There's also a big lithium deposit in Nevada, and preparations for mining are underway there.[1] General Motors put in $650 million for guaranteed access to the output of this Thacker Mine.
It's in a caldera in a mountain that I-80 bypassed to go through Winnemuca, Nevada. Nearest town is Mill City, NV, which is listed as a ghost town, despite being next to I-80 and a main line railroad track. The mine site is about 12km from Mill City on a dirt road not tracked by Google Street View.
Google Earth shows signs of development near Mill City. Looks like a trailer park and a truck stop. The road to the mine looks freshly graded. Nothing at the mine site yet.
It's a good place for a mine. There are no neighbors for at least 10km, but within 15km, there's good road and rail access.
Well I guess this is a good win for short term energy infrastructure, though I'm always pretty torn when its at the cost of ripping open huge swaths of earth to get at the raw material.
It is interesting to see how much of this data could be modelled based on wastewater brines from other industries in the area, assuming we go on to mine the lithium it will say a lot if the ML predictions prove accurate.
One thing I couldn't tell, and its probably just a limitation of how much time I could spend reading the source paper, is what method would be needed to extract the bulk of the lithium expected to be there. If processing brine water is sufficient that may be easier to control externalities than if they have to strip mine and get all the overburden out of the way first.
Is lithium even rare enough to matter? I've read that the Salton sea may contain enough lithium to supply years of demand anyways. From my observation, it isn't the presence of lithium that matters. It's how to cheaply exploit it into a commercial product. For most purposes this just boils down to "mine it somewhere without environmental regulations"
Work in this industry (hard rock mining).
Lithium supply is not an issue. Here in oz we have plenty, there is surplus in market (see current lithium prices).
Conversion however is an issue, majority of plants are in China. Build some refiners that turn it into lithium carbonate and oz will fill them.
ugh i really don't want people to mine in the mobile basin. that's one of the most diverse ecosystems in north america. https://www.youtube.com/watch?v=8j9coyJeB4Q
Ah spatial autocorrelation, my old friend.
Very good work - but typically we don't build prospectivity models this way (or rather we don't validate them this way anymore). Great to see the USGS starting to dip their toe back in this though, they and the GSC were long the leaders in this, but have dropped it on the last 5-7 years.
Say Lithium becomes essentially free because we find so much of it…would that drastically lower battery costs? Is our current supply of lithium limiting production?
Similar work done in Canada: https://www.juniorminingnetwork.com/junior-miner-news/press-...
> "the amount of lithium present would meet projected 2030 world demand for lithium in car batteries nine times over"
Does that mean the entire field has enough lithium for the requirements of 2030, 9 times? Or in other words, it can supply the lithium needs of car batteries from 2030 to 2039? That's not particularly long...
Time to buy some cheap land in southern Arkansas?
Lithium for everyone!
https://www.nytimes.com/2024/10/21/business/energy-environme...
Oh, is this where the conspiracy theory about the government controlling hurricanes to wipe out the south so that they could get lithium came from?
If not that’s funny timing given that was a few weeks ago
Great, now ask the AI to engineer a fungal genome that'll help us purify it more easily: Frack in the substrate and spores, harvest fruit bodies on the surface, profit.
Serious question:
Given the mood alerting properties of lithium, are people living here chiller than would be expected (controlling for instance for poverty / SES) ?
Love to see a project that uses bog standard ML techniques and doesn't call them AI. Respect.
Me thinks we might switch batteries to sodium in just a few years.
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Will never happen, because, you know... The environment...
People in the U.S. would rather be slaves to China than be self sufficient as we once were...
I read the article carefully, twice. Doesn't have a link to any original paper, of course. And I can't find the answer to my question... did they, you know, validate the model? Did they actually take some samples at new locations and compare it to what the model says?
Or are they literally just announcing that "Hey, we told the computer to tell us something, so it told us something"? Yes, that is how it works. The computer will always tell you something if you make it tell you something. That isn't the hard part. The hard part is getting it to tell you things that correspond to reality.
In the absence of validation, this means very little, especially in an environment where the USGS is fairly incentivized to loudly announce to the world that we've totes got plenty of lithium, my fellow countries, any effort to keep lithium away from us is just a waste of time, look at us just rolling in lithium over here.
Or, maybe they did do the validation, and it's just the reporting that doesn't consider that an important aspect of the story. Somewhere between funding and press release someone's lost the trail but I don't know who exactly.
Interesting, & not necessarily in a good way. This method could well presage unprecedented numbers of attempts at eminent domain takings or other means of forcing people out of their properties.
From the paper's method section, a bit more about which type of ML algo was used:
An RF machine-learning model was developed to predict lithium concentrations in Smackover Formation brines throughout southern Arkansas. The model was developed by (i) assigning explanatory variables to brine samples collected at wells, (ii) tuning the RF model to make predictions at wells and assess model performance, (iii) mapping spatially continuous predictions of lithium concentrations across the Reynolds oolite unit of the Smackover Formation in southern Arkansas, and (iv) inspecting the model for explanatory variable importance and influence. Initial model tuning used the tidymodels framework (52) in R (53) to test XGBoost, K-nearest neighbors, and RF algorithms; RF models consistently had higher accuracy and lower bias, so they were used to train the final model and predict lithium.
Explanatory variables used to tune the RF model included geologic, geochemical, and temperature information for Jurassic and Cretaceous units. The geologic framework of the model domain is expected to influence brine chemistry both spatially and with depth. Explanatory variables used to train the RF model must be mapped across the model domain to create spatially continuous predictions of lithium. Thus, spatially continuous subsurface geologic information is key, although these digital resources are often difficult to acquire.
Interesting to me that RF performed better the XGBoost, would have expected at least a similar outcome if tuned correctly.