Seismic Refraction Data Analysis Using Machine Learning and Numerical Modeling for Characterization of Dam Construction Sites

Seismic refraction is a cost-effective tool to reveal subsurface P-wave velocity. Inversion of travel times for estimating a realistic velocity model is a significant step in the processing of seismic refraction data. The results of the seismic data inversion are stochastic and, thus, using prior information or complementary geophysical data can have a significant role in estimating the structural properties based on observed data. Nevertheless, sufficient prior information or auxiliary data are not available in many geophysical sites. In such situations, developing advanced computational modeling is a vital step in providing primary information and improving the results. To this aim, a new inversion framework through hybrid committee artificial neural networks (CANN) and the flower pollination (FP) optimization algorithm is introduced for inversion of refracted seismic travel times. Synthetic models generated by a forward modeling approach are used to train the machine learning model. Then, model parameters, such as the number of layers, thicknesses, and P-wave velocities, are predicted using a committee machine constructed based on several neural networks, which is achieved by averaging and stack generalization methods where the latter method provides a better result. Then, the CANN results are used in the FP inversion algorithm to estimate the final model as it provides essential prior information on the number of layers and model parameters, which can be used in the FP searching algorithm. The proposed inversion procedure is tested on different synthetic datasets and applied at a dam site to determine the number of layers and their thicknesses. Our findings indicate a successful performance on both synthetic and real data for automatic inversion of seismic refraction data.