Robust interpretation of single and multiple self-potential anomalies via flower pollination algorithm

摘要: Self-potential (SP) method is suitable for identifying positions of fractures, ore bodies, leakage, corrosion metal, etc. The identification SP anomalies can be implemented through inversion process, which not only serves to determine the best model parameters but also estimate their uncertainties. and uncertainty determined from posterior distribution (PDM) anomaly inversion. To find required PDM, in this paper, generalized likelihood estimation (GLUE) thresholding proposed as a joint approach together with flower pollination algorithm (FPA). This overall then tested on several synthetics fields (i.e., metallic drum, Weiss, Sawoo, LUSI anomalies) data containing single multiples sources. results demonstrate that robust solving quantitative interpretations data. Moreover, does require prior assumptions over shape source.