlight a range of near-surface-related work that includes applying full-waveform inversion (FWI) to improve near-surface velocity models, identifying potential sinkhole hazards before they collapse, the potential of smartphones as geophysical sensors, and new open-source software for ground-penetrating radar data.


Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Gérard C. Herman ◽  
Colin Perkins

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.


2021 ◽  
Author(s):  
◽  
Francesco Civilini

<p>We present three projects that use different bandwidths of the ambient noise spectrum to solve geophysical problems. Specifically, we use signals within the noise field to determine surface and shear wave velocities, image the shallow and deep crust, and monitor time-dependent deformation resulting from geothermal fluid injection and extraction.  Harrat Al-Madinah, a Cenozoic bimodal alkaline volcanic field in west-central Saudi Arabia, is imaged using shear-velocities obtained from natural ambient seismic noise. To our knowledge, this project is the first analysis of Saudi Arabia structure using ambient noise methods. Surface wave arrivals are extracted from a year's worth of station-pair cross-correlations, which are approximations of the empirical Green's function of the interstation path. We determine group and phase velocity surface wave dispersion maps with a 0.1 decimal degree resolution and resolve a zone of slow surface wave velocity south-east of the city of Medina, which is spatially correlated with the most recent historical eruption (the 1256 CE Medina eruption). Dispersion curves are calculated at each grid-point of the surface-wave velocity maps and inverted to obtain measurements of shear-velocity with depth. The 1D velocity models are then used to produce average shear-velocity models for the volcanic field. A shear-velocity increase ranging from 0.5 to 1.0 km/s, suggesting a layer interface, is detected at approximately 20 km depth and compared to P-wave measurement from a previous refraction study. We compute cross-section profiles by interpolating the inversions into a pseudo-3D model and resolve a zone of slow shear-velocity below the 1256 CE eruption location. These areas are also spatially correlated with low values of Bouguer gravity. We hypothesize that the low shear-velocity and gravity measurements are caused by fluids and fractures created from prior volcanic eruptions.   We use the coda of cross-correlations extracted from ambient noise to determine shear-velocity changes at Rotokawa and Ngatamariki, two electricity producing geothermal fields located in the North Island of New Zealand. Stacks of cross correlations between stations prior to the onset of production are compared to cross correlations of moving stacks in time periods of well stimulation and the onset of electricity production using the Moving Window Cross Spectral technique. An increase between 0.05% to 0.1% of shear-velocity is detected at Rotokawa coinciding with an increase of injection. The shear-velocity subsequently decreases by approximately 0.1% when the rate of production surpasses the rate of injection. A similar amplitude shear-velocity increase is detected at Ngatamariki during the beginning of injection. After the initial increase, the shear-velocity at Ngatamariki fluctuates in response to differences in injection and production rates. A straight-ray pseudo-tomography analysis is conducted at the geothermal fields, which reveals that localized positive velocity changes are co-located with injection wells.  Lastly, we use ambient noise and active sources at the Ngatamariki geothermal field to determine the structure of the top 200 meters using the Refraction Microtremor technique. We deployed a linear 72-channel array of vertical geophones with ten meter spacing at two locations of the geothermal field and determine average 1D and 2D shear-velocity profiles. We were able to image depths between 57 to 93 meters for 2D profiles and up to 165 meters for 1D profiles. A shear-velocity anomaly was detected across one of the lines that coincided with the inferred location of a fault determined from nearby well logs. This suggests that the method can be used to cheaply and quickly constrain near-surface geology at geothermal fields, where ambient noise is abundant and typical reflection and refraction surveys require large inputs of energy and are hindered by attenuation and scattering in near-surface layers.</p>


2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  

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.


Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A83-75A102 ◽  
Author(s):  
Laura Valentina Socco ◽  
Sebastiano Foti ◽  
Daniele Boiero

Today, surface-wave analysis is widely adopted for building near-surface S-wave velocity models. The surface-wave method is under continuous and rapid evolution, also thanks to the lively scientific debate among different disciplines, and interest in the technique has increased significantly during the last decade. A comprehensive review of the literature in the main scientific journals provides historical perspective, methodological issues, applications, and most-promising recent approaches. Higher modes in the inversion and retrieval of lateral variations are dealt with in great detail, and the current scientific debate on these topics is reported. A best-practices guideline is also outlined.


Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. V21-V37 ◽  
Author(s):  
Christine E. Krohn ◽  
Partha S. Routh

We have developed a new tomographic inversion method that is able to determine the properties of complex surface waves, which are multimodal and heterogeneous. These properties can be used to generate a detailed near-surface earth model or to predict and remove the surface waves, while protecting reflection signals even with aliased data. The inversion assumes plane-wave physics and generates surface-consistent model parameters as a function of frequency. In this paper, we validate our method with 2D models and data. In a companion paper, we demonstrate its application to 3D data. Inversion for a single mode is linear, but the linearity does not hold at higher frequencies, where multiple modes interfere. However, single-mode inversion results can be used to create a starting model for the subsequent nonlinear multimode tomography. The resulting velocity-frequency grid has greater resolution compared with a beam-forming method. The dispersion curves can be used as input to a subsequent standard 1D surface-wave inversion to generate a velocity-depth model. The tomographic method also determines a grid of attenuation quality factors and variations in the source amplitude and bandwidth, which correlate with the near-surface elevation changes. The amplitude and phase properties can be used together to predict the surface-wave waveforms, which can then be adaptively subtracted from the data on a trace-to-trace basis.


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