In recent decades, the study and recognition of liniments has become an important issue in the field of geosciences. Liniments consider could be a schedule hone for the interpretation of gravity information. Besides, it is imperative for a wide extend of topographical information. Be that as it may, elucidation results of gravity information are found to be exceedingly variable among interpreter and need consistency indeed inside a person. In this manner, modern strategies have been displayed to progress the unwavering quality of basic elucidation, and these methods help interpreters to reach similar results from the same gravity data.
     Potential field methods play a fundamental role in geophysical explorations. One of the main goals in the interpretation of potential field data is to determine the location and estimate the depth of magnetic and gravity anomalies. Quantitative interpretation methods of potential field data, such as standard Euler, have always been modified in order to increase the accuracy of determining the characteristics of subsurface sources, and generalizations have been made in order to improve the reliability of the results. Standard Euler method is based on choosing the dimensions of the window and depends on the structural index. Therefore, defining a window with suitable dimensions and moving it within the data grid or profile and choosing the appropriate structural index can provide the results of this method with higher accuracy. Since the lack of accurate determination of the structural index can lead to wrong results in depth estimation, Euler's generalizations are presented to remove the structural index from the calculation process. Quality of the field data poses great influence on the Euler inversion solutions. If the data has low signal-to-noise ratio, the computational process will be masked. This issue makes it difficult to outline boundaries of the causative sources.
     In this research, in order to determine the location and estimate the depth of the gravity anomaly, a generalization of the standard Euler approach using the singular value decomposition method and the ratio of the horizontal gradient to the analytical signal have been used. Also, the quantity of phase congruency was applied to the data in order to interpret the results more accurately. Compared to the standard Euler, the results show more compliance with the anomaly boundaries. Moreover, the depth estimation interval (histogram) in the improved Euler is smaller than the standard Euler. For further investigation and increasing the credibility of the interpretation, phase congruency was applied to synthetic and real data aiming at more accurately determining the location of the subsurface anomalies.