Research civil engineer, Geotechnical Laboratory, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS
Assistant professor, School of Civil Engineering, Georgia Institute of Technology, Atlanta, GA
An artificial neural network is proposed as an expeditious alternative to the trial-and-error and least-squares surface wave inversion techniques that are currently available. To use an artificial neural network for surface wave inversion, synthetic dispersion curves are calculated for representative shear wave velocity profiles using a theoretical wave propagation algorithm. An artificial neural network is then “taught” to map these dispersion curves back into their respective shear wave velocity profiles. Once the network has been successfully trained on these synthetic dispersion curves, experimental dispersion curves can be inverted by passing them through the neural network. Because the neural network requires only a single forward pass of the data, it performs inversions much more quickly than iterative procedures.
To determine the feasibility of using an artificial neural network for surface wave inversion, a two-dimensional wave-propagation algorithm was used to create synthetic dispersion curves for 99 000 randomly generated, two-layer velocity profiles. A backpropagation neural network was then trained to associate the synthetic dispersion curves with their respective velocity profiles. The trained network was evaluated using synthetic dispersion curves for another 1000 randomly generated velocity profiles as surrogate experimental curves.
Paper ID: GTJ10282J