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This paper presents a frequency-velocity convolutional neural network (CNN)for rapid, non-invasive 2D shear wave velocity (Vs) imaging of near-surfacegeo-materials. Operating in the frequency-velocity domain allows forsignificant flexibility in the linear-array, active-source experimental testingconfigurations used for generating the CNN input, which are normalizeddispersion images. Unlike wavefield images, normalized dispersion images arerelatively insensitive to the experimental testing configuration, accommodatingvarious source types, source offsets, numbers of receivers, and receiverspacings. We demonstrate the effectiveness of the frequency-velocity CNN byapplying it to a classic near-surface geophysics problem, namely, imaging atwo-layer, undulating, soil-over-bedrock interface. This problem was recentlyinvestigated in our group by developing a time-distance CNN, which showed greatpromise but lacked flexibility in utilizing different field-testingconfigurations. Herein, the new frequency-velocity CNN is shown to havecomparable accuracy to the time-distance CNN while providing greaterflexibility to handle varied field applications. The frequency-velocity CNN wastrained, validated, and tested using 100,000 synthetic near-surface models. Theability of the proposed frequency-velocity CNN to generalize across variousacquisition configurations is first tested using synthetic near-surface modelswith different acquisition configurations from that of the training set, andthen applied to experimental field data collected at the Hornsby Bend site inAustin, Texas, USA. When fully developed for a wider range of geologicalconditions, the proposed CNN may ultimately be used as a rapid, end-to-endalternative for current pseudo-2D surface wave imaging techniques or to developstarting models for full waveform inversion.

A Frequency-Velocity CNN for Developing Near-Surface 2D Vs Images from Linear-Array, Active-Source Wavefield Measurements