基于经验模态分解的重力异常分离

Gravity anomaly separation based on empirical mode decomposition

根据重力异常水平梯度特性,建立了基于经验模态分解的新型重力异常分离方法。通过不同模型实验对比,发现该方法分离所得异常和模型理论异常能很好地吻合,可进行异常定量划分。实测数据处理中,将该方法与平均对数功率谱分析相结合,所分离出的区域异常可以有效描述研究区的区域地质特征。模型试验和实测数据处理都充分表明该方法所分离出的重力异常基本没有畸变和虚假信息产生,具有良好的实际应用价值。

Based on empirical mode decomposition（EMD） method,the authors built a new gravity anomaly separation method according to the horizontal gradient characteristics of gravity anomaly.Through experiments and contrast of different model,the anomaly separated by this method can match well with model theoretical anomaly and carry on the quantitative division of anomaly.In measured data processing,combined this method with the analysis of average logarithm power spectrum,the separated regional anomaly can describe the regional geological characteristics effectively in the studied area.Both of the model experiment and measured data processing indicated that the gravity anomaly separated by this method does not have distortion and false information,which is valuable for practical application.