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• W2153991621 abstract "This overview article gives a picture of multichannel analysis of high-frequency surface (Rayleigh and Love) waves developed mainly by research scientists at the Kansas Geological Survey, the University of Kansas and China University of Geosciences (Wuhan) during the last eighteen years by discussing dispersion imaging techniques, inversion systems, and real-world examples. Shear (S)-wave velocities of near-surface materials can be derived from inverting the dispersive phase velocities of high-frequency surface waves. Multichannel analysis of surface waves—MASW used phase information of high-frequency Rayleigh waves recorded on vertical component geophones to determine near-surface S-wave velocities. The differences between MASW results and direct borehole measurements are approximately 15% or less and random. Studies show that inversion with higher modes and the fundamental mode simultaneously can increase model resolution and an investigation depth. Multichannel analysis of Love waves—MALW used phase information of high-frequency Love waves recorded on horizontal (perpendicular to the direction of wave propagation) component geophones to determine S-wave velocities of shallow materials. Because of independence of compressional (P)-wave velocity, the MALW method has some attractive advantages, such as 1) Love-wave dispersion curves are simpler than Rayleigh wave's; 2) dispersion images of Love-wave energy have a higher signal to noise ratio and more focused than those generated from Rayleigh waves; and 3) inversion of Love-wave dispersion curves is less dependent on initial models and more stable than Rayleigh waves. To derive S-wave velocities of near-surface materials from high-frequency surface waves only utilizes their phase information. Feasibility of using their amplitude information to estimate near-surface quality factors (Qs and/or Qp) has been studied. Attenuation coefficients of high-frequency surface (Rayleigh and/or Love) waves can be calculated from their amplitude. And by inverting attenuation coefficients, it is feasible to obtain quality factors. Similar to inverting phase velocities of Love waves for S-wave velocities, attenuation coefficients of Love waves are independent of Qp, which makes inversion of attenuation coefficients of Love waves to estimate Qs simpler than that of Rayleigh waves. Both MASW and MALW methods to estimate near-surface S-wave velocities are non-invasive, non-destructive, environment-friendly, low-cost, fast, and in situ seismic methods and possess stable and efficient inversion algorithms to invert phase velocities of surface waves. Real world examples demonstrated that near-surface S-wave velocities derived from phase information are reliable and that methods discussed in the paper to estimate near-surface quality factors from amplitude information are feasible." @default.
• W2153991621 created "2016-06-24" @default.
• W2153991621 creator A5074813047 @default.
• W2153991621 date "2014-04-01" @default.
• W2153991621 modified "2023-10-01" @default.
• W2153991621 title "Estimation of near-surface shear-wave velocities and quality factors using multichannel analysis of surface-wave methods" @default.
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• W2153991621 cites W1967652956 @default.
• W2153991621 cites W1977273766 @default.
• W2153991621 cites W1978423631 @default.
• W2153991621 cites W1980862556 @default.
• W2153991621 cites W1981633816 @default.
• W2153991621 cites W1985322490 @default.
• W2153991621 cites W1985377452 @default.
• W2153991621 cites W1990302166 @default.
• W2153991621 cites W1992304369 @default.
• W2153991621 cites W1993270348 @default.
• W2153991621 cites W1994325700 @default.
• W2153991621 cites W1995228469 @default.
• W2153991621 cites W2000334937 @default.
• W2153991621 cites W2005208601 @default.
• W2153991621 cites W2006895430 @default.
• W2153991621 cites W2012836461 @default.
• W2153991621 cites W2013760202 @default.
• W2153991621 cites W2014287661 @default.
• W2153991621 cites W2021872449 @default.
• W2153991621 cites W2023223009 @default.
• W2153991621 cites W2025003584 @default.
• W2153991621 cites W2025695778 @default.
• W2153991621 cites W2030779670 @default.
• W2153991621 cites W2036988450 @default.
• W2153991621 cites W2041765898 @default.
• W2153991621 cites W2047319551 @default.
• W2153991621 cites W2053315555 @default.
• W2153991621 cites W2055511959 @default.
• W2153991621 cites W2058474279 @default.
• W2153991621 cites W2064648860 @default.
• W2153991621 cites W2064903949 @default.
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• W2153991621 cites W2078841894 @default.
• W2153991621 cites W2087070363 @default.
• W2153991621 cites W2106632637 @default.
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• W2153991621 cites W2122095312 @default.
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• W2153991621 cites W2126682510 @default.
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• W2153991621 cites W2133526188 @default.
• W2153991621 cites W2145921611 @default.
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• W2153991621 cites W2171909447 @default.
• W2153991621 cites W2333306898 @default.
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• W2153991621 cites W4246566919 @default.
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• W2153991621 doi "https://doi.org/10.1016/j.jappgeo.2014.01.016" @default.
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