scholarly journals Time-lapse monitoring of climate effects on earthworks using surface waves

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. EN1-EN15 ◽  
Author(s):  
Paolo Bergamo ◽  
Ben Dashwood ◽  
Sebastian Uhlemann ◽  
Russell Swift ◽  
Jonathan E. Chambers ◽  
...  
Keyword(s):  
Surface Wave ◽  
Transportation Network ◽  
Wave Dispersion ◽  
Time Lapse ◽  
Spatial And Temporal Variability ◽  
Data Sets ◽  
Initial Model ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Surface Wave Data

The UK’s transportation network is supported by critical geotechnical assets (cuttings/embankments/dams) that require sustainable, cost-effective management, while maintaining an appropriate service level to meet social, economic, and environmental needs. Recent effects of extreme weather on these geotechnical assets have highlighted their vulnerability to climate variations. We have assessed the potential of surface wave data to portray the climate-related variations in mechanical properties of a clay-filled railway embankment. Seismic data were acquired bimonthly from July 2013 to November 2014 along the crest of a heritage railway embankment in southwest England. For each acquisition, the collected data were first processed to obtain a set of Rayleigh-wave dispersion and attenuation curves, referenced to the same spatial locations. These data were then analyzed to identify a coherent trend in their spatial and temporal variability. The relevance of the observed temporal variations was also verified with respect to the experimental data uncertainties. Finally, the surface wave dispersion data sets were inverted to reconstruct a time-lapse model of S-wave velocity for the embankment structure, using a least-squares laterally constrained inversion scheme. A key point of the inversion process was constituted by the estimation of a suitable initial model and the selection of adequate levels of spatial regularization. The initial model and the strength of spatial smoothing were then kept constant throughout the processing of all available data sets to ensure homogeneity of the procedure and comparability among the obtained [Formula: see text] sections. A continuous and coherent temporal pattern of surface wave data, and consequently of the reconstructed [Formula: see text] models, was identified. This pattern is related to the seasonal distribution of precipitation and soil water content measured on site.

Effects of thin soft layers on body waves

1969 ◽  
Vol 59 (5) ◽  
pp. 2071-2078
Author(s):  
Tom Landers ◽  
Jon F. Claerbout
Keyword(s):  
Surface Wave ◽  
Upper Mantle ◽  
Body Wave ◽  
Wave Dispersion ◽  
Soft Material ◽  
Body Waves ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Use Of Time ◽  
Surface Wave Data

abstract The inability of simple layered models to fit both Rayleigh wave and Love wave data has led to the proposal of an upper mantle interleaved with thin soft horizontal layers. Since surface-wave dispersion is not sensitive to the distribution of soft material but only to the fraction of soft material a variety of models is possible. The solution to this indeterminancy is found through body-wave analysis. It is shown that body waves are dispersed according to the thinness and softness of the layers. Three models, each of which satisfy all surface-wave data, are examined. Transmission seismograms calculated for these models show one to be impossible, one improbable and the other possible. Synthesis of the seismograms is accomplished through the use of time domain theory as the complicated frequency response of the models makes a frequency oriented Haskell-Thompson approach impractical.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

Multi-Window Weighted Stacking of Surface-Wave Dispersion

Geophysics ◽  
2020 ◽  
pp. 1-53
Author(s):  
Sylvain Pasquet ◽  
Wei Wang ◽  
Po Chen ◽  
Brady A. Flinchum
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Real Data ◽  
Wave Dispersion ◽  
Lateral Resolution ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Shallow Subsurface ◽  
Depth Of Investigation

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.

Retrieving 2D structures from surface-wave data by means of space-varying spatial windowing

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. EN39-EN51 ◽  
Author(s):  
Paolo Bergamo ◽  
Daniele Boiero ◽  
Laura Valentina Socco
Keyword(s):  
Surface Wave ◽  
Real Data ◽  
Processing Technique ◽  
Dispersion Curves ◽  
Data Sets ◽  
Local Dispersion ◽  
Wave Data ◽  
Survey Line ◽  
Wave Path ◽  
Surface Wave Data

