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Geophysics Review: 2023 ZTEM-MAG Survey

Geophysical Review

 

of

 

Airborne Geophysics

 

Meridian Project

  

on behalf of

New Gold

Report Prepared by:

Todd Ballantyne, P.Geo.

 

December 20, 2023

New Gold – Meridian Project

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Geophysics Review Airborne MAG Surveys

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This document is organized in labelled sections. The section divisions can only be seen in the downloaded version.

Viewing the presentation online will not show the section breaks and will be more difficult to navigate.

This summary is primarily meant as a basis for discussion of the geophysics. The comments accompanying each slide may be insufficient to fully appreciate the objective of the observations. It is also lofty in the number of slides as it is meant to be able to discuss the topics without access to the data.

It is also meant as a visual notepad to capture progress or workflows through the geophysical data so that observations are not lost.

Date of work: 2023 December 20

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Data Naming – Geophysics Processing

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Data naming convention:

Data filtering is noted in order of processing from left to right.

 

Project_MAG_RTP_1VD _L200-40m_2013_bg

Magnetic TMI data reduced to pole with first vertical derivative applied and gridded using 40 m cells from 200 m spaced lines based on bi-directional gridding. ��Project_MAG_RTP_TGA_L200-40m_2013_mc

Magnetic data reduced to pole, followed by a Total Gradient filter (same as ASIG: analytic signal). Data gridded using 40 m cells from 200 m spaced lines based on minimum curvature gridding.��Project_MAG_TMI_PGRAV_TGA_L100-20m_2013_bg

Magnetic data (total magnetic intensity) transformed to pseudogravity and then the analytic signal or total gradient (TGA) is applied. Data gridded at 20 m cells using bi-directional gridding.

  

TMI: total magnetic intensity ( others, TFM, TF)

TMIge: gradient enhanced TMI using measured horizontal gradients

RMI: residual magnetic intensity

RTP: reduced to pole TMI using local magnetic inclination and declination

RTPge: gradient enhanced RTP based on TMIge

 

1VD: first vertical derivative calculated on the gridded data (others 1VG, CVG)

1VD1d: first vertical derivative calculated on the line data and then gridded

ASIG: analytic signal, essentially the same as TGA. ASIG1d is calculated on the line data as per original theory

TGA: total gradient amplitude calculated on gridded data

TILT: tilt angle (others TiltDrv)

THDR: total horizontal derivative/gradient

TDX: derivative-based filter for trends and edge detection

THETA: derivative-based filter for trends and edge detection

HG: horizontal gradient (or HGRAD)

HG045: directional horizontal gradient along 045°

HP: high-pass filter

HP750m: 750 m high-pass filter

General data naming convention:

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Data Naming – Geophysics Processing

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VIAS: vertical integral followed by analytic signal (TGA) useful for magnetic remanence

VIAS-ntr-03-09: no trend removed, low/high frequency cut offs 0.3/0.9

VRMI: vector residual magnetic intensity (useful for magnetic remanence)

MLE: magnetic layer extraction, depth to top and thickness of layer – this is an attempt to visualize the data as shallow, intermediate and deep sources

MLE-100-300m: depth to top 100 m, slab thickness 300 m

ntr: no trend removed

tr: trend removed

 

UPCON: upward continuation

TERN: ternary image from three grids

CMY: cyan magenta yellow colours used in ternary image

RGB: red green blue colours used in ternary image

 

CET: center for exploration technology, Australia, CET module in Geosoft

CET-DRC: dynamic range compression in the CET module processes the magnetic data such that it can be viewed better using a linear colour distribution. Data are processed at several wavelengths: this specifies the cut-off frequency of the high-pass filter which controls the scale of analysis. Spatial frequencies in the image with wavelengths greater than the specified value will be suppressed allowing the residual features to be seen more readily.

