Predicting the Concentrations of Rare Earth Elements and Yttrium in Coal Using Self-Organizing Map - Na Xu; Fei Li; Wei Zhu; Mark A. Engle; Jiapei Kong; Pengfei Li; Qingfeng Wang; Lishan Shen; Robert B. Finkelman; Shifeng Dai (Natural Resources Research, 2025)

Intelligent identification of coal macerals using improved semi-supervised semantic segmentation methods - Na Xu; Qingfeng Wang; Pengfei Li; Jiapei Kong; Qing Li; Mark A. Engle; James C. Hower; Wei Zhu (International Journal of Coal Geology, 2025)

Visualizing high dimensional structures in geochemical datasets using a combined compositional data analysis and Databionic swarm approach - Mark A. Engle; Julien Chaput (International Journal of Coal Geology, 2023)

Modes of occurrence of elements in high-germanium coals using correlation analysis algorithm - Na Xu; Wei Zhu; Bin Huang; Mark A. Engle; Qiang Li; Robert B. Finkelman (Applied Geochemistry, 2023)

Machine Learning Can Assign Geologic Basin to Produced Water Samples Using Major Ion Geochemistry - Jenna L. Shelton; Aaron M. Jubb; Samuel W. Saxe; Emil D. Attanasi; Alexei V. Milkov; Mark Engle; Philip A. Freeman; Christopher A. Shaffer; Madalyn S. Blondes (Natural Resources Research, 2021)
Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO 3 ), Ca, Na, Cl, Mg, and sulfate (SO 4 ). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO 3 , Ca, Na, Cl, Mg, SO 4 , pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.

Weighted Symmetric Pivot Coordinates for Compositional Data with Geochemical Applications - Karel Hron; Mark Engle; Peter Filzmoser; Eva Fišerová (Mathematical Geosciences, 2021)

Spatial variability of produced-water quality and alternative-source water analysis applied to the Permian Basin, USA - Binod K. Chaudhary; Robert Sabie; Mark A. Engle; Pei Xu; Spencer Willman; Kenneth C. Carroll (Hydrogeology Journal, 2019)

Advances in self-organizing maps for their application to compositional data - Josep A. Martín-Fernández; Mark A. Engle; Leslie F. Ruppert; Ricardo A. Olea (Stochastic Environmental Research and Risk Assessment, 2019)

Atmospheric particulate matter in proximity to mountaintop coal mines: sources and potential environmental and human health impacts - Laura Kurth; Allan Kolker; Mark Engle; Nicholas Geboy; Michael Hendryx; William Orem; Michael McCawley; Lynn Crosby; Calin Tatu; Matthew Varonka; Christina DeVera (Environmental Geochemistry and Health, 2015)

Three-way compositional analysis of water quality monitoring data - Mark A. Engle; Michele Gallo; Karl T. Schroeder; Nicholas J. Geboy; John W. Zupancic (Environmental and Ecological Statistics, 2014)

Interpretation of Na–Cl–Br Systematics in Sedimentary Basin Brines: Comparison of Concentration, Element Ratio, and Isometric Log-ratio Approaches - Mark A. Engle; Elisabeth L. Rowan (Mathematical Geosciences, 2013)

Application of environmental groundwater tracers at the Sulphur Bank Mercury Mine, California, USA - Mark A. Engle; Fraser Goff; David G. Jewett; Gregory J. Reller; Joel B. Bauman (Hydrogeology Journal, 2008)

Development of a Grassland Integrity Index Based on Breeding Bird Assemblages - Bryan R. Coppedge; David M. Engle; Ronald E. Masters; Mark S. Gregory (Environmental Monitoring and Assessment, 2006)