Surface-wave techniques are mainly used to retrieve 1D subsurface models. However, in 2D environments, the 1D approach usually neglects the presence of lateral variations and because the surface-wave path crosses different materials, the resulting model is a simplified or misleading description of the site. We tested a processing technique to retrieve 2D structures from surface-wave data acquired with a limited number of receivers. Our technique was based on a two-step process. First, we extracted several local dispersion curves along the survey line using a spatial windowing based on a set of Gaussian windows with different shapes; the window maxima span the survey line so that we were able to extract a dispersion curve from the seismic record for every window. This provided a set of local dispersion curves each of them referring to a different subsurface portion. This space varying spatial windowing provided a good compromise between wavenumber resolution and the lateral resolution of the obtained local dispersion curves. In the second step, we inverted the retrieved set of dispersion curves using a laterally constrained inversion scheme. We applied this procedure to the processing of synthetic and real data sets and the method proved to be successful in reconstructing even complex 2D structures in the subsurface.

scholarly journals Potential Use of Time-Lapse Surface Seismics for Monitoring Thawing of the Terrestrial Arctic

2020 ◽  
Vol 10 (5) ◽  
pp. 1875
Author(s):  
Helene Meling Stemland ◽  
Tor Arne Johansen ◽  
Bent Ole Ruud
Keyword(s):  
Surface Wave ◽  
Seismic Data ◽  
Rock Physics ◽  
Wave Dispersion ◽  
Time Lapse ◽  
Unconsolidated Sediments ◽  
Frozen Ground ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Surface Temperatures

The terrestrial Arctic is warming rapidly, causing changes in the degree of freezing of the upper sediments, which the mechanical properties of unconsolidated sediments strongly depend upon. This study investigates the potential of using time-lapse surface seismics to monitor thawing of currently (partly) frozen ground utilizing synthetic and real seismic data. First, we construct a simple geological model having an initial temperature of −5 °C, and infer constant surface temperatures of −5 °C, +1 °C, +5 °C, and +10 °C for four years to this model. The geological models inferred by the various thermal regimes are converted to seismic models using rock physics modeling and subsequently seismic modeling based on wavenumber integration. Real seismic data reflecting altered surface temperatures were acquired by repeated experiments in the Norwegian Arctic during early autumn to mid-winter. Comparison of the surface wave characteristics of both synthetic and real seismic data reveals time-lapse effects that are related to thawing caused by varying surface temperatures. In particular, the surface wave dispersion is sensitive to the degree of freezing in unconsolidated sediments. This demonstrates the potential of using surface seismics for Arctic climate monitoring, but inversion of dispersion curves and knowledge of the local near-surface geology is important for such studies to be conclusive.

Spurious low velocity zones in joint inversions of surface waves and receiver functions

2019 ◽  
Vol 219 (2) ◽  
pp. 1032-1042
Author(s):  
Chao Gao ◽  
Erin Cunningham ◽  
Vedran Lekić
Keyword(s):  
Surface Wave ◽  
Crustal Structure ◽  
Receiver Function ◽  
Model Space ◽  
Sedimentary Basins ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
Low Velocity ◽  
Surface Wave Dispersion ◽  
Surface Wave Data

SUMMARY Low-velocity layers within the crust can indicate the presence of melt and lithologic differences with implications for crustal composition and formation. Seismic wave conversions and reverberations across the base of the crust or intracrustal discontinuities, analysed using the receiver function method, can be used to constrain crustal layering. This is commonly accomplished by inverting receiver functions jointly with surface wave dispersion. Recently, the proliferation of model-space search approaches has made this technique a workhorse of crustal seismology. We show that reverberations from shallow layers such as sedimentary basins produce spurious low-velocity zones when inverted for crustal structure with surface wave data of insufficiently high frequency. Therefore, reports of such layers in the literature based on inversions using receiver function data should be re-evaluated. We demonstrate that a simple resonance-removal filter can suppress these effects and yield reliable estimates of crustal structure, and advocate for its use in receiver-function based inversions.

