Independently researched Settings Guide

Minelab Manticore Settings for Target ID

Manticore target interpretation uses three linked clues: its 1–99 conductive ID, the target trace on the 2D map and ferrous classification above or below the centerline. One number alone is not enough.

Step 1Stabilize the detectorNoise Cancel, ground procedure and sensible sensitivity first.
Step 2Watch trace positionCenterline supports non-ferrous; upper/lower regions add ferrous evidence.
Step 3Compare sweep anglesGood targets should remain reasonably repeatable from more than one direction.
Step 4Edit lastUse discrimination or Ferrous Limits only for a measured site problem.

Bottom line

Read the 2D trace and audio before editing discrimination

Begin in the search mode that matches the site, run Noise Cancel and Ground Balance, use the factory Ferrous Limits, and dig repeatable targets while learning local ID ranges. Add rejection only after identifying recurring trash.

Operating rule: Treat every number as a starting point. Test on representative targets, change one control at a time and retain only repeatable improvements.

Field setup

Starting points—not universal presets

SituationMode / baselineFirst adjustmentCheck
Clean coin siteAll-Terrain High ConductorsFactory Ferrous LimitsLearn local coins rather than importing a chart
Mixed general siteAll-Terrain GeneralOpen enough to hear contextUse trace shape plus audio
Dense modern trashAll-Terrain Fast or Trash RejectFaster recovery as neededExpect reduced faint/deep response
Suspected iron falseAny appropriate modeTemporarily expose rejected audioLook for upper/lower ferrous smearing

Adjustment method

Test the setting instead of copying it

Step 1

Stabilize the detector

Noise Cancel, ground procedure and sensible sensitivity first.

Step 2

Watch trace position

Centerline supports non-ferrous; upper/lower regions add ferrous evidence.

Step 3

Compare sweep angles

Good targets should remain reasonably repeatable from more than one direction.

Step 4

Edit last

Use discrimination or Ferrous Limits only for a measured site problem.

Decision analysis

What actually changes the result

The conductive axis is 1–99. Manticore does not assign a separate negative ID range to iron in Multi-IQ+. Ferrous classification is shown by the indicator, audio and trace position.

Ferrous Limits differ from discrimination. Discrimination rejects ID columns across the map; Ferrous Limits define upper and lower areas classified as ferrous. Editing one is not equivalent to editing the other.

Single frequency changes the evidence. Minelab says Ferrous Limits are disabled and ferrous targets can display 1–19 with a red ferrous indication. Avoid transferring Multi-IQ conclusions directly.

FAQ

Seven questions specific to this detector

What is Manticore’s conductive Target ID range?

In Multi-IQ+ it uses conductive IDs from 1 to 99, with ferrous evidence shown separately on the 2D map and indicator.

Why can iron show a positive number?

Manticore uses the same conductive scale; processor classification, trace position and ferrous audio show the iron evidence.

What does a trace on the centerline mean?

It supports a non-ferrous classification, but depth, mixed targets and mineralization still require audio and cross-sweep checks.

Are Ferrous Limits the same as discrimination?

No. Ferrous Limits shape upper/lower ferrous classification areas; discrimination accepts or rejects conductive ID columns.

Should I copy another user’s coin IDs?

Use them only as rough context. Alloy, country, orientation, depth and nearby metal change IDs.

Why does ID jump on a deep target?

A weak signal, mineralized ground, edge-on orientation or adjacent iron can spread the trace and number.

What changes in single-frequency operation?

Ferrous Limits are disabled, and Minelab says ferrous targets may show IDs 1–19 with the red ferrous indication.

Use stable, repeatable settings

Record the mode, coil, firmware and ground conditions that work at your own sites.

Disclosure: As an Amazon Associate we earn from qualifying purchases. Product conclusions are based on documented features and stated limitations; no hands-on testing is claimed.
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