Detection of cavities and tunnels from gravity data using a neural network

Abstract

We have developed a simple approach to determine the depth and radius of subsurface cavities from microgravity data. The horizontal location of cavity centre is picked up as the projection of the minimum of gravity anomaly. Depth to the cavity centre is estimated using a back propagation neural network. The cavity radius can be then calculated using the determined parameters if the density contrast between the host rock and the cavity filling materials is known or assumed according to the geological background. The present method is tested using synthetic data and was able to determine the cavity parameters in the presence of noise. The method was also tested using field data measured over known cavities in the Medford cavity site, Florida USA. The estimated cavity parameters agree with the borehole results. The results show that the method is promising for estimating the parameters of cavities and tunnels from microgravity data.