Previous work has suggested a potential value in the combination of physical property data types (e.g. magnetic and terrain slope) when searching for oil and mineral deposits. This work studies a notional multi-dimensional function to determine the likelihood of finding such deposits. Additionally, this hypothesis assumes some basic requirements must be meet in order to validate this function.
The standard for determining the value of commercially gathered electro optical imagery is the same as with any optical system -- the ability to determine object in the field of view. Further, this function is defined as the ability to determine the presence of two parallel lines, vice only one. The National Imagery and Mapping Agency (NIMA) uses a function called Digital Terrain Elevation Data (DTED) to determine the elevation within a field of view. The DTED values for each pixel within a digital, commercial image can be considered similar to a gradient, whereby higher values are merely higher elevations. For the commercial electro optical system IKONOS (owned by Space Imaging, Inc.), the “resolution” is commonly referred to as 1 meter, which is the least discernable, parallel-line, separation distance.
This hypothesis uses gravity and magnetic data to augment the DTED "gradient". As with the terrain values on the earth, gravity and magnetic values are continuously changing. Further, they can change for various reasons. Both are greatly affected by the changes in the subsurface materials, or the density of the soil and metallic content (e.g. iron). It is precisely these variations, through the combination of such differing forms of data, which can help determine the presence of oil and mineral deposits.
The core of this work is a notional function development. Previous peer review has rightly pointed out that data fusion principles state that data must be commensurable before it can be fused. This work does not attempt to redefine data fusion concepts, but merely establish a set of gradients for digital terrain, gravity, and magnetic data sets. From these gradients, a combined function is defined which relates to a unique "signatures" for oil and mineral deposits. This signature could be derived from common gradient data (DTED slope, gravity, and magnetic).