Rayleigh Wave Analysis in the Presence of High Impedance Boundaries

2020 ◽  
Author(s):  
Gabriel Gribler
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
High Velocity ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
High Impedance ◽  
Surface Wave Data

Surface wave data is commonly used to estimate shear wave velocity of the subsurface. Most standard approaches for analyzing surface wave data fail under conditions when high-impedance boundaries, or sharp contrasts, exists within the range of sensitivities. I present two primary scenarios, one with a high velocity bedrock layer in the upper 20 meters overlain by low velocity unconsolidated sediment, and a thin high velocity road layer on top of unconsolidated sediments. For the shallow bedrock case, I present new multicomponent methods to more accurately and reliably extract surface wave dispersion information from active source waveforms. I also present a new data inversion method that utilizes additional information from multicomponent wavefields, allowing for more accurate estimates of shear wave velocities in these environments. For the thin, high velocity surface layer, I highlight the potential pitfalls of ignoring this layer when inverting for the underlying shear wave velocities, and I propose a solution that yields more accurate velocity estimates. All of these approaches are explained and presented using modeled data, then extended to highlight the improvements over standard approaches using real data.

Surface-wave dispersion spectrum inversion method applied to Love and Rayleigh waves recorded by distributed acoustic sensing

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. EN1-EN12 ◽  
Author(s):  
Zhenghong Song ◽  
Xiangfang Zeng ◽  
Clifford H. Thurber
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Love Wave ◽  
Wave Dispersion ◽  
Dynamic Strain ◽  
Inversion Method ◽  
Data Sets ◽  
Surface Wave Dispersion ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing

Recently, distributed acoustic sensing (DAS) has been applied to shallow seismic structure imaging providing dense spatial sampling at a relatively low cost. DAS on a standard straight fiber-optic cable mostly records axial dynamic strain, which makes it difficult to separate the Rayleigh and Love wavefields. As a result, the mixed Rayleigh and Love wave signals cannot be used in the conventional surface-wave dispersion inversion method. Therefore, it is often ensured that the source and the cable are in the same line and only Rayleigh wave dispersion is used, which limits the constraints on structure and model resolution. We have inverted surface-wave dispersion spectra instead of dispersion curves. This inversion method can use mixed Rayleigh and Love waves recorded when the source and receiver array are not aligned. The multiple-channel records are transformed to the frequency domain, and a slant stack method is used to construct the dispersion spectra. The genetic algorithm method is used to obtain an optimal S-wave velocity model that minimizes the difference between theoretical and observed dispersion spectra. A series of synthetic tests are conducted to validate our method. The results suggest that our method not only improves the flexibility of the acquisition system design, but the Love wave data also provide additional constraints on the structure. Our method is applied to the active source and ambient noise data sets acquired at a geothermal site and provides consistent results for different data sets and acquisition geometries. The sensitivity of the dispersion spectra to layer thickness, density, and P-wave velocity is also discussed. With our method, the amount of usable data can be increased, helping deliver better subsurface images.

Constrained Surface-wave Dispersion Inversion Using GPR Reflection Data

2020 ◽  
Author(s):  
Shufan Hu ◽  
Yonghui Zhao ◽  
Wenda Bi ◽  
Ruiqing Shen ◽  
Bo Li ◽  
...  
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Reflection Data ◽  
Uniqueness Of The Solution ◽  
S Model

<p>Ground penetrating radar (GPR) and Seismic Surface Wave methods (SWMs) are two nondestructive testing (NDT) methods commonly used in near-surface site investigations. These two methods investigate the media properties of subsurface based on different physical phenomena. GPR has a good resolvability to characterize the layered structure since the propagation of electromagnetic wave is sensitive to the change of electrical properties, while, the geometric dispersion of surface waves can be used to retrieve the variation of S-wave velocity (<em>V</em>s) with depth. In most situations, these two data sets are processed separately, and the results are later used for comprehensive interpretation. Constrained inversion, as a way to implement data fusion, can alleviate the non-uniqueness of the solution and produce more consistent information for the comprehensive site and material investigations.</p><p>We present an algorithm for the inversion of surface-wave dispersion curves with GPR interface constraints in 2D media. The reflection interfaces interpreted from the GPR profile are integrated into a cell- and boundary-based <em>V</em>s model. This implementation allows both vertical and lateral changes within each region while also allows sharp changes across the boundaries. In addition, our algorithm simultaneously inverts several dispersion curves extracted along the survey line using multi-size spatial windows, which mitigates the adverse effects of 1D assumption in traditional surface-wave dispersion inversion and improves the matching of GPR and SWMs in lateral variations. We use synthetic and field data sets to test the effectivity of the proposed method. Both results show the improved resolution of the <em>V</em>s model retrieved by the constrained inversion compared to the standard inversion.</p>

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