Goldfields
Mineralised ground needs calmer choices.
Hot ground often rewards a cleaner threshold, sensible sensitivity, steady sweep, and coil choice that suits the patch instead of the biggest coil available.
Detector settings by ground
Use settings that match the ground, not a generic checklist.
Noisy ground, hot rocks, EMI, wet sand, and threshold drift are field problems, not spreadsheet problems. PFA helps record what changed and why, so the next setup starts closer to the truth.
Product promise
Stop starting every hunt from scratch.
PFA turns detector behaviour into reusable field memory: what the ground was doing, which setup calmed it down, and what should be tried next time.
What people search when the day goes wrong
metal detector chatterhot rocksground balancemineralised soilwet sand settingsEMIthreshold driftcoil choice
Beach
Wet sand and salt need their own notes.
Beach work changes between dry sand, wet sand, black sand, tide lines, and EMI. PFA keeps those notes tied to the session.
Parks and bush
Trash, EMI, and access context matter.
Detector settings should be remembered alongside trash level, signal confidence, local interference, permission context, and the target pattern you were chasing.
Field workflow
1
Start with the detector and coil.
Record the actual machine and coil in use so the guidance is not detached from the hardware in your hand.
2
Name the ground problem.
Hot rocks, mineralisation, EMI, moisture, salt, threshold noise, and false signals each point to different field decisions.
3
Save the settings that settled it.
Sensitivity, threshold, ground balance, mode, recovery, sweep speed, and coil behaviour are worth saving when the patch improves.
4
Compare the next session.
A better setup becomes useful only when it can be compared against the next ground type, signal response, and find result.
Why PFA is different
PFA does not pretend one setting fits all.
The app treats setup as a field decision shaped by ground, coil, goal, interference, and confidence, not a magic universal number.
AI stays grounded in your notes.
The useful AI path is setup guidance from recorded field behaviour, not vague advice divorced from the actual patch.
The map supports the setup.
LiDAR, geology, soil markers, faults, and waterline context help explain why a patch behaves differently under the coil.
Field memory first