ParkSEIS v3.0 is the current major release of Park Seismic LLC’s MASW (Multi-channel Analysis of Surface Waves) software, with the significant AUTO module added 

Key Features of v3.0

AUTO Module (Fully Automated Processing)
The headline feature of v3.0 is the “AUTO” module, which provides a fully automated sequence for MASW data analysis. After importing seismic data with proper acquisition geometry encoded, users can generate 1D or 2D shear-wave velocity (Vs) profiles with one click . The algorithm uses fundamental-mode (M0) pattern recognition developed over three decades, automating dispersion image preparation and dispersion curve extraction . A dataset of 500 records (24-channel) can produce a 2D Vs cross-section in less than 30 minutes on standard hardware .

Core Capabilities
ParkSEIS generates shear-wave velocity (Vs) information in multiple formats :

  • 1D Vs profiles (depth profiles)

  • 2D Vs cross-sections (depth and surface)

  • Depth slices

  • Back Scattering Analysis (BSA) for anomaly detection (voids, tunnels, boulders, weak zones)

  • MASW modeling (seismic data from layered-earth models)

It supports active, passive, and combined active/passive MASW surveys, and can process data from both MASW and refraction surveys .

Seismic Site Characterization
The 1D Vs display module includes site characterization based on the top 30m (100 ft) of shear wave velocity, supporting both NEHRP (USA) and Eurocode standards .

Version Options
Three editions were available at release: Version 3.0 (without AUTO), AUTO (full AUTO function), and AUTO Lite (limited AUTO function) .

Licensing & Availability

ParkSEIS is available for purchase or lease. A purchased license comes on a USB dongle for transferring between computers, with one year of technical support and maintenance included. A one-week demo license is available for a fee, credited toward future purchase .

Important Note on AUTO Results

Park Seismic recommends that even experienced users run both manual and AUTO approaches to cross-check results, as the AUTO process may not be perfect for data with extraordinary complications .