Identifying Multi-Scale Gravity and Magnetic Anomalies Using Statistical Empirical Mode Decomposition: A Case Study from the Eastern Tianshan Orogenic Belt

Identifying multi-scale anomalies that have simple forms and geological significance is critical for enhancing the interpretability of gravity and magnetic survey data. In recent years, empirical mode decomposition (EMD), which was developed as a significant data-driven approach for analyzing complex signals, has been widely used in identifying gravity and magnetic anomalies due to its advantages of adaptability to nonlinear and nonstationary data. Nevertheless, the traditional EMD method is usually sensitive to outliers and irregularly spaced data because of the interpolation process in the construction of envelopes. In this regard, an extended algorithm called statistical EMD (SEMD) has been proposed based on the smoothing technique. In this study, for validation purposes, the novel SEMD method has been employed to identify multi-scale gravity and magnetic anomalies. The sensitivities of local polynomial and cubic spline smoothing methods in SEMD to combination and arrangement patterns of field sources including the size, depth, and distance in gravity and magnetic anomaly identification were investigated and compared by forward modeling under the same conditions. The results demonstrated that the local polynomial smoothing method performed better than the cubic spline smoothing method. Thus, in the case study, the SEMD method using the local polynomial smoothing technique was employed for identifying multi-scale gravity and magnetic anomalies in the eastern Tianshan orogenic belt, northwestern China. It has illustrated that the SEMD method provides a novel and useful data-driven method for extracting gravity and magnetic anomalies.