CET-PS: CET module phase symmetry lineament analysis

DEM/DTM: digital terrain model

 

bg: bi-directional gridding

mc: minimum curvature gridding

mtg: multi trend gridding

ntr: no trend removed

L100-20m: 100 m line spacing and gridded data at 20 m cells

 

MAG inversion

MVI: magnetization vector intensity, handles MAG remanence

SUS: conventional MAG susceptibility model, cannot solve for remanence

EM data

TDEM: time-domain electromagnetics, data are collected over time

TDEM dB/dt data: time derivative of the EM data

TDEM B-field data: B-field data often calculated from dB/dt if not directly measured

FDEM: frequency-domain electromagnetics, data are collected using frequencies

In-phase: portion of the EM response that is in-phase with the transmitted signal (TX)

Out of phase (quadrature): portion of the EM response that is not in phase with the TX

CPI: coplanar in-phase data

CPQ: coplanar quadrature data

CXI: coaxial in-phase data

CXQ: coaxial quadrature data

K-means clustering: (single or multiple variables)

 

KM06: using 6 zones of clusters

lin: linear normalization

log: log normalization

cslin: custom extents linear colour scale used

cslog: custom extents log colour scale used

Geosoft grids: do not use any grid with “_s” at the end of the file name. These are used by Geosoft to make shaded grids.

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Geophysics Review Airborne MAG Surveys

Introduction

 

This data review has been prepared at the request

Scope of Work

 

Geophysical data compilation

Data Delivery

 

The geophysical data were provided by _______________.

Datum and Projection

 

The data were received in

Work Performed

 

The following is a brief list of work performed:

  • Import the 2019 and 2023 airborne geophysics deliverables to a Geoscience Analyst workspace
  • A quick 3D magnetic MVI and SUS inversions of 2019 data

  • Delivery of PowerPoint summary and Geoscience Analyst workspaces

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MAG survey 2023 (Drone)

Two lines of data are missing in the database. The final products by the contractor show a solid grid across this gap. Either the contractor has gridded through the gap or the database was delivered is not the final version.

According to Pioneer survey was not completed and issues will be dealt with in 2024.

2023 DEC 13

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MAG survey 2023 (Drone) - 3D inversion preparation

Contractor delivered altitude of drone (black line) and laser alt data. The laser alt data needs processing before use. The blue line is CDEM data and the purple line is CDEM + laser alt for comparison with the altitude (black) delivered by contractor. In order to perform MAG inversion we need accurate topography and data height above it. We can calculate a pseudo-altimeter height based on the drone GPS height and the CDEM topography. However, manual inspection of all of the lines is needed as shown in this example where there is a 17 m abrupt drop in the drone elevation. This is unlikely to have happened, but it may be related to overlapping drone flights due to the limited range of flying time.

When looking at this example, should the data to the left of the drop be corrected by 17 m or should the data to the right of the drop be corrected by 17 m?

2023 DEC 13

Geophysics Review MAG 2023 Survey

Correct altitude of the data? Seems not base on abrupt shift. 17 m drop

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MAG survey 2023 (Drone) - 3D inversion preparation

Filtering the laser ALT data: are the highest values the ground surface and everything else tree cover? Example below could suggest a 4-5 m difference between contractor altitude and CDEM. Ideally, MAG inversion is conducted using the non-levelled MAG data and survey height above ground surface. However, it can also be done with levelled data and a nominal or average height above ground.

Does this represent laser ground hit and correct height above topo?

2023 DEC 13

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MAG survey 2023 (Drone) - 3D inversion preparation

Investigating laser alt data vs. flat areas without water reflection issues.

2023 DEC 13

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MAG survey 2023 (Drone) - 3D inversion preparation. Investigating laser alt data vs. flat areas without water reflection issues. L170 middle profile panel shows the contractor MAG data altitude versus the CDEM topo plus the laser altimeter. Ideally the laser altimeter needs a lot of processing, but in this flat area we can see that the contractor altitude agrees well with altitude calculated by using the laser altimeter. However, there still remains the issue of confidently defining the height above ground with respect to magnetic inversion.

