基于垂向一阶导数与解析信号比值的欧拉反演方法

常规欧拉反褶积法中构造指数的选取以及分散解存在较多的问题,提出了基于联立垂向一阶导数与解析信号的欧拉齐次方程的RDAS-Euler反演方法。该方法可以更为精确的估计场源的范围及埋深,且不需考虑构造指数N的影响,避免了因构造指数不当而引起的反演误差。通过对单一地质体及组合地质体模型的实验证明本文方法能有效地完成目标体的反演工作,反演结果与理论值之间的误差小于10%,且相对于常规欧拉反褶积法更加稳定准确,能够更好的得到地质体边界及深度信息。将RDAS-Euler法应用于黑龙江省虎林盆地实测布格重力异常数据,获得了丰富的断裂信息,说明RDASEuler法增强了对断裂平面位置的识别能力。

Improved Euler method for the interpretation of potential data based on the ratio of the vertical first derivative to analytic signal

We propose a new automatic method for the interpretation of potential field data, called the RDAS-Euler method, which is based on Euler’s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies (structural index). It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfit between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS-Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.