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Proposed sessions

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1. AI-driven Mineral Prospectivity Modeling

   Emmanuel John Carranza, Renguang Zuo

   Mineral prospectivity modeling as a computer-based approach to delineate target areas for exploration of certain mineral deposits in a mineral system has evolved from being knowledge driven to artificial intelligence (AI)-driven. The applications of AI in mineral exploration are ever increasing nowadays to address the complexity of relationships among datasets and with known deposit occurrences. The session welcomes submissions for presentations of: (1) novel AI algorithms and applications for recognition and integration of geo-anomalies to support mineral exploration, in 2D or 3D; and (2) novel AI algorithms and applications for analysis and synthesis of a variety of geoscience datasets to model mineral prospectivity and associated uncertainty, in 2D or 3D.
2. Short Course on Mathematical Morphological (Spatial) Algorithms in Surfaces

   B. S. Daya Sagar

   Many micro-scale to macro-scale surfaces represented in numerous ways—(e.g.: Digital Elevation Models: an Important Source of Data for Geoscientists, IEEE Geoscience and Remote Sensing Magazine, v. 8, no. 4, p. 138-142. 10.1109/MGRS.2020.3031910; rock surfaces; floodplain surfaces, material and mineral surfaces, etc))—contains rich geometric, morphologic and topologic information, but hidden for naked eyes. Unraveling such information from the surfaces of relevance to the Earth and Planetary sciences is a challenge. Such information from time-dependent and time independent surfaces is essential to model and visualize terrestrial and planetary surficial phenomena and processes. Geoscientists with appropriate mathematical knowledge can better exploit the full potential of the surfaces that have hitherto been analyzed via classical mathematical and statistical techniques. Mathematical Morphology is an area of geoscience that most people don’t realize will literally change the way they look at the surfaces of relevance to Earth and Planetary studies! This short course would provide a glimpse of how mathematical morphology could be employed to treat surfaces to derive scientific outcomes. Geoscience communities involving all its sub-branches some way or the other are familiar with surfaces of varied types acquired through varied sensing mechanisms. Mathematical morphology, a powerful artificial intelligence tool, is the basis for developing the spatial morphological algorithms that deal with those surfaces of relevance to the Earth and Planetary sciences. With the advent of the availability of accurate data at multiple spatial, spectral and temporal scales from various sources, the simulation, reconstruction, and prediction of surficial changes and processes on discrete time scales are feasible. With over three and a half decades of knowledge in showing applications of mathematical morphology, fractal geometry and chaos theory in understanding terrestrial phenomena and processes, Professor Daya Sagar deals with first lecturing on Mathematical Morphology, followed by analyzing such surfaces through mathematical morphology. This proposed tutorial would provide required knowledge to the interested audience on mathematical morphology in analyzing surfaces in a firm quantitative manner. 1) Audience would gain knowledge on morphological thinking to better understand the terrestrial and planetary surfaces processes. 2) Audience would be able to employ mathematical morphological operations and transformations in studies related to geosciences, remote sensing and geospatial data sciences. 3) Audience would be able to reason the importance of differential morphology in the context of studying the surficial morphological changes of relevance to terrestrial and planetary studies, with examples on rock surfaces, porous-medium, DEMs. 4) Audience with reasonable knowledge in basic set theory and the surfaces would benefit significantly in understanding the morphological equations, illustrations, and spatial (morphological) algorithms that would be explained in a span of six hours. The following material would be provided to the audience: 1) Electronic version of my book on "Mathematical Morphology in GISci and Geomorphology", CRC Press, p. 546, 2013. 2) Several papers of the instructor and his group published in journals. 3) Laurent Najman, Junior Barrera, B. S. Daya Sagar, Petros Maragos, and Dan Schonfeld (Eds.), Special Issue on 'Filtering and Segmentation with Mathematical Morphology' IEEE Journal of Selected Topics in Signal Processing, v. 6, no. 7, p. 737-886, 2012. 4) Handbook of Mathematical Geosciences, Springer, Cham, p. 942, 2018 (Openly Accessible) Special Requirements include Black or White Board with pieces of chalk, and an LCD projector to show some results, and mostly (for about 3 to 4 hours), board would be used. Books and/or Edited Special Issues of Journals: -B. S. Daya Sagar and Jean Serra (Eds.), Special Issue on 'Spatial Information Retrieval, Analysis, Reasoning and Modelling', International Journal of Remote Sensing, v. 31, no. 22, p. 5747-6032, 2010. -B. S. Daya Sagar (Monograph), Mathematical Morphology in Geomorphology and GISci (2013), ISBN-10: 1439872007, ISBN-13: 9781439872000. Pages: 546, Publisher: CRC Press (Taylor & Francis Group), A Chapman & Hall Book, Boca Raton, Florida, USA. -Laurent Najman, Junior Barrera, B. S. Daya Sagar, Petros Maragos, and Dan Schonfeld (Eds.), Special Issue on 'Filtering and Segmentation with Mathematical Morphology' IEEE Journal of Selected Topics in Signal Processing, v. 6, no. 7, p. 737-886, 2012. -B. S. Daya Sagar, Qiuming Cheng, and Frits Agterberg (Editors), 2018, Handbook of Mathematical Geosciences: Fifty Years of IAMG, Springer, Cham, Pages 942. Instructor's Brief Bio: B. S. Daya Sagar is a Professor (Higher Administrative Grade) of the Systems Science and Informatics Unit (SSIU) and the Director of the Indian Statistical Institute – Bangalore Centre. Sagar received his MSc and PhD degrees in Geoengineering and Remote Sensing from the Faculty of Engineering, Andhra University, Visakhapatnam, India, in 1991 and 1994, respectively. He is also the Founding Head of the SSIU. Earlier, he worked in the College of Engineering, Andhra University, and the Centre for Remote Imaging Sensing and Processing (CRISP), The National University of Singapore, in various positions from 1992 to 2001. He served as associate professor and researcher in the Faculty of Engineering & Technology (FET), Multimedia University, Malaysia, from 2001 to 2007. Sagar has made significant contributions to geosciences, with particular emphasis on developing spatial algorithms meant for geo-pattern retrieval, analysis, reasoning, modelling, and visualization by using mathematical morphology and fractal geometry concepts. He has published over 90 papers in journals and has authored and guest-edited 14 books and special theme issues for journals. He authored a book entitled "Mathematical Morphology in Geomorphology and GISci," CRC Press: Boca Raton, 2013, p. 546. He co-edited two special issues on "Filtering and Segmentation with Mathematical Morphology" for IEEE Journal of Selected Topics in Signal Processing (v. 6, no. 7, p. 737-886, 2012), and "Applied Earth Observation and Remote Sensing in India" for IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing (v. 10, no. 12, p. 5149-5328, 2017). His book "Handbook of Mathematical Geosciences", Springer Publishers, p. 942, 2018, crossed 1.30 Million downloads. His two-volume Encyclopedia of Mathematical Geosciences (Springer Nature, 1744 pages) is released in 2023. He was elected as a member of the New York Academy of Sciences in 1995, as a Fellow of the Royal Geographical Society in 2000, as a Senior Member of IEEE Geoscience and Remote Sensing Society in 2003, as a Fellow of the Indian Geophysical Union in 2011, as a Fellow of the Indian Academy of Sciences (FASc) in 2022, the Indian National Science Academy (FNA) in 2024, and the International Artificial Intelligence Industry Alliance (FAIIA). He has also been a member of the American Geophysical Union since 2004 and a life member of the International Association for Mathematical Geosciences (IAMG). He delivered the "Curzon & Co - Seshachalam Lecture - 2009" at Sarada Ranganathan Endowment Lectures (SRELS), Bangalore, and the "Frank Harary Endowment Lecture - 2019" at International Conference on Discrete Mathematics - 2019 (ICDM - 2019). He was awarded the 'Dr. Balakrishna Memorial Award' of the Andhra Pradesh Academy of Sciences in 1995, the Krishnan Medal of the Indian Geophysical Union in 2002, the 'Georges Matheron Award - 2011 with Lectureship' of the IAMG, and the Award of IAMG Certificate of Appreciation - 2018. He is the Founding Chairman of the Bangalore Section IEEE GRSS Chapter. He has been appointed as an IEEE Geoscience and Remote Sensing Society (GRSS) Distinguished Lecturer (DL) for three years from 2020 to 2024. He is an AGU Honors & Recognition Committee (HRC) Member for 2022-2025. He is on the Editorial Boards of Computers & Geosciences, Frontiers: Environmental Informatics, and Mathematical Geosciences. For more details about him, the following webpages may be referred to http://www.isibang.ac.in/~bsdsagar, https://en.wikipedia/wiki/B._S._Daya_Sagar, https://in.linkedin.com/in/bs-daya-sagar-a015495
3. Computational methods for quantifying hydrocarbon resource potential and exploration risk