2023 DEC 13

Geophysics Review MAG 2023 Survey

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MAG survey 2023 (Drone) - 3D inversion preparation - Investigating laser alt data vs. flat areas without water reflection issues. Middle profile panel shows the drone altitude versus CDEM topography. The bottom panel shows the original laser altimeter (grey dotted), laser clipped between 30-75 m and new “altimeter” calculated from drone GPS height and CDEM (orange).

2023 DEC 13

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MAG survey 2023 (Drone) - 3D inversion preparation

Investigating laser alt data vs. flat areas without water reflection issues.

Lines needing attention: 110, 140, 230, 250, 270, 290, 310, 390, 520-590, 660-700, 1210,

L520 shows overlapping data that was not properly dealt with. The overall impact is likely minor. This is an example of attention to detail that drone surveys often don’t bother to correct for.

Inversion using a mean ground clearance:

Laser data (clipped 30-75m): 54 m

Estimated clearance (GPS – CDEM): 70 m

L1210 example where laser data is indicating 2 m above ground and the difference between grown GPS and the CDEM is 124 m. If we use nominal survey height above ground of 70 m then data in this location will be incorrectly models because it is 124 m above ground.

2023 DEC 13

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MAG survey coverage for 2019 (Precision GeoSurveys) and 2023 Drone survey.

2019: 100 m lines

2023: 40 m lines

Bottom image viewing azimuth 104 of CDEM topo.

2023 DEC 13

Geophysics Review Airborne MAG Surveys

2019 MAG

2023 MAG

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MAG survey 2019 -

MAG RTP data, 20 m cells bi-directional gridding

Reduced to Pole data shifts the TMI data to represent a vertical magnetic field of 90 degrees. The magnetic inclination here is 72 degrees. If no magnetic remanence exists in the source rocks then RTP is ideal. When RTP and TGA (next slide) show obvious differences, remanence needs to be considered and MVI inversion is a good double check.

The cyan dashed box in the bottom of the image highlights an area where RTP and TGA/MVI show notable differences.

2023 DEC 19

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MAG survey 2019 -

MAG TMI TGA data (total gradient)

The cyan dashed box in the bottom of the image highlights an area where RTP and TGA/MVI show notable differences.

2023 DEC 19

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MAG survey 2019 - 3D MVI inversion run 2 i25

MAG_MVI_r2_m50x50_d50_n0222_e5

50 m cell resolution

-100 m below topo slice

Histogram colour bar, 15 zones

Recommend a more detailed inversions at 25 m cells and splitting the survey area in to 2 blocks.

Or wait for final Drone MAG data and invert that**.

Gridded data are done at 20 m cells.

** The north region has been inverted using 25 m cells.

The cyan dashed box in the bottom of the image highlights an area where RTP and TGA/MVI show notable differences.

2023 DEC 19

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MAG survey 2019 - 3D MVI inversion run 2 i25

-200 m below topo slice

Histogram colour bar, 15 zones

Bottom image viewing azimuth 104.

2023 DEC 19

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MAG survey 2019 - 3D MVI inversion run 2 i25

-300 m below topo slice

Histogram colour bar, 15 zones

2023 DEC 19

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MAG survey 2019 -

Northern veins

RTP (reduced to pole TMI data)

2023 DEC 19

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MAG survey 2019 -

Northern veins

TMI TGA (total gradient)

2023 DEC 19

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MAG survey 2019 -

Northern veins

MVI inversion run 2 iteration 25

-100 m depth slice following topo

50 m model mesh

2023 DEC 19

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MAG survey 2019 -

Northern veins

RTP 1VD (vertical derivative)

2023 DEC 19

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MAG survey 2019 -

Northern veins

TMI TGA TILT

(Tilt of TGA to sharpen responses)

2023 DEC 19

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MAG survey 2019 -

Central and South veins

RTP (reduced to pole TMI data)

