A New Potential-Field Downward Continuation Iteration Method Based on Adaptive Filtering

Potential-field downward continuation is a crucial tool to process gravity and magnetic data, which is capable of effectively enhancing weak anomalies and identifying overlapped ones. However, available methods in this procedure do not include analysis about the impact from different frequencies of the observed magnetic data on the continuation effect. Besides, these methods contain quite a few iteration processes that fix the filter operator and result in their poor performance in reality. In this article, a new downward continuation method is presented, which is based on adaptive filtering within the iteration framework. It finds out the wavenumber distribution of observed data by analyzing its power spectrum, followed by adaptive adjustments on the passband of the filter operator, so as to effectively enhance the convergence speed and continuation accuracy. The simulation results indicated that this method could achieve adaptive adjustments as desired, and it could produce results with higher accuracy and faster convergence rate than the Landweber iterative method can. In addition, tests with actual data revealed a fast and stable downward continuation effect with the proposed method.