BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Beaver Watershed Alliance - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.beaverwatershedalliance.org
X-WR-CALDESC:Events for Beaver Watershed Alliance
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20241103T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20250309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250821T120000
DTEND;TZID=America/Chicago:20250821T130000
DTSTAMP:20260425T131932
CREATED:20250804T195929Z
LAST-MODIFIED:20250804T195929Z
UID:10000135-1755777600-1755781200@www.beaverwatershedalliance.org
SUMMARY:Machine Learning as a Tool to Predict Lithium in Brines Webinar
DESCRIPTION:Join us on Aug 21\, 2025\, at noon for the next Arkansas Water Webinar! To register\, click here.\n\n\n\nTopic: Machine Learning as a Tool to Predict Lithium in Brines: An Example from the Smackover Formation of Southern Arkansas\n\n\n\nSpeaker: Katherine Knierim\, Hydrologist\, US Geological Survey\, Lower Mississippi – Gulf Water Science Center\, Little Rock\, Arkansas\n\n\n\n\nThe chemical composition of high salinity groundwater\, or brine\, is important to understand for quantifying the availability of both water and critical mineral resources. For example\, brines may have high concentrations of dissolved critical minerals\, such as lithium (Li)\, and provide an important economic resource as the world transitions to a greater reliance on Li-ion batteries. The Smackover Formation is part of a regionally important petroleum and brine system in the Gulf Coast region of the southern United States (U.S.) and includes high Li concentrations (> 400 mg/L) in the brines. In this study\, the U.S. Geological Survey and the Arkansas Department of Energy and Environment—Office of the State Geologist used published and newly (2022) collected brine Li concentration data to train a random-forest (RF) machine-learning model using geologic\, geochemical\, and temperature explanatory variables. Predicted brine Li concentrations from the RF model at approximately 1\,000 to 3\,000 meters depth across the Smackover Formation ranged from 3 to 420 mg/L. Uncertainty in the mapped RF model predictions—based on the 90th percentile prediction interval across the Li map—were used with formation thickness and porosity information to calculate the range of the Li mass in Smackover Formation brines. This study provides an example of using machine learning to predict deep brine chemistry for a critical mineral resource evaluation. For more info see: https://pubs.usgs.gov/publication/fs20243052/full
URL:https://www.beaverwatershedalliance.org/event/machine-learning-as-a-tool-to-predict-lithium-in-brines-webinar/
LOCATION:AR
CATEGORIES:Virtual
ATTACH;FMTTYPE=image/jpeg:https://www.beaverwatershedalliance.org/wp-content/uploads/2025/08/528282624_1309762727826024_5067891089923458684_n.jpg
ORGANIZER;CN="The Arkansas Water Resource Center (AWRC)":MAILTO:awrcwql@uark.edu
END:VEVENT
END:VCALENDAR