2023 DEC 19

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MAG survey 2019 -

Central and South veins

TMI TGA (total gradient)

Image below: TGA TILT

2023 DEC 19

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MAG survey 2019 -

Central and South veins

MVI inversion run 2 iteration 25

-100 m depth slice following topo

50 m model mesh

2023 DEC 19

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MAG survey 2019 –

25 m cell MVI inversion test (run 3)

Looking at the “data fit” blue line we might need to increase the data errors (using non-levelled TMI) or use the levelled data. First choice would be increasing errors. The default error was 3.5 nT and 4 nT was used. Perhaps an error of 5 or 6 nT would make a difference.

The target data fit value is 6,300 on the left vertical axis. The plot shows the data being either under or over this target value.

This model has been added to GA workspace v3. More work recommended.

Depth slices following topo would be the next step. Elevation slices are not very useful in the near surface due to topography.

2023 DEC 19

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Geophysics To Do’s

Investigating the use of magnetic profile data.

TMI in-line gradient (ILG)

Left image is linear scale and the right image is a log/linear scale.

Positioning of peaks for the N-line gradient will differ from the ASIG since the ILG data are TMI and have not been converted to RTP. The ILG is a measured in-line gradient as opposed to ASIG which is calculated.

2024 JAN 09

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Geophysics To Do’s

Investigating the use of magnetic profile data.

TMI ASIG analytic signal

(effectively the same as the TGA, total gradient except that it is calculated on the line data instead of 2D gridded data)

Left image is linear scale and the right image is a log/linear scale.

2024 JAN 09

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Radiometric survey 2019 (Precision GeoSurveys)

K-means clustering (8 zones) of Total Count coloured by TC.

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

Ternary image (RGB colours) of K, Th, and U radioelements using histogram distribution.

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

Ternary image (CMY colours) of K, Th, and U radioelements using histogram distribution.

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

K-means clustering (8 zones) of K, Th, and U radioelements coloured by Th.

SPEC could suggest prospectivity along features of known veins.

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

Potassium

K radioelement, linear colour 0.05 to 0.7

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Radiometric survey 2019 (Precision GeoSurveys)

Thorium

Th radioelement, linear colour 0.07 to 7

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

Uranium

U radioelement, linear colour

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

Comparison of MAG TGA and Radiometric data with respect to mapped veins and faults (purple).

K-means, 8 zones, TGA + Total Count (TC) coloured by TGA amplitude.

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

Comparison of MAG TGA and Radiometric data with respect to mapped veins and faults (purple).

K-means, 8 zones, TGA + Thorium (Thcor) coloured by TGA amplitude.

2023 DEC 19

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Radiometric survey 2019 (Precision GeoSurveys)

MAG TGA with respect to mapped veins and faults (purple).

K-means, 8 zones, TGA coloured by TGA amplitude.

2023 DEC 19

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Disclaimer – in3D Geoscience Inc.

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in3D Geoscience Inc.

Client: New Gold

in3D Geoscience has prepared this presentation based upon information and data provided by CLIENT. It is believed to be accurate and of good quality. This data was not within the control of in3D Geoscience. This presentation has been undertaken in accordance with professional industry standards. The opinions and recommendations presented reflect the author's skill and judgment at the time of presentation preparation.

 

Under no circumstances does in3D Geoscience make any warranties either expressed or implied relating to the personal opinions, interpretations or completeness of the information comprising this presentation. CLIENT is solely responsible for the use, further interpretation, and application of the contents and for any costs incurred and expenditures made in relation thereto. CLIENT acknowledges that any use or modification to the information or recommendations contained within this presentation is at CLIENT sole risk and without liability to in3D Geoscience. This presentation is intended for those who have the appropriate degree of experience to understand and apply its contents. in3D Geoscience will not accept any liability for errors or omissions, for losses or damages, or expenses (including court costs and attorney’s fees) arising out of or relating to any use of the contents of this presentation.

in3D Geoscience Inc.

