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.

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

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

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>

scholarly journals Reliable workflow for inversion of seismic receiver function and surface wave dispersion data: a “13 BB Star” case study

2019 ◽  
Vol 24 (1) ◽  
pp. 101-120
Author(s):  
Kajetan Chrapkiewicz ◽  
Monika Wilde-Piórko ◽  
Marcin Polkowski ◽  
Marek Grad
Keyword(s):  
Surface Wave ◽  
Inverse Problems ◽  
Receiver Function ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
P Wave ◽  
Surface Wave Dispersion ◽  
Phase Velocity Dispersion ◽  
Wide Range

AbstractNon-linear inverse problems arising in seismology are usually addressed either by linearization or by Monte Carlo methods. Neither approach is flawless. The former needs an accurate starting model; the latter is computationally intensive. Both require careful tuning of inversion parameters. An additional challenge is posed by joint inversion of data of different sensitivities and noise levels such as receiver functions and surface wave dispersion curves. We propose a generic workflow that combines advantages of both methods by endowing the linearized approach with an ensemble of homogeneous starting models. It successfully addresses several fundamental issues inherent in a wide range of inverse problems, such as trapping by local minima, exploitation of a priori knowledge, choice of a model depth, proper weighting of data sets characterized by different uncertainties, and credibility of final models. Some of them are tackled with the aid of novel 1D checkerboard tests—an intuitive and feasible addition to the resolution matrix. We applied our workflow to study the south-western margin of the East European Craton. Rayleigh wave phase velocity dispersion and P-wave receiver function data were gathered in the passive seismic experiment “13 BB Star” (2013–2016) in the area of the crust recognized by previous borehole and refraction surveys. Final models of S-wave velocity down to 300 km depth beneath the array are characterized by proximity in the parameter space and very good data fit. The maximum value in the mantle is higher by 0.1–0.2 km/s than reported for other cratons.

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