Automatic Detection of UXO from Airborne Magnetic Data Using a Neural Network
Automatic Detection of UXO from Airborne Magnetic Data Using a Neural Network
复制标题
使用神经网络从机载磁性数据自动检测未爆炸弹药
DOI：
10.3997/2214-4609-pdb.192.ux1_1
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
K. Ushijima
中科院分区：
文献类型：
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作者：
A. Salem;K. Ushijima
机载磁性探测系统的最新发展使得探测小型铁金属物体成为可能。然而，机载磁数据可能非常大，因此，越来越需要一种全自动解释技术，可用于实时做出有关现场源性质的决策。人工神经网络的大规模并行处理优势使其适合硬件实现；因此，将这些网络与磁性系统结合使用有可能大大加快铁金属物体的检测速度。在本文中，我们开发了一种使用 Hopfield 神经网络应用于机载磁数据来检测和表征未爆炸弹药 (UXO) 的新方法。 Hopfield 网络用于优化代表规则位置磁性物体的偶极源的磁矩。对于每个位置，Hopfield 网络都达到其稳定的能量状态。物体的位置对应于产生最小霍普菲尔德能量的位置。输出结果包括二维位置（水平位置和深度）、磁矩和有效倾角。理论和现场实例表明Hopfield网络是准确、客观的未爆炸弹药检测工具。此外，由于 Hopfield 神经网络是一种自然的模数转换器，因此非常适合集成到机载磁性仪器系统中。
Recent developments in airborne magnetic detection systems have made it possible to detect small ferro-metallic objects. However, airborne magnetic data can be really large and, therefore, there is an increasing need for a fully automatic interpretation technique that could be used to make decisions regarding the nature of the sources in the field in real time. The massively parallel processing advantage of artificial neural networks makes them suitable for hardware implementations; therefore, using these networks in conjunction with a magnetic system has the potential to greatly speed up the detection of ferro-metallic objects. In this paper, we have developed a new method for detection and characterization of unexploded ordnance (UXO) using a Hopfield neural network as applied to airborne magnetic data. The Hopfield network is used to optimize the magnetic moment of a dipole source representing the magnetic object at regular locations. For each location, the Hopfield network reaches its stable energy state. The location of the object corresponds to the location yielding the minimum Hopfield energy. Output results include position in two dimensions (horizontal location and depth), magnetic moment, and effective inclination. Theoretical and actual field examples show that the Hopfield network is accurate and objective tool for the detection of UXO. Moreover, because the Hopfield neural network is a natural analog-to-digital converter, it is ideally suited for incorporation into airborne magnetic instrumentation systems.
罗伯逊，A.：“地中海海脊的泥火山活动”地质学 24·239-242（1996）。
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