Permit Number (BC): #1001322

Todd Ballantyne, P.Geo. (BC): #20264

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Data Resolution

Data resolution

Survey line spacing and height above ground are important factors in what we can measure or resolve in the subsurface.

This is a good example of how important the line spacing and survey height is to data acquisition. Ignore the units of feet and consider the data shows when the line spacing is doubled each time. Survey data will always generate responses that look very normal and real in the absence of more detailed data.

In the left image there is a NE-SW trending feature, but in the middle image it is missing and there appears to be a NW-SE trending feature which we know from the higher resolution data that it is not real. In the right image with a line spacing that is too wide for mapping at this scale we see nothing.

*The image is from a 2014 Alan Reid talk, from Hood (1979)-Exploration ‘77 volume.

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Unconstrained 3D Magnetic or Gravity Inversion

MAG or GRAV interpretation considerations

Potential field data does not allow for a single unique solution.

This example illustrates that the red profile data could be reproduced by multiple “sources” which are a combination of geometry, depth and intensity of magnetic susceptibility or density contrast.

A large near surface low density contrast can reproduce the data as well as a deep, small and extremely dense object. In theory there are an infinite number of solutions, but in reality, there will be a finite number of geologically realistic solutions.

Unconstrained inversion modelling is a very useful tool to gain perspective into the subsurface distribution of either magnetic susceptibility or density contrasts.

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data

depth

Increasing MAG SUS or Density

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Unconstrained 3D Magnetic Inversion

Unconstrained 3D magnetic inversions

A magnetic or gravity inversion model represents a mathematical estimate of subsurface distribution of magnetic susceptibility or density that would be capable of reproducing the observed data as acquired by the ground or airborne system.

Potential field modeling, utilizing magnetic or gravity data, will not provide a single unique solution (often referred to as non-uniqueness). This should not be viewed as detrimental to the process of using inversion modeling. It is simply a reality with respect to the use of numerical modeling efforts to represent a subsurface interpretation of what is often a complex distribution of physical rock properties. Geophysical inversion models are then be interpreted with respect to geology, alteration and structure. These models act as a three-dimensional perspective into what could be occurring in the subsurface.

Constrained inversion modelling involves the addition of known information which helps to direct the inversion modelling in a particular direction. For example, if a survey was conducted in an area with deep overburden and the overburden depth was known across the survey area, then this information could instruct the inversion to not locate any magnetic susceptibility in the overburden. This forces or constrains the inversion model to populating the bedrock with a magnetic susceptibility distribution that reproduces the original survey data.

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An illustration of the difficulties associated with magnetic and gravity data interpretation. The problem of non-uniqueness is an important consideration. In this example, the gravity data profile (top red line) can be reproduced by any of the four model sources. From simply looking at how well the 4 model responses reproduced the survey data it may be difficult to choose the best option if we knew nothing about the geologic environment. We need some kind of a priori information in order to choose the best model solution.

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Geophysics – MAG – Total Gradient/ASIG

Using Analytic Signal (ASIG) on profile data

Simplifies the MAG response and can be thought of “as a map of magnetization in the ground”. Some care must be taken to interpret whether a peak represents the edge of a source or is centered on the source. The width of the magnetic source plays a role in this. Note in the examples on the right the source is a depth limited prism. In the ASIG center and bottom images the peaks are representing the edges of the prism.

Generally, my preferred data naming is ASIG for profile data (i.e., 1D line data filter) and TGA (total gradient amplitude) for gridded data (i.e., 2D grid filtering). Essentially, they are the same except that ASIG was designed/purposed for filtering line data and extended to grid filtering.

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3D Inversion

Unconstrained 3D magnetic inversions:

A magnetic or gravity inversion model represents a mathematical estimate of subsurface distribution of magnetic susceptibility or density that would be capable of reproducing the observed data as acquired by the ground or airborne system.

