SWIP: An integrated workflow for surface-wave dispersion inversion and profiling

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. WB47-WB61 ◽  
Author(s):  
Sylvain Pasquet ◽  
Ludovic Bodet
Keyword(s):  
Surface Wave ◽  
Low Frequency ◽  
P Wave ◽  
Simultaneous Estimation ◽  
S Wave ◽  
Process Data ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Uncertainty Range ◽  
And Inversion

The simultaneous estimation of 2D pressure (P-) and S-wave velocities ([Formula: see text] and [Formula: see text], respectively) is a promising approach for imaging subsurface mechanical properties. It can be performed with a single acquisition setup by combining P-wave refraction and surface-wave (SW) analysis. Although SW methods are commonly applied for the 1D estimation of [Formula: see text], 2D profiling requires the implementation of specific processing and inversion tools not yet widely available in the community. We have developed an open-source MATLAB-based package that performs SW inversion and profiling (SWIP) so as to retrieve 1D to 2D variations of [Formula: see text] from any kind of linear active-source near-surface seismic data. Each step of the workflow involves up-to-date processing and inversion techniques and provides ready-to-use outputs with quality control tools. First, windowing and stacking techniques are implemented to enhance the signal-to-noise ratio and extract local dispersion images along the line. Then, dispersion curves are picked for each window with an uncertainty range in the phase velocity including higher uncertainties at low frequency. These curves are next inverted using a Monte Carlo approach with various parameterizations (e.g., user defined, refraction based). The best models are finally selected according to their fit to the data to build an average final model with a suggested investigation depth. As an example, we used SWIP to process data collected at a Yellowstone hydrothermal system. Our results show the benefits of estimating [Formula: see text] and [Formula: see text] from a single seismic setup to highlight subsurface gas pathways.

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.

Advances in surface-wave tomography for near-surface applications

2021 ◽  
Vol 40 (8) ◽  
pp. 567-575
Author(s):  
Myrto Papadopoulou ◽  
Farbod Khosro Anjom ◽  
Mohammad Karim Karimpour ◽  
Valentina Laura Socco
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
P Wave ◽  
Seismic Exploration ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Processing Times ◽  
Spatial Coverage ◽  
Wave Tomography

Surface-wave (SW) tomography is a technique that has been widely used in the field of seismology. It can provide higher resolution relative to the classical multichannel SW processing and inversion schemes that are usually adopted for near-surface applications. Nevertheless, the method is rarely used in this context, mainly due to the long processing times needed to pick the dispersion curves as well as the inability of the two-station processing to discriminate between higher SW modes. To make it efficient and to retrieve pseudo-2D/3D S-wave velocity (VS) and P-wave velocity (VP) models in a fast and convenient way, we develop a fully data-driven two-station dispersion curve estimation, which achieves dense spatial coverage without the involvement of an operator. To handle higher SW modes, we apply a dedicated time-windowing algorithm to isolate and pick the different modes. A multimodal tomographic inversion is applied to estimate a VS model. The VS model is then converted to a VP model with the Poisson's ratio estimated through the wavelength-depth method. We apply the method to a 2D seismic exploration data set acquired at a mining site, where strong lateral heterogeneity is expected, and to a 3D pilot data set, recorded with state-of-the-art acquisition technology. We compare the results with the ones retrieved from classical multichannel analysis.

Crustal structure of the Iceland region from dispersion of surface waves

1962 ◽  
Vol 52 (2) ◽  
pp. 359-388
Author(s):  
Eysteinn Tryggvason
Keyword(s):  
Surface Layer ◽  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Crustal Structure ◽  
Wave Dispersion ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Mid Atlantic Ridge

ABSTRACT A number of Icelandic records of earthquakes originating in the Mid-Atlantic Seismic Belt between 52° and 70° N. lat. have been investigated. The surface waves on these records are chiefly in the period interval 3–10 sec, and are first mode Love-waves and Rayleigh-waves. The surface wave dispersion can be explained by a three-layered crustal structure as follows. A surface layer of S-wave velocity about 2.7 km/sec covering the whole region studied, a second layer of S-wave velocity about 3.6 km/sec covering Iceland and extending several hundred kilometers off the coasts and a third layer of S-wave velocity about 4.3 km/sec and P-wave velocity about 7.4 km/sec underlying the whole region. The thickness of the surface layer appears to be about 4 km on the Mid-Atlantic Ridge south of Iceland and in western Iceland, 3 km in central Iceland and 7 km northwest of Iceland. The second layer is apparently of similar thickness than the surface layer, while the third layer is thick; and the surface wave dispersion does not indicate any layer of higher wave velocity. This 7.4-layer is supposed to belong to the mantle, although its wave velocity is significantly lower than usually found in the upper mantle

