Self-reversal of natural remanent magnetisation in the Olby-Laschamp lavas

Nature ◽  
1980 ◽  
Vol 284 (5754) ◽  
pp. 334-335 ◽  
Author(s):  
Friedrich Heller
Keyword(s):  
Remanent Magnetisation ◽  
Natural Remanent Magnetisation

Classifying Copper-Molybdenum-Gold Porphyry Deposit Alteration using Magnetic and Spectral data

2021 ◽  
Author(s):  
Oliver Dixon ◽  
William McCarthy ◽  
Nasser Madani ◽  
Michael Petronis ◽  
Steve McRobbie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Susceptibility ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Copper Production ◽  
Magnetic Data ◽  
Machine Learning Algorithm ◽  
Porphyry Deposit ◽  
Remanent Magnetisation ◽  
Magnetic Remanence

<p>Copper is one of the most important critical metal resources needed to achieve carbon neutrality with a projected increase in demand of >300% over the next half century from electronics and renewables.  Porphyry deposits account for most of the global copper production, but the discovery of new reserves is ever more challenging. Machine learning presents an opportunity to cross reference new and traditionally under-utilised data sets with a view to developing quantitative predictive models of hydrothermal alteration zones to guide new, ambitious exploration programs.</p><p>The aim of this study is to demonstrate a new alteration classification scheme driven by quantitative magnetic and spectral data to feed a machine learning algorithm. The benefits of an alteration model based on quantitative data rather than subjective observations by geologists, are that there is no bias in the data collected, the arising model is quantifiable and therefore easy to model and the process be fully automated. Ultimately, this approach aids more detailed exploration and mine modelling, in turn, reducing the extraction process carbon footprint and more effectively identifying new deposits.</p><p>Presented here are magnetic susceptibility and shortwave infrared (SWIR) data collected from the KazMinerals plc. owned Aktogay Cu-Mo giant porphyry deposit, eastern Kazakhstan, which has a throughput of 30Mtpa of ore. These data are cross referenced using a newly developed machine learning algorithm. Generated autonomously, our results reveal twelve statistically and geologically significant clusters that define a new alteration classification for porphyry style mineralisation. Results are entirely non-subjective, reproducible, quantitative and modellable.</p><p>Importantly, magnetic susceptibility measurements improve the algorithm’s ability to identify clusters by between 29-36%; enhancing the sophistication of the included magnetic data promises to yield substantially better statistical results. Magnetic remanence data are therefore being complied on representative samples from each of the twelve identified clusters, including hysteresis, isothermal remanent magnetisation (IRM) acquisition, FORC measurements, natural remanent magnetisation (NRM) and anhysteretic remanent magnetisation (ARM). Through collaboration with industry partners, we aim to develop an automated means of collecting these magnetic remanence data to accompany the machine learning algorithm.</p>

Environmental magnetism as a stream sediment tracer: an interpretation of the methodology and some case studies

Soil Research ◽  
1998 ◽  
Vol 36 (1) ◽  
pp. 167 ◽  
Author(s):  
R. H. Crockford ◽  
P. M. Fleming
Keyword(s):  
Grain Size ◽  
Case Studies ◽  
Environmental Magnetism ◽  
Extensive Discussion ◽  
Remanent Magnetisation ◽  
Grain Sizes ◽  
South Wales ◽  
Wide Range ◽  
River Confluences ◽  
The Difference

A comprehensive sediment sampling program was undertaken in the upper Molonglo catchment in south-eastern New South Wales to determine if mineral magnetics could be used to estimate sidestream contribution at river confluences in this environment. Some 12 confluences were examined over 1400 km 2 in 2 major basins and over 2 contrasting geological types. Sediment samples were divided into 7 size classes and the following magnetic properties measured: magnetic susceptibility at 2 frequencies, isothermal remanent magnetisation at 3 flux densities, and anhysteristic remanent magnetisation. The sidestream inputs were calculated for each particle size class from the range of magnetic parameters. Significant discrepancies and differences appeared in the resultant sidestream inputs, and this paper outlines the conclusions as to the reliability of the different analytical procedures. It is shown that both the concentration and magnetic grain size of ferrimagnetic minerals in the sediments must be taken into account. Where the difference in magnetic grain size between the upstream and sidestream sediments is small, the use of parameter crossplots or bulked magnetic ratios is generally not appropriate. The use of mass (concentration) magnetic values may be better. The difference in the demands of the crossplots and mass values methods is that crossplots require a wide range of mass magnetic concentrations in each branch, with the upstream and sidestream sediments having different magnetic grain sizes, whereas the mass values procedure does best with a very limited (but different) range of concentrations at the upstream and sidestream branches, but similar magnetic grain sizes. This paper provides an extensive discussion of the estimation technique using different parameter combinations, and uses 3 contrasting confluences as case studies.

Sign in / Sign up

Export Citation Format

Share Document