I've put together a rough estimation for TREO for Yin in my...

I've put together a rough estimation for TREO for Yin in my spare time.

Some disclaimers:
-I am not a qualified competent person in REE geology.
-This is literally a 2 hour estimation, while I expect it should be in the ball park of the soon to be released official estimation, It is just a guess - really. I dont have access to the data and geological interpretations to do a proper estimation.

1) I used the provided drill hole information from the 21 November ASX ann 'BROAD, HIGH-GRADE ASSAYS AT YIN REE DISCOVERY – MANGAROON 100% DRE'
- this data poses many issues for estimation. grades were averaged over large intervals with an unknow cut-off. This effectively only gives me 1 sample per interval for estimation. The higher grades included in intervals needed to be removed as those high grades are already accounted for by averaging up the larger intervals.
- intervals under 3m were excluded, as it would take me too long to model.
-I capped grades at 2.5%. anything over that was re-assigned to 2.5%.
- I assumed a density of 2.5.
- there were some holes missing assays. I assumed this was because they were barren. All holes missing assay are modeled as barren.
- there were a few obvious typos in the data tables. I assume this is because someone manually typed out the data into the ann. This means that there may be other typos I didn't catch.



2) Grades shells were created using basic tools. I modeled 10 individual veins, which I'm sure is way over simplifying the geology. DREs interp is more complex and more accurate, however, I'm not being paid to do this nor do I have the time. Simple wins.



3) a sub-blocked model was created. Blocks within the grade shells are 1x1 m with variable heights, no more than 5m. this works well with the steeply dipping veins.



4) 2 estimations were calculated in every block:
1 - Nearest neighbor. This assigns the grade of the block to the nearest sample.
2 - Ordinary kriging. This does a basic calculation using a variogram and search parameters to estimate a grade based on several nearby samples.





5) these are the resulting grade tonnage curves from both estimation methods overlaid:

@ 0.3% TREO cutoff - roughly 13 million tons with an average grade of about 1%

@ 0.5% TREO cutoff - roughly 11 million tons with an average grade of about 1.175%

@ 1.0% TREO cutoff - roughly 6 million tones with an average grade of about 1.4%


Again, please understand the officially estimate figures could be very different from this. However, I would guess this is pretty close and that the official estimation is more likely to come in slightly higher rather than lower than this. I used some pretty conservative modeling and estimation parameters. I also cut a lot of corners to make it faster and easier, which would probably reduce the tones slightly (eg ignored smaller intervals)