Presents a technique for detecting conductivity anomalies in sediments, e.g., a buried object in sedimentary layers under sea water, by using the neural network approach. The electric field values are used as the inputs to the neural network and the associated conductivities are treated as the targets. The neural network is then trained to associate these conductivities and field values. It is shown that a trained neural network can be used to estimate the conductivity of new objects that were not employed originally to train the network.