【摘要】 针对目前地下小型磁目标形状识别局限于磁测数据的反演,受测量精度影响大,识别效果不理想的问题,提出了基于磁梯度张量和支持向量机的地下磁目标模式识别方法。该方法将机器学习的方法引入地下磁目标识别领域,利用量子粒子群改进的支持向量机(QPSO-SVM)识别地下小目标的形状。同时从样本信号中计算并分离出基于磁梯度张量矩阵的9个特征量联合识别磁目标,并对磁异常数据进行化极和延拓处理,提高了数据质量,使数据特征更突出。仿真和实验结果证明,本方法克服了重磁数据正、反演过程中大量的公式推导和计算,降低了对磁测数据精度的依赖,提高了识别正确率。更多还原

【Abstract】 In view of the fact that the shape recognition of underground small magnetic targets is limited to the inversion of magnetic data, and the influence of measurement accuracy is large, and the recognition effect is not ideal, a magnetic gradient tensor and support vector machine based underground magnetic target pattern recognition method was proposed. This method introduced the machine learning method into the field of underground magnetic target recognition, and used the Quantum Particle Swarm Optimization Support Vector Machine(QPSO-SVM) to identify the shape of the underground small target. At the same time, the nine feature quantities based on the magnetic gradient tensor matrix were calculated and separated from the sample signal to jointly identify the magnetic target, and the magnetic anomaly data was subjected to the polarization and extension processing, which improved the data quality and made the data features more prominent. The simulation and experimental results showed that the method overcome a large number of formula derivation and calculation in the process of gravity and magnetic data inversion and inversion, which reduced the dependence on the accuracy of magnetic measurement data and improved the recognition accuracy.更多还原