Neural network methods in hydrodynamic yield estimation

Hydrodynamic theory allows us to use the speed of a shock wave front to determine the yield of an explosion. On the basis of this theory we developed a neural network to estimate a yield of underground explosions from the shock wave radius versus time (RVT) data, as measured by continuous reflectometry for radius versus time experiments (CORRTEX). The proposed method not only replaces the subjective elements of conventional algorithms, but produces significantly improved yield estimates. The network was trained with real hydrodynamic data and its performance on unseen test events was studied. A backpropagation network was employed; the architecture consisted of ten input units, a hidden layer with eleven hidden units, and one output unit. The network was trained with thousands of input-output measurement vectors, the feasible input set, derived from the RVT data from only four other training or standard events (selected on the basis of the given RVT data from the unknown event). The feasible input vectors were propagated through the trained network and the network output was used to select the optimum yield estimate. Elements of the input vector were: center of energy (COE) offsets, shock front radii, and time onset and interval of analysis for both the standard and unknown events. We studied the performance of the proposed system using 24 Nevada Test Site (NTS) events that were located in the geologic medium tuff. Sensitivity analysis of the proposed method to the assumed nominal COE offset is discussed. Variations of the proposed system that might lead to further improvements in performance are suggested.