Engineering topics
Publications and source records attributed to Toomarian, N. B..
Each material has unique spectral distribution that can be viewed as its fingerprint. Advanced hyper and ultra spectral sensors will produce large amount of data. Therefore, advanced capabilities are required that can effectively deal with large data cubes, possibly in real time.
This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. Our approach relies on JPL's adjoint operators general purpose neural network code to determine the best suited architecture. We believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters.
To this date, the most successful approaches to learning have been either the back-propagation or gradient descent method.