利用人工神经网络推断曲线间的空间关系

Reasoning of Spatial Relation Between Curves by Neural Network

在利用综合信息进行矿产预测中 ,航磁和重力图件一阶导数极值线 (曲线 )分别反映了地质体的磁性和密度断裂 ,为了推断不同深度地质体的断裂构造、产状和规模 ,需要对不同延拓高度、不同方向的航磁和重力曲线进行比较 ,以推断它们之间的空间关系是否相似 ,只有相似的曲线所反映的断裂才是可靠的 .为了能够自动进行这种推理分析 ,给出了曲线间空间关系推理的神经网络方法 ,并且与传统的曲线间空间关系的判断方法进行了比较 .结果表明 ,利用神经网络方法进行曲线间空间关系分析所得到的判断结果与权威地质专家的判断非常吻合 。

In the field of synthetic information forecast for mineral product, extremum lines (curves) of the first order differential coefficient of aeromagnetic and gravitational maps reflect the ruptures of magnetism and the densities of geologic bodies respectively. In order to deduce the rupture conformation, the similarities of attitude and size of geologic bodies in different deepness, curves of aeromagnetic and gravitational maps in different deepness and directions must be compared; only those ruptures of reflection with similar curves are reliable. In order to deduce and analyze the situation automatically, in this paper, we first introduced the conventional estimation approach of spatial relation between curves. Later on, we also described the Neural Networks approach of reasoning of spatial relation between curves. The result indicate that these estimations of spatial relations between curves obtained by the Neural Networks approaches matched perfectly with the estimations of authoritative geological experts, and achieved triumphs in practical applications.