From SASW to MASW: A Technical Comparison of Multi-Channel Surface Wave Analysis

The evaluation of subsurface geological and structural properties relies heavily on the analysis of seismic surface waves, primarily Rayleigh and Love waves. Spectral Analysis of Surface Waves (SASW) emerged in the early 1980s through the pioneering work of Kenneth Stokoe and Soheil Nazarian at the University of Texas at Austin. This non-invasive method utilized a pair of receivers to measure the phase velocity of surface waves, allowing engineers to infer the shear-wave velocity profiles of soil and pavement systems without the need for boreholes. By the late 1990s, the methodology evolved significantly with the introduction of Multi-channel Analysis of Surface Waves (MASW), a technique formalized by researchers at the Kansas Geological Survey in 1999.

Surface wave methods exploit the dispersive nature of Rayleigh waves in layered media, where different frequencies travel at different velocities based on the stiffness of the materials at various depths. While SASW provided a foundational framework for this discipline, MASW addressed many of its inherent limitations by employing multi-channel geophone arrays and advanced wavefield transformation algorithms. These advancements have made surface wave analysis a standard practice in geotechnical engineering, earthquake site characterization, and non-destructive testing of critical infrastructure.

What changed

• Receiver Configuration:The transition from the two-receiver setup of SASW to the multi-channel arrays (typically 12 to 48 geophones) of MASW allowed for simultaneous data acquisition across a broader spatial range.
• Signal Processing:MASW replaced the manual phase unwrapping of spectral data used in SASW with automated wavefield transformation techniques, such as the f-k (frequency-wavenumber) or phase-shift methods.
• Noise Management:Multi-channel recording introduced the ability to use spatial redundancy, effectively filtering out incoherent noise and identifying non-planar wave energy that often contaminated SASW measurements.
• Higher Mode Identification:The use of arrays enabled the clear separation of fundamental and higher-mode Rayleigh waves, which is critical for accurate inversion in complex stratigraphies.
• Operational Efficiency:Data acquisition time was reduced from several hours per site to minutes, as a single impact source could capture the necessary dispersion information for the entire target depth.

Background

The theoretical basis for surface wave analysis dates back to the late 19th century with Lord Rayleigh’s mathematical description of waves propagating along the free surface of an elastic solid. These waves, now known as Rayleigh waves, involve a combination of longitudinal and transverse motion that decays exponentially with depth. In a perfectly homogeneous half-space, Rayleigh waves are non-dispersive; however, Earth’s subsurface is rarely homogeneous. Because geological strata vary in density and elasticity, the velocity of these waves becomes frequency-dependent—a phenomenon known as dispersion.

In the mid-20th century, the oil and gas industry began utilizing seismic waves for deep exploration, but the shallow subsurface remained largely the domain of invasive testing like Standard Penetration Tests (SPT) or Cone Penetration Tests (CPT). The development of SASW in the 1980s represented a major change, offering a way to characterize the upper 30 meters of the soil profile (Vs30) through non-destructive surface measurements. This was particularly vital for urban environments where drilling was prohibited or for pavement assessment where preserving structural integrity was critical. The subsequent refinement into MASW by Choon Park, Rick Miller, and Jianghai Xia at the Kansas Geological Survey provided the technical robustness required for widespread industrial adoption.

Methodology of SASW: The Two-Receiver Era

The SASW method functions by placing a seismic source (such as a sledgehammer or drop weight) in line with two receivers. The distance between the source and the first receiver, as well as the distance between the two receivers, is typically kept equal and incremented in a geometric progression. For each spacing, the time-history data from both receivers are transformed into the frequency domain using a Fast Fourier Transform (FFT). The cross-power spectrum is then calculated to determine the phase difference between the receivers for each frequency component.

The primary challenge in SASW is "phase unwrapping." Because the phase difference is measured in cycles, researchers must manually or semi-automatically determine the correct integer number of cycles to calculate the actual travel time. This process is highly susceptible to error, particularly in noisy environments or sites with complex layering. Furthermore, SASW assumes that the captured signal consists entirely of the fundamental mode of the Rayleigh wave. If body waves or higher-mode surface waves are present, they interfere with the phase measurements, leading to inaccurate dispersion curves. To mitigate these issues, practitioners often performed multiple tests with varying receiver spacings and source offsets, a labor-intensive process that required significant expertise to interpret correctly.

