Position detection of unexploded ordnance from airborne magnetic anomaly data using 3-D self organized feature map

The detection of any buried object depends on the analysis of its emerged magnetic anomaly data. In this paper, the self-organized feature map (SOFM) neural network is used to detect the position of the unexploded ordnance (UXO) from airborne magnetic anomaly data. The SOFM is an unsupervised-competitive neural network that can learn a topology-preserving mapping from a high dimensional input space to a lower dimensional map lattice. The SOFM was trained using two-dimensional theoretical magnetic signatures of an equivalent UXO dipole source in different X, Y, and Z coordinates. The feature map is designed as a three-dimensional SOFM, where each dimension in this feature map is used to detect one coordinate of the UXO position. The experimental results showed that the SOFM could accurately recognize the UXO position from both simulated magnetic anomaly data and actual airborne magnetic field data. The network could also recognize the UXO position correctly when tested with noisy magnetic anomaly data up to +/-15%. It is concluded that the SOFM is an efficient, fast, and accurate technique for position detection of the UXO buried objects

[1]  Mansor Nakhkash,et al.  Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition , 2000 .

[2]  A. Reid,et al.  Euler Deconvolution: Past, Present, And Future-A Review , 1995 .

[3]  William C. Pearson,et al.  Aeromagnetic structural interpretation using neural networks: A case study from the Northern Denver‐Julesberg basin , 1990 .

[4]  I. W. Somerton,et al.  Magnetic interpretation in three dimensions using Euler deconvolution , 1990 .

[5]  Geology From Geophysics; Or, Converting Magnetic Data to Depth, Dip, And Susceptibility Contrast Using the Source Parameter Imaging (SPI TM ) Method. , 1996 .

[6]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[7]  R. Blakely.  Potential theory in gravity and magnetic applications , 1996 .

[8]  Dhananjay Ravat,et al.  Magnetic properties of unrusted steel drums from laboratory and field-magnetic measurements , 1996 .

[9]  Misac N. Nabighian,et al.  The analytic signal of two-dimensional magnetic bodies with polygonal cross-section; its properties and use for automated anomaly interpretation , 1972 .

[10]  Mary M. Poulton,et al.  Location of subsurface targets in geophysical data using neural networks , 1992 .

[11]  K. V. Paulson,et al.  Magnetotelluric inversion using regularized Hopfield neural networks , 1997 .

[12]  P. Taylor,et al.  Determination of depths to centroids of three-dimensional sources of potential-field anomalies with examples from environmental and geologic applications , 1998 .

[13]  D. T. Thompson,et al.  EULDPH: A new technique for making computer-assisted depth estimates from magnetic data , 1982 .

[14]  D. Ravat.  Analysis of the Euler Method and Its Applicability in Environmental Magnetic Investigations , 1996 .

[15]  Y. Ashida.  Data processing of reflection seismic data by use of neural network , 1995 .

[16]  Zehui Huang,et al.  Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada , 1996 .

[17]  Richard S. Smith,et al.  Automatic conversion of magnetic data to depth, dip, and susceptibility contrast using the SPI (TM) method , 1997 .

[18]  Walter R. Roest,et al.  Magnetic interpretation using the 3-D analytic signal , 1992 .

[19]  Mary M. Poulton,et al.  Locating Buried Objects for Environmental Site Investigations Using Neural Networks , 1996 .