Edge detection in the potential field using the correlation coefficients of multidirectional standard deviations

利用各方向均方差相关系数进行位场边界检测（英文）

Most edge-detection methods rely on calculating gradient derivatives of the potential field, a process that is easily affected by noise and is therefore of low stability. We propose a new edge-detection method named correlation coefficient of multidirectional standard deviations（CCMS） that is solely based on statistics. First, we prove the reliability of the proposed method using a single model and then a combination of models. The proposed method is evaluated by comparing the results with those obtained by other edge-detection methods. The CCMS method offers outstanding recognition, retains the sharpness of details, and has low sensitivity to noise. We also applied the CCMS method to Bouguer anomaly data of a potash deposit in Laos. The applicability of the CCMS method is shown by comparing the inferred tectonic framework to that inferred from remote sensing（RS） data.

位场边界检测在位场数据处理中占有重要的地位,大多数的检测方法都是基于位场的梯度计算,因此算法易受干扰、稳定性较低。本文基于数理统计理论,不需要进行位场梯度计算,提出了利用各方向均方差相关系数进行位场边界检测,并且对算法及其合理性进行了详细的阐述与分析。在模型试验中,分别做了单一模型、组合模型以及加入随机噪声的组合模型试验,验证了方法的可靠性,并进一步与其它边界识别方法作了比较。各方向均方差相关系数法的特点为:算法简单稳定,结果辨识度较高,能同时对不同埋深的地质体边界都有较好的检测效果,能较好地保留边界形态,对噪声敏感度较低。最后将方法应用于老挝万象附近某地实测布格重力异常的处理中,利用研究区遥感解译的构造格架作为佐证,说明了各方向均方差相关系数法在实际应用中的可行性,为进一步判读区内构造展布提供了依据。