Location of subsurface targets in geophysical data using neural networks

Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampled image, the peak and adjacent troughs, the peak, and components from a two‐dimensional (2-D) fast Fourier transform. The paradigms tested were standard back propagation, directed random search, functional link, extended delta bar delta, and the hybrid combination of self‐organizing map and back propagation. For input patterns with less than 100 elements, the directed random search and functional link networks gave ...

[1]  S. Y. Kung,et al.  An algebraic projection analysis for optimal hidden units size and learning rates in back-propagation learning , 1988, IEEE 1988 International Conference on Neural Networks.

[2]  Mary M. Poulton,et al.  Neural network pattern recognition of subsurface EM images , 1992 .

[3]  David Hillman,et al.  Integrating neural nets and expert systems , 1990 .

[4]  Jeffrey L. Baldwin,et al.  Application Of A Neural Network To The Problem Of Mineral Identification From Well Logs , 1990 .

[5]  Robert M. Pap,et al.  Handbook of neural computing applications , 1990 .

[6]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[7]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[8]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[9]  Geoffrey E. Hinton,et al.  A general framework for parallel distributed processing , 1986 .

[10]  A. C. Tsoi.  Multilayer perceptron trained using radial basis functions , 1989 .

[11]  Bernardo A. Huberman,et al.  AN IMPROVED THREE LAYER, BACK PROPAGATION ALGORITHM , 1987 .

[12]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[13]  T. Kohonen,et al.  Statistical pattern recognition with neural networks: benchmarking studies , 1988, IEEE 1988 International Conference on Neural Networks.

[14]  Mohamad T. Musavi,et al.  A neural network approach to character recognition , 1989, Neural Networks.

[15]  H. Frank Morrison,et al.  ELECTROMAGNETIC DEPTH SOUNDING EXPERIMENT ACROSS SANTA CLARA VALLEY , 1972 .

[16]  D. T. Biewinga.  ELECTROMAGNETIC DEPTH SOUNDING EXPERIMENT , 1977 .

[17]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[18]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[19]  Dean Pomerleau,et al.  ALVINN, an autonomous land vehicle in a neural network , 2015 .

[20]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[21]  A. Khotanzad,et al.  Distortion invariant character recognition by a multi-layer perceptron and back-propagation learning , 1988, IEEE 1988 International Conference on Neural Networks.

[22]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[23]  R. Watts,et al.  ELECTROMAGNETIC SCATTERING FROM BURIED WIRES , 1978 .

[24]  Jay J. Pulli,et al.  Regional seismic event classification at the NORESS array: Seismological measurements and the use of trained neural networks , 1990 .

[25]  Lawrence Davis,et al.  Mapping Classifier Systems Into Neural Networks , 1988, NIPS.

[26]  Ben K. Sternberg,et al.  Rapid, high-accuracy electromagnetic soundings using a novel four-axis coil to measure magnetic field ellipticity , 1993 .

[27]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Norio Baba,et al.  A new approach for finding the global minimum of error function of neural networks , 1989, Neural Networks.

[29]  Teuvo Kohonen,et al.  Statistical pattern recognition with neural networks , 1988, Neural Networks.

[30]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[31]  A. Lapedes,et al.  Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .

[32]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[33]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[34]  Jean-Bernard Theeten,et al.  Neural Approach for TV Image Compression Using a Hopfield Type Network , 1988, NIPS.

[35]  Scott J. Thomas,et al.  Forward modeling and data acquisition for high-accuracy electromagnetic subsurface imaging , 1989 .

[36]  G. O. Stone,et al.  An analysis of the delta rule and the learning of statistical associations , 1986 .

[37]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[38]  Kishan G. Mehrotra,et al.  Bounds on the number of samples needed for neural learning , 1991, IEEE Trans. Neural Networks.

[39]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[40]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[41]  Terrence J. Sejnowski,et al.  NETtalk: a parallel network that learns to read aloud , 1988 .

[42]  Madan M. Gupta.  Fuzzy logic and neural networks , 1992, [Proceedings 1992] IEEE International Conference on Systems Engineering.

[43]  Terrence J. Sejnowski,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cognitive Sciences.

[44]  Nam Ha Bak.  Development of an advanced electromagnetic subsurface imaging system and interpretation of data , 1991 .

[45]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[46]  Ben K. Sternberg.  High-accuracy, simultaneous calibration of signal measuring systems , 1990 .

[47]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[48]  D. O. Hebb,et al.  The organization of behavior , 1988 .