|Abstract||Airborne geophysical surveys provide spatially continuous regional data coverage, which directly reflects subsurface petrophysical differences and thus the underlying geology. A modern geologic mapping
exercise requires the fusion of this information to complement what is typically limited regional outcrop. Often, interpretation of the geophysical data in a geological context is done qualitatively using total field and derivative maps. With a
qualitative approach, the resulting map product may reflect the interpreter's bias. Source edge detection provides a quantitative means to map lateral physical property changes in potential and non-potential field data. There|
are a number of
Source edge detection algorithms, all of which apply a transformation to convert local signal inflections associated with source edges into local maxima. As a consequence of differences in their computation, the various algorithms generate slightly
different results for any given source depth, geometry, contrast, and noise levels. To enhance the viability of any detected edge, it is recommended that one combines the output of several Source edge detection algorithms. Here we introduce a simple
data compilation method, deemed edge stacking, which improves the interpretable product of Source edge detection through direct gridding, grid
addition, and amplitude thresholding. In two examples, i.e., a synthetic example and a real-world
example from the Bathurst Mining Camp, New Brunswick, Canada, a number of transformation algorithms are applied to gridded geophysical data sets and the resulting Source edge detection solutions combined. Edge stacking combines the benefits and
nuances of each Source edge detection algorithm; coincident or overlapping and laterally continuous solutions are considered more indicative of a true edge, whereas isolated points are taken as being indicative of random noise or false solutions.
When additional data types are available, as in our example, they may
also be integrated to create a more complete geologic model. The effectiveness of this method is limited only by the resolution of each survey data set and the necessity of
lateral physical property contrasts. The end product aims at creating a petrophysical contact map, which, when integrated with known lithological outcrop information, can be led to an improved geological map.