Since version 1.01 was released a year ago (September 2015), the functionality and user friendliness of ParkSEIS© (PS) have been
greatly improved mostly due to extensive in-house operations, but also from suggestions and reports from users.  Most bugs found
were minor, but a few of them were critical.  All bugs we found have been fixed.  Updates, additions, and modifications incorporated
into the current version (v. 2.0) are explained
here.  For a brief introduction of the software features, click here.  To go to ParkSEIS
home page, click
here.  New features of v. 2.0 are presented here.  

ParkSEIS - Introduction Video
ParkSEIS provides the most up-to-date comprehensive tools in the history of MASW development.  The technical algorithms have evolved through the
last two decades of
author's career as developer and practitioner of MASW, making it the most robust and reliable MASW analysis tool available today.  
A brief introduction of the software features is presented
here.  More technical details of the software can be found online.  

Those characteristics of MASW software are discussed here that are critical to the reliability of final product of shear-wave velocity (Vs) profiles.

Key Features

Key elements of any MASW software should include following modules, as minimum, for (1) source/receiver (SR) setup, (2) dispersion image
generation, (3) dispersion-curve extraction, (4) inversion to generate 1-D shear-velocity (Vs) profiles, and (5) presentation of velocity (Vs) information
in 1-D and 2-D formats.  In addition, various types of special-analysis modules such as
back-scattering analysis and common-offset section generation
may be included for more advanced handling of surface wave data.

Different software may adopt slightly different algorithms in dispersion and inversion analyses.  

Dispersion Algorithm

For dispersion analysis, the phase-shift method by Park et al. (1998) is most commonly used after investigations by many researchers (e.g., Moro et
al., 2003) because of its proven high-resolution capability in comparison to other conventional methods such as f-k and tau-pi transformation methods
McMechan and Yedlin, 1981).  The high-resolution dispersion-imaging scheme is the most critical component of MASW analysis because of its
ability to discriminate different modes of plane waves that may include both body and surface waves travelling horizontally along the surface.  

Inversion Algorithm

For inversion analysis, the fundamental-mode (M0) generation algorithm (e.g., Schwab and Knopoff, 1972) is most commonly used.  Although there
has been a great deal of research and development in multi-mode utilization, software that takes full advantage of multi-mode while efficiently handling
all the associated complications (e.g.,
mode misidentification and mode mix) has not yet been developed. This is because of the fact that modal
identities of higher modes in reality cannot be uniquely determined.  In consequence, the higher-mode inversion methods generate results often less
reliable than those from the traditional fundamental-mode (M0) inversion method.  This is further illustrated in
this video (from 3:40 time line).  Yet, the
traditional approach of the fundamental-mode (M0), or an apparent mode (AM0), inversion provides an excellent outcome under most common near-
surface (overburden and bedrock) settings, and can provide a 1st-degree approximation of other more complex settings.     

Practical (Yet Critical) Algorithms

In addition to such obvious aspects of algorithms that are extensively described through major publications, there are also practical aspects of
software that are not publicly well addressed, yet can influence significantly on the final result of velocity (Vs) profiles.  They may include, but not
limited to, the way the following issues are addressed:

  • how the process to update model velocity (Vs) is optimized in inversion,
  • how computational artifacts are suppressed during the dispersion-image generation,
  • the way incoherent ambient noise is handled during the dispersion-image generation and subsequent curve (M0) extraction,
  • how the maximum depth of velocity (Vs) profile is determined,
  • the way the initial velocity (Vs and Vp), density, and thickness models are created,
  • how the apparent mode (AM0) is handled during inversion,
  • how the overburden/bedrock interface is detected during inversion, and
  • how non-uniqueness of inversion process is handled.

These practical aspects should be properly calibrated in the software throughout extensive research and development based on both theoretical and
empirical experiments using diverse data sets.