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.
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.
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.
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.
Animats ·3 hours ago
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.
[1] https://en.wikipedia.org/wiki/Thacker_Pass_lithium_mine
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folli ·5 hours ago
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.
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_heimdall ·2 hours ago
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.
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tommykins ·2 hours ago
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.
CHB0403085482 ·22 minutes ago
https://www.nytimes.com/2024/10/21/business/energy-environme...
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