Critical Depths for Higher Modes by Minimally-invasive Multimodal Surface Wave (MMSW) Method: Simulations and Field Test

Abstract

A Minimally-invasive Multimodal Surface Wave (MMSW) geophysical testing method, which is a hybrid of surface and borehole seismic methods, was developed recently by the authors to measure more extensive multi-mode dispersion data and thus improve the accuracy of inversion profiles. The new MMSW method employs a borehole geophone at selected depths to record seismic waves from different source offsets on the soil surface. Presented in this paper is a procedure for estimating a range of optimum geophone depths to capture a given higher mode by the MMSW method. Stiffness matrix and finite-element-based numerical simulations of the MMSW method are performed to identify the relationships between critical geophone depths and apparent cutoff frequencies of higher modes. Specifically, it is shown for increasing velocity profiles that 1) at a given borehole sensor measurement depth, the apparent cutoff frequencies of higher modes increase with mode number, 2) at a given frequency, the critical geophone depth at which a higher mode will first become dominant increases with mode number, and 3) for a given higher mode, the apparent cutoff frequency decreases as measurement depth increases. A preliminary field test is conducted using a vertical geophone placed at five different depths while impacts are applied to the soil surface from 3.66 to 43.89 m from the borehole, with an impact spacing of 3.66 m. Dispersion images from the five geophone depths are superimposed to produce a dispersion image having three modes with improved clarity relative to the surface-only Multichannel Analysis of Surface Waves (MASW) method. A comparison of the experimental and theoretical apparent cutoff frequencies for higher modes is used to validate the theoretical prediction of critical depths by the stiffness matrix method. Matching of experimental and theoretical apparent cutoff frequencies could provide additional optimization constraints to reduce the uncertainty of final inversion profiles.