  Non-invasive subsurface imaging using full waveform inversion (FWI) has the
potential to fundamentally change engineering site characterization by enabling
the recovery of high resolution 2D/3D maps of subsurface stiffness. Yet, the
accuracy of FWI remains quite sensitive to the choice of the initial starting
model due to the complexity and non-uniqueness of the inverse problem. In
response, we present the novel application of convolutional neural networks
(CNNs) to transform an experimental seismic wavefield acquired using a linear
array of surface sensors directly into a robust starting model for 2D FWI. We
begin by describing three key steps used for developing the CNN, which include:
selection of a network architecture, development of a suitable training set,
and performance of network training. The ability of the trained CNN to predict
a suitable starting model for 2D FWI was compared against other commonly used
starting models for a classic near-surface imaging problem; the identification
of an undulating, two-layer, soil-bedrock interface. The CNN developed during
this study was able to predict complex 2D subsurface images of the testing set
directly from their seismic wavefields with an average mean absolute percent
error of 6%. When compared to other common approaches, the CNN approach was
able to produce starting models with smaller seismic image and waveform
misfits, both before and after FWI. The ability of the CNN to generalize to
subsurface models which were dissimilar to the ones upon which it was trained
was assessed using a more complex, three-layered model. While the predictive
ability of the CNN was slightly reduced, it was still able to achieve seismic
image and waveform misfits comparable to the other commonly used starting
models. This study demonstrates that CNNs have great potential as a tool for
developing good starting models for FWI ...