A comprehensive comparison between the refraction microtremor and seismic interferometry methods for phase-velocity estimation

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. EN99-EN108 ◽  
Author(s):  
Zongbo Xu ◽  
T. Dylan Mikesell ◽  
Jianghai Xia ◽  
Feng Cheng
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Velocity Estimation ◽  
Seismic Interferometry ◽  
S Wave ◽  
Noise Sources ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Refraction Microtremor

Passive-source seismic-noise-based surface-wave methods are now routinely used to investigate the near-surface geology in urban environments. These methods estimate the S-wave velocity of the near surface, and two methods that use linear recording arrays are seismic interferometry (SI) and refraction microtremor (ReMi). These two methods process noise data differently and thus can yield different estimates of the surface-wave dispersion, the data used to estimate the S-wave velocity. We have systematically compared these two methods using synthetic data with different noise source distributions. We arrange sensors in a linear survey grid, which is conveniently used in urban investigations (e.g., along roads). We find that both methods fail to correctly determine the low-frequency dispersion characteristics when outline noise sources become stronger than inline noise sources. We also identify an artifact in the ReMi method and theoretically explain the origin of this artifact. We determine that SI combined with array-based analysis of surface waves is the more accurate method to estimate surface-wave phase velocities because SI separates surface waves propagating in different directions. Finally, we find a solution to eliminate the ReMi artifact that involves the combination of SI and the [Formula: see text]-[Formula: see text] transform, the array processing method that underlies the ReMi method.

Three-component seismic land streamer study of an esker architecture through S- and surface-wave imaging

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. B339-B353 ◽  
Author(s):  
Bojan Brodic ◽  
Alireza Malehmir ◽  
André Pugin ◽  
Georgiana Maries
Keyword(s):  
Surface Wave ◽  
Seismic Source ◽  
Transverse Component ◽  
P Wave ◽  
Wave Component ◽  
Sh Wave ◽  
S Wave ◽  
Near Surface ◽  
Wave Velocities ◽  
Wave Nature

We deployed a newly developed 3C microelectromechanical system-based seismic land streamer over porous glacial sediments to delineate water table and bedrock in Southwestern Finland. The seismic source used was a 500 kg vertical impact drop hammer. We analyzed the SH-wave component and interpreted it together with previously analyzed P-wave component data. In addition to this, we examined the land streamer’s potential for multichannel analysis of surface waves and delineated the site’s stratigraphy with surface-wave-derived S-wave velocities and [Formula: see text] ratios along the entire profile. These S-wave velocities and [Formula: see text] ratios complement the interpretation conducted previously on P-wave stacked section. Peculiarly, although the seismic source used is of a vertical-type nature, the data inspection indicated clear bedrock reflection on the horizontal components, particularly the transverse component. This observation led us to scrutinize the horizontal component data through side-by-side inspection of the shot records of all the three components and particle motion analysis to confirm the S-wave nature of the reflection. Using the apparent moveout velocity of the reflection, as well as the known depth to bedrock based on drilling, we used finite-difference synthetic modeling to further verify its nature. Compared with the P-wave seismic section, bedrock is relatively well delineated on the transverse component S-wave section. Some structures connected to the kettle holes and other stratigraphic units imaged on the P-wave results were also notable on the S-wave section, and particularly on the surface-wave derived S-wave velocity model and [Formula: see text] ratios. Our results indicate that P-, SV-, and SH-wave energy is generated simultaneously at the source location itself. This study demonstrates the potential of 3C seismic for characterization and delineation of the near-surface seismics.

Estimating surface-wave dispersion curves from 3D seismic acquisition schemes: Part 1 — 1D models

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. G85-G93 ◽  
Author(s):  
Daniele Boiero ◽  
Paolo Bergamo ◽  
Roberto Bruno Rege ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
P Wave ◽  
Seismic Exploration ◽  
3D Seismic ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Orthogonal Geometry ◽  
Seismic Acquisition

Surface-wave analysis is based on the estimation of surface-wave dispersion curves, which are then inverted to provide 1D S-wave velocity profiles. Surface-wave dispersion curves can be extracted from P-wave records obtained in seismic exploration and used to characterize the ground structure at a shallow depth. Dispersion curve estimation using 2D wavefield transforms is well-established for 2D acquisition schemes (in-line source and receiver spread). It is possible to extract surface-wave dispersion curves using 2D wavefield transforms from 3D seismic data acquired with any acquisition scheme. In particular, we focus on areal geometry and orthogonal geometry, and we provide a method based on the analysis in the offset domain and the [Formula: see text] multiple signal classification (MUSIC) transform. We assess the performance of the method on synthetic and field data concerning 1D sites.

