Optimal Recovery of Rayleigh-Wave Overtones by Multi-Directional Acquisition

Rayleigh waves are ubiquitously used for subsurface characterization through dispersion curve inversion, whose quality depends on the number of useable overtones. Traditional analysis is based on vertical receivers and, for active surveys, sources. However, for layered media, eigenfunction theory shows that optimal recovery of any given dispersion mode and frequency can be achieved by either vertical source-vertical receiver or radial source-radial receiver configurations, with source directivity being more dominant. Multi-directional near-surface wave-equation modeling and field examples, including distributed acoustic sensing data, validate these predictions. Statistical analysis of compaction-type near-surface models shows that overtones are better recovered for radial-radial surveys in large portions of the useable spectrum, extending beyond 60% for the second and above modes. We conclude that incorporating radial-radial data acquisition is beneficial and should become standard procedure in active surveying, as well as analyzing the radial component in ambient noise and earthquake-induced Rayleigh waves. Surface waves are a type of seismic wave that are often utilized for near-surface characterization in a wide variety of fields. However, the quality of their recording is highly variable between different sites. By utilizing sources and receivers that generate and record energy in different directions, we can better excite these surface waves and facilitate their subsequent analysis. We show the benefits of directional surveys through mathematical, simulative and field data examples, and claim that they should become a widespread procedure in all fields relying on surface-wave surveys. Radial and vertical directivity of sources and receivers strongly influences excitation of different Rayleigh-wave dispersion modesEigenfunction theory predicts the effect of survey directivity on recorded Rayleigh waves, also critical for distributed acoustic sensingStatistical analysis of near-surface models shows significant improvement of overtone recovery by adding radial-radial acquisition