4 ms·

A kalman filter does what, in this case? Pardon my ignorance, but I have only seen that used for estimating locations / parameters of some process or target, no

A kalman filter does what, in this case? Pardon my ignorance, but I have only seen that used for estimating locations / parameters of some process or target, not for determining existence ("is N>0?") or amounts ("what is N?"). You could cobble together a quantity estimation doing some kind of batch data association with noisy "presence" measurements, but you're probably not much better off than k-means at that point and any KF-based measurement will basically just say "Yes N>=1" because the probability is nonzero.

defrost 2y ago
There's much fun to be had with filters of varying kinds - in airborne mag KF filters can be used for removing heading induced signal - variations due to the plane travelling in specific headings wrt earth magnetic field. In a similar handwavy fashion, after picking instrument paramers and distances that might twitch on shaft responses, 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. Other tells for lost 'hidden' shafts might include Lidar profiles of spoil heaps .. these are clear in some cases, eroded and softened in many others. A good many old shafts are visible from the air in any case; more so at some times of days than others - if the will is there to map them then a first pass combo of visual processing and lidar returns to map out open shafts is a good start.
- 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 ago
    In 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.

defrost 2y ago

There's much fun to be had with filters of varying kinds - in airborne mag KF filters can be used for removing heading induced signal - variations due to the plane travelling in specific headings wrt earth magnetic field. In a similar handwavy fashion, after picking instrument paramers and distances that might twitch on shaft responses, 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. Other tells for lost 'hidden' shafts might include Lidar profiles of spoil heaps .. these are clear in some cases, eroded and softened in many others. A good many old shafts are visible from the air in any case; more so at some times of days than others - if the will is there to map them then a first pass combo of visual processing and lidar returns to map out open shafts is a good start.

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 ago
  In 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.