Surface wave surveying for near-surface site characterization in the East San Francisco Bay Area, California

2016 ◽  
Vol 4 (4) ◽  
pp. SQ59-SQ69 ◽  
Author(s):  
Mitchell Craig ◽  
Koichi Hayashi
Keyword(s):  
Surface Wave ◽  
San Francisco ◽  
Wave Velocity ◽  
Site Characterization ◽  
Surface Zone ◽  
Surface Site ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface

Seismic surface wave methods are effective tools for estimating S-wave velocity in urban areas for near-surface site characterization and geologic hazard assessment. A surface wave survey can provide quantitative site-specific measurement of physical properties needed for the design of earthquake-resistant structures. We successfully used a combined active and passive seismic surface wave method to estimate the S-wave velocity in the upper 30 m at sites with a range of geologic conditions. At five of the six sites, multichannel analysis of surface waves (MASW) and microtremor array method (MAM) methods were used. The MAM method could not be used at one site due to insufficient ambient noise. Data from the active method (MASW) contained higher frequencies that contributed to higher resolution of the near-surface zone, whereas passive data (MAM) contained lower frequencies that provided deeper penetration. Phase velocities from the two methods were in good agreement in the frequency range where they overlapped. Surface wave dispersion curves from the two methods were used to prepare an initial velocity model, and a nonlinear inversion was performed to obtain an improved velocity-depth profile. The use of a multimethod data set provided greater confidence in velocity measurements. The six sites of this study may be classified as belonging to two main groups based on S-wave velocities and geologic materials. Two sites are located in the East Bay Hills on Mesozoic bedrock, and four sites are located on Holocene sedimentary units. The highest [Formula: see text] was [Formula: see text] (class C), at a site with fractured and weathered bedrock exposed in a geotechnical trench at 1–2 m depth. The four sites on Holocene sedimentary units have [Formula: see text] values ranging from 207 to [Formula: see text] (class D).

scholarly journals Joint Body‐ and Surface‐Wave Tomography of Yucca Flat, Nevada, Using a Novel Seismic Source

2019 ◽  
Vol 109 (5) ◽  
pp. 1922-1934 ◽  
Author(s):  
Liam D. Toney ◽  
Robert E. Abbott ◽  
Leiph A. Preston ◽  
David G. Tang ◽  
Tori Finlay ◽  
...  
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Wave Velocity ◽  
Velocity Structure ◽  
Joint Inversion ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models ◽  
Phase Velocity Dispersion

Abstract In preparation for the next phase of the Source Physics Experiments, we acquired an active‐source seismic dataset along two transects totaling more than 30 km in length at Yucca Flat, Nevada, on the Nevada National Security Site. Yucca Flat is a sedimentary basin which has hosted more than 650 underground nuclear tests (UGTs). The survey source was a novel 13,000 kg modified industrial pile driver. This weight drop source proved to be broadband and repeatable, richer in low frequencies (1–3 Hz) than traditional vibrator sources and capable of producing peak particle velocities similar to those produced by a 50 kg explosive charge. In this study, we performed a joint inversion of P‐wave refraction travel times and Rayleigh‐wave phase‐velocity dispersion curves for the P‐ and S‐wave velocity structure of Yucca Flat. Phase‐velocity surface‐wave dispersion measurements were obtained via the refraction microtremor method on 1 km arrays, with 80% overlap. Our P‐wave velocity models verify and expand the current understanding of Yucca Flat’s subsurface geometry and bulk properties such as depth to Paleozoic basement and shallow alluvium velocity. Areas of disagreement between this study and the current geologic model of Yucca Flat (derived from borehole studies) generally correlate with areas of widely spaced borehole control points. This provides an opportunity to update the existing model, which is used for modeling groundwater flow and radionuclide transport. Scattering caused by UGT‐related high‐contrast velocity anomalies substantially reduced the number and frequency bandwidth of usable dispersion picks. The S‐wave velocity models presented in this study agree with existing basin‐wide studies of Yucca Flat, but are compromised by diminished surface‐wave coherence as a product of this scattering. As nuclear nonproliferation monitoring moves from teleseismic to regional or even local distances, such high‐frequency (>5  Hz) scattering could prove challenging when attempting to discriminate events in areas of previous testing.

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