A dynamic window-based Euler depth estimation algorithm for potential field geophysical data

The Euler deconvolution method is an outstanding, automatic and fast tool for depth estimation of subsurface masses in potential field geophysics. One of the main challenges in solving the standard Euler equation is accurately determining the window size, while depth estimation is essentially controlled by the variation of the assumed window size. Due to the complexity of the geological structure and targets sought, selecting the optimal fixed window size, to scan all points within the study area, is usually a difficult task. The provision of an algorithm utilising the optimal dimensions of the dynamic window, for an accurate depth calculation of the explored sources, is of particular interest for potential field geophysics. In this study, the least-squares method is used to solve the Euler equation system, control a certain error, and, at the same time, search for the optimal window size in the entire area by means of the minimum error rate. In addition to introducing a new dynamic window, this study utilises a completely new computational framework that considers an adaptive and optimal window size in order to obtain an acceptable solution from an Euler equation system. The Euler solutions for gravity and magnetic data in 3D can be visualised by exploring multiple possible window sizes to achieve ideal dimensions and minimise error values. A dynamic window-based Euler depth estimator was successfully implemented in several synthetic scenarios with different characteristics. Next, the algorithm was run on the ground magnetic and gravity data sets of the Shavaz region by depositing several iron patches. As expected, depth estimates of the underlying causative potential field anomalies were reported to be in close agreement with the drill results.