Codebook vectors and predicted rare earth potential from a trained emergent self-organizing map displaying multivariate topology of geochemical and reservoir temperature data from produced and geothermal waters of the United States

This data release consists of three products relating to a 82 x 50 neuron Emergent Self-Organizing Map (ESOM), which describes the multivariate topology of reservoir temperature and geochemical data for 190 samples of produced and geothermal waters from across the United States. Variables included in the ESOM are coordinates derived from reservoir temperature and concentration of Sc, Nd, Pr, Tb, Lu, Gd, Tm, Ce, Yb, Sm, Ho, Er, Eu, Dy, F, alkalinity as bicarbonate, Si, B, Br, Li, Ba, Sr, sulfate, H (derived from pH), K, Mg, Ca, Cl, and Na converted to units of proportion. The concentration data were converted to isometric log-ratio coordinates (following Hron et al., 2010), where the first ratio is Sc serving as the denominator to the geometric mean of all of the remaining elements (Nd to Na), the second ratio is Nd serving as the denominator by the geometric mean of all of the remaining elements (Pr to Na), and so on, until the final ratio is Na to Cl. Both the temperature and log-ratio coordinates of the concentration data were normalized to a mean of zero and a sample standard deviation of one. The first table is the mean and standard deviation of all of the data in this dataset, which is used to standardize the data. The second table is the codebook vectors from the trained ESOM where all variables were standardized and compositional data converted to isometric log-ratios. The final tables provides are rare earth element potentials predicted for a subset of the U.S. Geological Survey Produced Waters Geochemical Database, Version 2.3 (Blondes et al., 2017) through the used of the ESOM. The original source data used to create the ESOM all come from the U.S. Department of Energy Resources Geothermal Data Repository and are detailed in Engle (2019).