While it’s true that we’re dealing with the same measurements – gravity and magnetic – the quality and utility of the public data can vary greatly depending on how the measurements were acquired. For one thing, public datasets are often acquired at fairly sparse line spacing – often 5-10km. Moreover, the public measurements might have been acquired using ground-based acquisition stations. If this is the case, not only is the ‘cross-line spacing’ going to be 5-10km, so too will be the ‘in-line’ spacing.
In the case of the Grav-Mag datasets that NEOS typically acquires for a neoBASIN survey, the cross-line spacing will be much tighter; our standard flight-line spacing is 1km. The in-line sampling density will be even greater, as the aircraft we use are capturing measurements roughly every 80 meters for gravity (1Hz sampling) and every 8 meters for magnetic (10Hz sampling).
The results become visible in the panel of images above. The upper panel shows the (Tilt Derivative of) Magnetic data, while the bottom panel shows the (First Vertical Derivative of) Gravity data. On the right side is the data we actually acquired for our neoBASIN project in and around Venango County, Pennsylvania. The black lines are faults and lineaments that were interpreted based on an analysis of not only these two derivative products of the Grav-Mag datasets, but also of a suite of roughly 15 direct Grav-Mag measurements and associated attributes and derivatives that were extracted from them.
We then decimated the actual datasets back to 5km (middle panels) and 10km (far left panels) flight-line spacing and reinterpreted the datasets (and the associated attributes and derivatives) to identify the faults and lineaments we felt were present. Hopefully you can see the improvements in not only resolution, but also in the number of interpreted faults and lineaments, as one moves from left to right.
And these are still best case differences – we didn’t decimate the datasets in-line; had we done so, the middle and left side data quality (and fault interpretations) would be even worse.
It is for the reasons shown here that we often recommend that our clients acquire new Grav-Mag data as part of a neoBASIN regional program even if they have access to publicly available measurements. After all, who would want to take the risk of drilling and fracing a $10 million well using “left panel data” when only 1/3 of the faults in the area have been identified?