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Welcome to the Birthplace of MASW! || SurfSeis Features

SurfSeis has many unique features, which we have developed exclusively here at the KGS (origin of MASW) since the late 1990s to meet our applied research needs and that address real-world problems.

Many of these features were specifically developed to meet the real-world needs of our many collaborators and software partners (we consider every owner of SurfSeis to be a partner). Accuracy of results, and therefore information about the subsurface at various sites, has always been our primary goal. SurfSeis is a mature software (first copyrighted in 1998) whose science, algorithms, and software applications have been tested for more than decade and a half and verified through both invasive confirmation and the peer review community. There is little doubt that this is the most scientifically sound and up-to-date software available anywhere at this time.

Below are listed some unique features of SurfSeis that we use frequently in our research and explanations of how they can be helpful in many applications in which you might be engaged. It is very likely that most of these can only be found in SurfSeis.

  1. HRLRT
    1. Interpret fundamental mode.
    2. Interpret higher modes.
    3. Improving horizontal resolution by allowing the use of shorter spreads while still being able to follow the dispersion curve trends.
    4. Improving vertical resolution by using more layers when inverting with higher mode.
  2. Multi-mode inversion
    1. Minimize instability.
    2. Higher vertical resolution by using more layers without instability.
    3. Increase the investigation depth.
  3. Variable topography
  4. Variable depth inversion
    1. Can be an interpretive tool itself.
  5. Ability to view and import initial models in 2D
    1. Help avoid initial-model erroneous assumptions/errors, which would not have been detected with the single 1D plots approach.
    2. Ability to import density values or derive 2D density models from Vp using Gardner's or user-specified equation parameters, i.e., tuned for a specific site.
  6. Love-wave processing
    1. No Vp a-priori information necessary for inversion.
    2. Better fundamental-mode quality due to reduced likelihood for higher mode.
  7. Qs estimation from Rayleigh-wave attenuation measurements
  8. Window splitting for improving passive data dispersion-curve imaging
  9. Stitching dispersion-curve images
    1. Allows user visually-controlled incorporation of different wavelengths from different data. Such data can result from using different sources, spread sizes, and/or transforms, which can enhance dispersion-curve frequency ranges, and horizontal and vertical resolution of the final results.

We are also very aware and actually excited that many commercial software developers have recently (in the last 5 years) begun working to produce software with the same flexibility, thoroughness, accuracy, and completeness as SurfSeis. Competition only benefits the users and the user has always been our focus as we make this living and growing product of our research available to everyone.

We encourage commercial software developers to try to match our science, algorithm efficiencies, breadth of applications, user centric features, etc. Our job and passion is research, but we are striving to make this product of that research available as quickly as possible to everyone through SurfSeis. You can track our most recent publications (http://www.kgs.ku.edu/software/surfseis/pubs_year.html) and match those to features available in SurfSeis (see our flyers on the SurfSeis web page http://www.kgs.ku.edu/software/surfseis/index.html). These comparisons and scientific endorsements through the peer reviewed publication process will provide you all the confidence necessary to become one of our SurfSeis partners.

Specific Studies, Approaches, and Applications

Ivanov et al. (2017b) is an example of using HRLRT, Multi-mode inversion, Love waves, and variable depth.

The importance of using a density trend instead of constant density values was driven by our Ivanov et al. (2016) research.

Ivanov et al. (2017a) show the benefits of using HRLRT, including using both active- and passive-source data.

Qs estimation from attenuation measurements was driven by our research (Xia et al., 2002; Xia et al., 2012; Ivanov et al., 2014; Feigenbaum et al., 2016). Obtaining Qs can be a challenging problem because there are many variables that can contribute to the problem. We have applied special efforts to make it work, including the use of a special filter and the ability to include 2D Vp and density models. To the best of our knowledge, we believe SurfSeis is the only software offering such a tool.

We particularly like HRLRT. On active data it provided high frequencies of the fundamental mode, when conventional transforms failed to do so because of interferences from higher modes. On some passive data sets it provided dispersion-curve images we would not have guessed were possible by looking at their conventional-transform counterparts. In addition, window-splitting can further improve the images, if multi passive sources (such as a freight train) were contributing (Ivanov et al., 2013) to your energy. We think you will find the HRLRT very useful for some of your data sets, as well.

References

Highlighted references are available for download courtesy of SEG (www.seg.org). If a publication is not offered for download, then this is likely due to following redistribution policies by other publishers.

Feigenbaum, D., J. Ivanov, R. Miller, S. Peterie, and S. Morton, 2016, Near-surface Qs estimations using multi-hannel analysis of surface waves (MASW) and the effect of nonfundamental mode energy on Q estimation: An example from Yuma proving ground, Arizona: SEG Technical Program Expanded Abstracts 2016, 4971-4976.

Ivanov, J., B. Leitner, W.T. Shefchik, T.J. Schwenk, and S.L. Peterie, 2013, Evaluating hazards at salt cavern sites using multichannel analysis of surface waves: The Leading Edge, 32, 289-305. [available online]

Ivanov, J., R.D. Miller, S.L. Peterie, and G. Tsoflias, 2014, Near-surface Qs and Qp estimations from Rayleigh waves using multi-channel analysis of surface waves (MASW) at an Arctic ice-sheet site: SEG Technical Program Expanded Abstracts 2014, 2006-2012. [available online]

Ivanov, J., G. Tsoflias, R.D. Miller, S. Peterie, S. Morton, and J. Xia, 2016, Impact of density information on Rayleigh surface wave inversion results: Journal of Applied Geophysics, 135, 43-54. [available online from Elsevier]

Ivanov, J., R. Miller, D. Feigenbaum, and J. Schwenk, 2017a, Benefits of using the high-resolution linear Radon transform with the multichannel analysis of surface waves method: SEG Technical Program Expanded Abstracts 2017, 2647-2653. [available online]

Ivanov, J., R.D. Miller, D. Feigenbaum, S.L.C. Morton, S.L. Peterie, and J.B. Dunbar, 2017b, Revisiting levees in southern Texas using Love-wave multichannel analysis of surface waves with the high-resolution linear Radon transform: Interpretation, 5, T287-T298. [available online]

Xia, J., Y.X. Xu, R.D. Miller, and J. Ivanov, 2012, Estimation of near-surface quality factors by constrained inversion of Rayleigh-wave attenuation coefficients: Journal of Applied Geophysics, 82, 137-144.

Xia, J., R.D. Miller, C.B. Park, and G. Tian, 2002, Determining Q of near-surface materials from Rayleigh waves: Journal of Applied Geophysics, 51, 121-129.