Geochemistry and Geologic Mapping

Latest papers

• Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai

  Margarita Veshchezerova, Egor Barashov, Evgenii Gusev, Michael R. Perelshtein, Arlan Kasymzhan, Bolat M. Kabaziev, Nurlan Y. Askarov · 10 September 2026

  Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the…

• A Large Open Multi-Energy Corpus of Soil Compaction Tests, with Machine-Learning Baselines

  Sompote Youwai, Chana Phutthananon, Warat Kongkitkul · 4 September 2026

  Every engineered fill is specified by a maximum dry density and an optimum moisture content. Each determination needs a full Proctor test. Published correlations rest on one to four hundred specimens, usually from one laboratory at one compactive energy, and are seldom released. This paper releases …

• MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity

  Yiran Zhang, Jinwen Liu, Daniel Su, Yisu Chen, Qiang Sun, Chris Gonzalez, Eun-Jung Holden, Marco Fiorentini, Wei Liu, Yihao Ding · 3 September 2026

  Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing prospectivity systems often provide only opaque scores or heatmaps. We present MineTRACE, a web-based system for evidence-grounded exploration of eight commodities: Cu, Au, Ni, W, S…

• Geometry-Aware Bayesian Quantification via Compositional Data Analysis

  Alejandro Moreo, Pablo Gonz\'alez, Juan Jos\'e del Coz · 7 July 2026

  Accurately estimating the unknown target label distribution is the critical first step for adapting to label shift. This task, widely known as quantification or class prevalence estimation, has recently seen significant advances through continuous KDE-based methods which model the density of multicl…

• Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling

  Boris Kriuk · 15 June 2026

  Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven classifiers trained on shallow surface proxies. Such models are blind to the subsurface physics that actually localises ore: heat advection, fluid flow, and lithology-dependent…

• Tree-Structured Orthonormal Decomposition of the Aitchison Simplex

  Daisuke Yamada, Qijun Zhang, Travis Pence, Barbara B. Bendlin, Federico Rey, Vikas Singh · 11 June 2026

  Compositional data -- vectors encoding relative proportions -- arise across scientific domains, including ecology, geochemistry, and genomics. The features in these data often come with known hierarchical structure (e.g., taxonomies, phylogenies, ontologies), yet existing methods either ignore this …

• Deep Single-Index Fr\'echet Regression

  Muqing Cui, Yidong Zhou, Su I Iao, Hans-Georg M\"uller · 8 June 2026

  Predicting outputs that are located in non-Euclidean spaces, such as probability distributions, networks, and symmetric positive-definite matrices, is becoming increasingly important in modern data analysis, particularly when inputs are high-dimensional. We propose DeSI (Deep Single-Index Fr\'echet …

• Explaining a probabilistic prediction on the simplex with Shapley compositions

  Paul-Gauthier No\'e, Miquel Perell\'o-Nieto, Jean-Fran\c{c}ois Bonastre, Peter Flach · 4 June 2026

  Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction. This requires a scalar prediction as in binary classification, whereas a multiclass probabilistic prediction is a…

• Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data

  Matthias Templ · 1 June 2026

  Compositional data must be analysed through log-ratios: scale invariance, the defining axiom of the field, leaves no alternative. The centred log-ratio divides by the geometric mean of every part, so a single contaminated component shifts every centred-log-ratio coordinate at once, displacing the lo…

• Coarse-to-Fine Domain Incremental Learning with Attentive Distillation for Mining Footprint Segmentation in Multispectral Imagery

  Alif Tri Handoyo, Vincent C. S. Lee, Rizka Widyarini Purwanto, Alex M. Lechner, Deanna Kemp, Muhamad Risqi U. Saputra · 26 May 2026

  Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio-environmental risks and impacts of mining, yet its progress is hindered by the scarcity of fine-grained annotated data. Although large-scale datasets with coarse …

• Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference

  Camille Touron, Gabriel V. Cardoso, Julyan Arbel, Pedro L. C. Rodrigues · 21 May 2026

  Compositional score-based approaches to simulation-based inference (SBI) approximate the posterior over a shared parameter given $n$ independent observations by aggregating individually learned posterior scores: currently, there are two main propositions of such methods (Geffner et al. (2023), Linha…

• LithoBench: Benchmarking Large Multimodal Models for Remote-Sensing Lithology Interpretation

  Jun Wang, Fengpeng Li, Hang Dong, Tianjin Huang, Wei Han · 11 May 2026

  Remote sensing lithology interpretation is fundamental to geological surveys, mineral exploration, and regional geological mapping. Unlike general land-cover recognition, lithology interpretation is a knowledge-intensive task that requires experts to infer rock types from various features, e.g., sub…

• Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution

  T. Ansah-Narh, G. Y. Afrifa, J. B. Tandoh, K. Asare, M. Addi, K. E. Yorke, D. M. A. Akpoley, K. Aidoo, S. K. Fosuhene · 4 May 2026

