An assessment of the Performance of Derivative Based Data Enhancement Techniques in the Presence of Coherent Noise

Abstract

SUMMARY Enhancement of potential field datasets using operators based on one or more of the spatial derivatives is common practice. The performance of these methods in the presence of noise is poorly understood; other than a general acceptance that they can be significantly affected, especially when higher order derivatives are used. Most published descriptions which involve noise tests use random noise and a dense and uniform sampling of the test region. More realistic tests of the effects of noise should account for the incomplete and anisotropic sampling within most datasets and also correlated noise such as due to incorrect levelling. An understanding of the effects of noise on the different methods of enhancement is particularly important when working with lower quality (older) and lower resolution datasets. Interpretation of geophysical data from West Africa, as part of a major project on the prospectivity of the region, is being undertaken. Much of the data available is of relatively low quality and resolution. An important component of the work will involve determining how best to enhance the gravity and magnetic datasets. Initial results working on gridded data show that the generalized derivative operator is the most robust derivative based enhanced product for low resolution data.