ECS (“Extremely Compact Sources”): a new method for potential field data filtering.

Filtering is a fundamental procedure that precedes further quantitative interpretations. In the potential fields case, filtering is used to separate and discriminate the different contributions of a given dataset. In this note we describe a potential field filtering technique based on a “Extremely Compact Sources” (ECS) approach. ECS filtering method allows us to solve the problem of the interfering anomalies, that could hide the real amplitude and shape of the single contributions. The interference phenomena may involve the superimposition of a regional field generated by deep sources in the study area on local anomalies, or the superimposition of anomalies having similar wavelengths. While many methods have been developed during the years to try to separate regional from local fields, fewer methods have been developed to address the separation of interfering  anomalies caused by similar sources. The ECS technique exploits the inherent ambiguity of potential fields to retrieve an extremely compact source model in which sources are well separated each one from other. In this way, the filtering process can be done through a simple "muting" process (setting the physical property of the cells relative to the unwanted contributions to 0) directly in the source domain. We show applications of the ECS technique to both synthetic and real anomalies to prove the validity of the methodology for both separation of interfering anomalies and filtering of regional fields.