应用加强解析信号倾斜角进行位场数据的边界检测

边界检测在地球物理位场数据解释中占有重要位置.现有的传统边界识别方法有的不能同时显示不同振幅的异常边界,有的虽然能均衡不同振幅的异常,但识别出来的边界信息中含有一些额外的错误的边界信息,尤其是当测量的异常中同时含有正异常和负异常时.目前已有的去除额外错误边界信息的方法存在着一定的人为主观性.为了解决这些问题,本文定义了加强解析信号倾斜角来进行地质体边界识别.通过模型试验证明了该方法不仅能同时清晰地识别深部和浅部地质体的边界,而且能有效地避免引入一些错误边界信息.最后将该方法应用到四川盆地的重力异常数据中,并取得了良好边界结果.

Edge detection of potential field data using an enhanced analytic signal tilt angle

Edge detection plays an important role in the interpretation of potential field data, and has been used in exploration of mineral resources, energy resources and regional tectonics. The main geological edges are fault lines and the boundaries of geological or rock bodies with different density, magnetism and other physical properties. #br#Many traditional methods are employed to outline the edges. Most of them are based on the horizontal derivatives and vertical derivatives of potential field data, such as total horizontal derivatives, analytic signal, and so on. However, all of them cannot equalize the amplitude of the edges of shallow and deep geological bodies simultaneously. #br#In order to equalize the amplitude of the edges, some balanced filters are proposed. Although they can balance the amplitude, they may bring some additional false edge information, especially when the measured anomalies contain both positive and negative anomalies simultaneously. Some scholars proposed a method to avoid this disadvantage. They bring a constant number in the normalization factor to remove the extra error boundary information. However, it has a certain subjectivity. #br#To solve this problem, we present a new method called enhanced analytic signal tilt angle to delineate geological bodies. This new method is tested on synthetic gravity anomalies, which shows that it can not only identify the edges of shallow and deep geological bodies clearly and precisely, but also avoid bringing some additional false edges. To further test the stability, we demonstrate the new edge detectors with model data corrupted with 10% Gaussian noise, which shows that our method needs to filter the noisy data before detecting the edges. Finally, we apply the new method to real measured gravity data in the Sichuan basin, China, obtaining good results.