  Groundwater in the Densu Basin is increasingly threatened by heavy metal contamination, but conventional methods fail to capture the statistical complexity and spatial heterogeneity of pollution indicators. A key challenge is modelling the Heavy Metal Pollution Index (HPI), which is typically skewed…

• RetroMotion: Retrocausal Motion Forecasting Models are Instructable

  Royden Wagner, Omer Sahin Tas, Felix Hauser, Marlon Steiner, Dominik Strutz, Abhishek Vivekanandan, Jaime Villa, Yinzhe Shen, Carlos Fernandez, Christoph Stiller · 30 April 2026

  Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint trajectory distributions grows exponentially with the number of agents. Therefore, we decompose multi-agent motion forecas…

• GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation

  Yitong Zhou, Mingyue Cheng, Jiahao Wang, Qingyang Mao, Qi Liu · 29 April 2026

  Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequences. Despite recent progress, existing approaches typically formulate the problem as a static, single-step discriminative mapping. This static par…

• Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon

  Haodi Xu, Joshua Fan, Feng Tao, Lifen Jiang, Fengqi You, Benjamin Z. Houlton, Ying Sun, Carla P. Gomes, Yiqi Luo · 30 March 2026

  The increasing availability of large-scale observational data and the rapid development of artificial intelligence (AI) provide unprecedented opportunities to enhance our understanding of the global carbon cycle and other biogeochemical processes. However, retrieving mechanistic knowledge from these…

• GEAR: Geography-knowledge Enhanced Analog Recognition Framework in Extreme Environments

  Zelin Liu, Bocheng Li, Yuling Zhou, Xuanting Li, Yixuan Yang, Jing Wang, Weishu Zhao, Xiaofeng Gao · 20 March 2026

  The Mariana Trench and the Qinghai-Tibet Plateau exhibit significant similarities in geological origins and microbial metabolic functions. Given that deep-sea biological sampling faces prohibitive costs, recognizing structurally homologous terrestrial analogs of the Mariana Trench on the Qinghai-Tib…

• Local Mechanisms of Compositional Generalization in Conditional Diffusion

  Arwen Bradley · 16 March 2026

  Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying this ability remain unclear. To make this concrete, we study length generalization, the ability to gene…

• GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration

  Yihao Ding, Yiran Zhang, Chris Gonzalez, Eun-Jung Holden, Wei Liu · 16 March 2026

  Geochemical anomaly detection plays a critical role in mineral exploration as deviations from regional geochemical baselines may indicate mineralization. Existing studies suffer from two key limitations: (1) single region scenarios which limit model generalizability; (2) proprietary datasets, which …

• EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes

  Samuel Stockman, Daniel Lawson, Maximilian Werner · 12 March 2026

  For decades, classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of earthquakes. Recent advances have led to Neural Point Processes (NPPs), which promise greater flexibility and improvements o…

• QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration

  Meng Ye, Xiao Lin, Georgina Lukoczki, Graham W. Lederer, Yi Yao · 23 February 2026

  Mineral prospectivity mapping requires synthesizing heterogeneous geological knowledge, including textual deposit models and geospatial datasets, to identify regions likely to host specific mineral deposit types. This process is traditionally manual and knowledge-intensive. We present QueryPlot, a s…

• FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping

  Jowaria Khan, Alexa Friedman, Sydney Evans, Rachel Klein, Runzi Wang, Katherine E. Manz, Kaley Beins, David Q. Andrews, Elizabeth Bondi-Kelly · 19 February 2026

  Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling. The lack of samples leads to difficulty simulating their …

• Gold Exploration using Representations from a Multispectral Autoencoder

  Argyro Tsandalidou, Konstantinos Dogeas, Eleftheria Tetoula Tsonga, Elisavet Parselia, Georgios Tsimiklis, George Arvanitakis · 9 February 2026

  Satellite imagery is employed for large-scale prospectivity mapping due to the high cost and typically limited availability of on-site mineral exploration data. In this work, we present a proof-of-concept framework that leverages generative representations learned from multispectral Sentinel-2 image…

• HyDeMiC: A Deep Learning-based Mineral Classifier using Hyperspectral Data

  M. L. Mamud, Piyoosh Jaysaval, Frederick D Day-Lewis, M. K. Mudunuru · 27 January 2026

  Hyperspectral imaging (HSI) has emerged as a powerful remote sensing tool for mineral exploration, capitalizing on unique spectral signatures of minerals. However, traditional classification methods such as discriminant analysis, logistic regression, and support vector machines often struggle with e…

• The future of AI in critical mineral exploration

  Jef Caers · 3 December 2025

  The energy transition through increased electrification has put the worlds attention on critical mineral exploration Even with increased investments a decrease in new discoveries has taken place over the last two decades Here I propose a solution to this problem where AI is implemented as the enable…