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> KF filters can be used for removing heading induced signal OK - fits what I'd use them for > filters can be zeroed on regions with no shafts to see if an e
by jvanderbot 2y ago
> KF filters can be used for removing heading induced signal
OK - fits what I'd use them for
> filters can be zeroed on regions with no shafts to see if an enhanced response can be amplified when processing the return over ground with a shaft to the surface
OK - I see, look for outliers as positives, though the input signals here are not clear I can see the path. Your KF filters produce a "likely signal" and that is used downstream to actually do estimation. Probably, if I've learned anything, by plotting those "likely signals" on a map and dispatching a team with cameras.
> Other tells for lost 'hidden' shafts might include Lidar profiles of spoil heaps
Here we're outside what I'd call just "filtering". It's more like a big data science problem to model and label mounds.
I was just confused by zeroing in on "KF" in the comment above. If we're talking a big data pipeline, then yeah, KF has its place in all that.
- defrost 2y agoIn my mind it's all multi spectral processing .. combining signal from many bands some quite disimilar, to build a representation that can be ground investigated ( or "truthed" ). In building exploration targets we'd pull together gravity, radiometrics, magnetics, EM, ERT, past mining records, regional surface geochemistry et al. If tasked with finding old workings and shafts many things spring to mind .. old road and track patterns, strong vegetation zones perhaps indicating catchment from workings and dead vegetation zones perhaps due to leached processing chemicals (cyanide etc.) But, yes, returning to the filter options there's signal and noise; when looking for the uncommon it's useful to use (say) SVD techniques to solidly lock onto common broad area signal patterns and then remove the commonplace and look for pattern in the residual noise.