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Browsing by Author "Vantassel, Joseph P."

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    A frequency-velocity CNN for developing near-surface 2D vs images from linear-array, active-source wavefield measurements
    Abbas, Aser; Vantassel, Joseph P.; Cox, Brady R.; Kumar, Krishna; Crocker, Jodie (Elsevier, 2023-04)
    This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (VS) imaging of near-surface geo-materials. Operating in the frequency-velocity domain allows for significant flexibility in the linear-array, active-source experimental testing configurations used for generating the CNN input, which are normalized dispersion images. While normalized dispersion images retain the most important aspects of near-surface wavefields, they are relatively insensitive to the exact experimental testing configuration used to generate and record the wavefields, accommodating various source types, source offsets, numbers of receivers, and receiver spacings. We demonstrate the effectiveness of the frequency-velocity CNN by applying it to a common near-surface geophysics problem, namely, imaging a two-layer, undulating, soil-over -bedrock interface. The frequency-velocity CNN was trained and tested using 100,000 synthetic near-surface models with variable soil-over-bedrock conditions. Then, the ability of the frequency-velocity CNN to gener-alize across various acquisition configurations was rigorously tested using thousands of synthetic near-surface models with different acquisition configurations from that of the training set. Lastly, it was applied to experi-mental field data collected at the Hornsby Bend site in Austin, Texas, USA and found to produce a subsurface 2D image that was in great agreement with ground truth from invasive site characterization data.
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    Near-Surface 2D Imaging via FWI of DAS Data: An Examination on the Impacts of FWI Starting Model
    Yust, Michael B. S.; Cox, Brady R.; Vantassel, Joseph P.; Hubbard, Peter G.; Boehm, Christian; Krischer, Lion (MDPI, 2023-02-23)
    Full waveform inversion (FWI) and distributed acoustic sensing (DAS) are powerful tools with potential to improve how seismic site characterization is performed. FWI is able to provide true 2D or 3D images of the subsurface by inverting stress wave recordings collected over a wide variety of scales. DAS can be used to efficiently collect high-resolution stress wave recordings from long and complex fiber optic arrays and is well-suited for large-scale site characterization projects. Due to the relative novelty of combining FWI and DAS, there is presently little published literature regarding the application of FWI to DAS data for near-surface (depths <30 m) site characterization. We perform 2D FWI on DAS data collected at a well-characterized site using four different, site-specific 1D and 2D starting models. We discuss the unique benefits and challenges associated with inverting DAS data compared to traditional geophone data. We examine the impacts of using the various starting models on the final 2D subsurface images. We demonstrate that while the inversions performed using all four starting models are able to fit the major features of the DAS waveforms with similar misfit values, the final subsurface images can be quite different from one another at depths greater than about 10 m. As such, the best representation(s) of the subsurface are evaluated based on: (1) their agreement with borehole lithology logs that were not used in the development of the starting models, and (2) consistency at shallow depths between the final inverted images derived from multiple starting models. Our results demonstrate that FWI applied to DAS data has significant potential as a tool for near-surface site characterization while also emphasizing the significant impact that starting model selection can have on FWI results.