Potential field modeling, utilizing magnetic or gravity data, will not provide a single unique solution (often referred to as non-uniqueness). This should not be viewed as detrimental to the process of using inversion modeling. It is simply a reality with respect to the use of numerical modeling efforts to represent a subsurface interpretation of what is often a complex distribution of physical rock properties. Geophysical inversion models are then be interpreted with respect to geology, alteration and structure. These models act as a three-dimensional perspective into what could be occurring in the subsurface.

Constrained inversion modelling involves the addition of known information which helps to direct the inversion modelling in a particular direction. For example, if a survey was conducted in an area with deep overburden and the overburden depth was known across the survey area, then this information could instruct the inversion to not locate any magnetic susceptibility in the overburden. This forces or constrains the inversion model to populating the bedrock with a magnetic susceptibility distribution that reproduces the original survey data.

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Geophysics– Magnetic Remanence

Magnetic Data – Remanence

https://gpg.geosci.xyz/content/magnetics/magnetics_basic_principles.html?highlight=remanence

The black profile is measured during a magnetic survey. It is generally expected that the majority of survey response will be due to induced magnetization from the Earth’s magnetic field. However, if a permanent magnetization is present (remanence), the measured response is a combination (or sum) of normal induced magnetization and remanent magnetization. The blue profile represents the magnetic cube without any remanence. Red profile is the magnetic response of the remanent magnetization.

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2022 JUL 22

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3D Magnetic Inversion Codes: Susceptibility (SUS) vs. Magnetization Vector intensity (MVI)

Unconstrained inversion example (from Geosoft help video)

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3D Magnetic Inversion Codes: Susceptibility (SUS) vs. Magnetization Vector intensity (MVI)

Considering magnetic remanence

Using this data example and attempting to interpret the TMI data our interpretation would be a complex magnetic high zone flanked by different magnetic low units. Interpreting the TMI data would not be recommended with a magnetic field inclination of 29°. Therefore, one would first calculate the RTP. However, in this example of a remanent source the RTP would be incorrect and likewise compromise our interpretation. A better option would be calculating the total gradient, VIAS or VRMI grid products.

The bottom middle image shows conventional magnetic susceptibility inversion which assumes no magnetic remanence. The model is not capable of solving for remanence and therefore must assume that data are caused only by the current/inducing magnetic field. The solution is incorrect.

The bottom right image takes into account the distortion of the magnetic measurements that remanence would cause and solves for the magnetization vector intensity/direction and not simply its amplitude.

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Unconstrained inversion example (from Geosoft help video)

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3D Magnetic Inversion Codes: Susceptibility (SUS) vs. Magnetization Vector intensity (MVI)

In a modelling study the magnetic source was given a remanent magnetization. The magnetic response of the model was then inverted using conventional susceptibility and magnetization vector intensity.

The susceptibility model shows good data fit, but model result is incorrect and not geologically realistic. The magnetization vector model fits the input data and the model resolves the source body well.

MVI interpretation should be limited to vectors of high amplitude – changes in direction of the small vectors are due to the smooth model norm. �

(images from Geosoft help video)

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3D Magnetic Inversion Codes: Susceptibility (SUS) vs. Magnetization Vector intensity (MVI)

If we were to look at the magnetic profile response of the 2 magnetic inversions shown below, we would see a high/low combination on the susceptibility model (left) and a simple magnetic high on the MVI model (right). The concern relative to magnetic remanence is that when we interpret the magnetic data without 3D inversion information, we make assumptions about the magnetic highs and lows we see relative to amount of magnetic mineralization that the underlying geology is expected to have. We could interpret the left side model responses as two magnetic sources: a magnetic high unit on the left and a low unit on the right. But in fact the actual geology would be a magnetic source located in between the inversion responses.

(images from Geosoft help video on VOXI magnetic inversion)

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in3D Geoscience Inc.

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Unconstrained 3D Magnetic Inversion

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Unconstrained 3D Magnetic Inversion

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in3D Geoscience Inc.