The MASW Revolution: Wavefield Transformation

The introduction of MASW in 1999 addressed the instability of SASW by recording the entire wavefield across an array of geophones. Rather than looking at the phase difference between two points, MASW analyzes the phase relationships across the entire array. This is achieved through wavefield transformation, which maps the time-space (t-x) domain data into the frequency-velocity (f-v) or frequency-wavenumber (f-k) domain. In this transformed space, the dispersion energy appears as distinct peaks or trends, allowing the operator to visually distinguish between different modes of propagation and various types of noise.

One of the most significant advantages of MASW is its inherent ability to filter out "cultural noise" (traffic, machinery) and scattered energy from subsurface anomalies. Because the signal is recorded at multiple points, random noise tends to cancel out during the transformation process, while the coherent surface wave signal is amplified. This spatial redundancy provides a much higher signal-to-noise ratio (SNR) compared to the two-channel approach. Additionally, the wavefield transformation eliminates the need for phase unwrapping, as the velocity is determined directly from the energy maxima in the dispersion image.

Technical Comparison: SASW vs. MASW

A rigorous comparison of these methods reveals why MASW has largely superseded SASW in professional practice, though SASW remains relevant for certain high-frequency pavement applications. The following table summarizes the key technical differences based on performance metrics observed in field studies, including those conducted by the United States Geological Survey (USGS).

Signal-to-Noise Ratios and Processing Efficiency

Field data published in USGS reports and geotechnical journals highlight the disparity in signal quality between the two methods. In a standard urban environment, a two-receiver SASW setup often yields a coherence function that drops significantly below 0.9 at frequencies below 10 Hz, rendering the data unreliable for deep profiling. In contrast, MASW arrays effectively stack the signal across 24 or more channels, maintaining high data integrity even in the presence of continuous background traffic. The processing efficiency of MASW is also notably higher; while an experienced geophysicist might spend an hour unwrapping the phase of a single SASW site, MASW software can generate a preliminary dispersion curve in seconds.

The mathematical robustness of MASW also allows for better handling of the "near-field effect." This occurs when the source is too close to the receivers, causing the wavefield to be dominated by non-planar energy that does not follow the theoretical dispersion model. MASW allows researchers to identify and exclude near-field geophones from the calculation without re-running the entire test, a flexibility that SASW lacks.

Inversion Algorithms and Subsurface Imaging

Both methods require an inversion process to convert the observed dispersion curve (velocity vs. Frequency) into a shear-wave velocity profile (Vs vs. Depth). This is an iterative process where an initial theoretical model of the earth is adjusted until its calculated dispersion curve matches the field observations. Because MASW provides a clearer and more complete dispersion curve, including higher modes, the inversion process is better constrained. Higher modes are particularly important in sites where stiffness increases and then decreases with depth (velocity inversions), such as a hard pavement over a soft subgrade. SASW often fails to resolve these features accurately, whereas MASW can use higher-mode data to reduce the non-uniqueness of the inversion solution.

Applications in Modern Engineering

The practical application of surface wave hub disciplines extends into various sectors of civil and environmental engineering. MASW is the preferred method for determining the site class for seismic design as per the International Building Code (IBC), which requires the average shear-wave velocity in the top 30 meters. It is also used extensively for detecting subsurface anomalies such as sinkholes, abandoned mines, and buried utilities. In these applications, the meticulous interpretation of microtremor (passive MASW) or controlled source (active MASW) data allows for the creation of 2D cross-sections of the subsurface.

For infrastructure health monitoring, surface wave analysis is applied to bridges and dams. By analyzing the dispersion curves of induced waves, engineers can detect internal delamination or changes in material porosity that signify structural degradation. The precision of modern accelerometers and geophones allows for the detection of subtle ground-motion signatures, providing a non-destructive look into the elastic moduli and density of engineered materials. This level of lithological characterization is essential for ensuring the long-term stability of foundations and tunnels in heterogeneous solid-state media.

While MASW is the dominant tool for geotechnical scales, the legacy of SASW continues in specialized high-frequency testing. For thin-layered systems like asphalt pavements, the high-frequency resolution of two-channel setups can sometimes exceed that of standard MASW arrays, provided the environment is sufficiently controlled. However, for the vast majority of geological and structural investigations, the shift to multi-channel analysis represents the definitive standard for accuracy and reliability in the empirical study of acoustic wave propagation.