   Jingdu Yu, Weihong Liu, Qian Zhang

   Advanced algorithms in hydrocarbon exploration and resource assessment, as well as risk computation, provide essential insights and strategies for exploration activities. This topic encompasses not only oil and gas but also resources such as uranium in sediments and helium in gas reservoirs. Developing methods for risk and potential analysis to achieve a higher degree of quantification is at the forefront of both theoretical and practical advancements in geology and exploration decision-making. We welcome the submission of methods and abstracts related to data fusion, geological information extraction, resource potential computation, risk quantification models for forecasting, seismic and well logging attributes from data processing, and mathematical data applications. Submissions should specifically link to hydrocarbon and associated resource assessment technologies. This topic holds significant potential and appeal for academic journals focusing on technological advancements and their practical applications in hydrocarbon exploration and exploitation.
4. Big Data Mining & Artificial Intelligence in Solid Earth Science

   Yongzhang Zhou, Marshall (Xiaogang) Ma, Hui Yang, Craig A. Knoblock

   Big data and machine learning have brought earth resource and environmental study into an artificial-intelligence research stage. Big data mining, machine learning and artificial intelligence algorithms and models have been applied to study multi-scale and multi-type ore deposit exploration and environmental observation. This session is devoted to highlight recent progress in the research and applications of big data and machine learning in the fields of earth science. We welcomes all big data and AI-driven ideas and research to address the development, especially current and future challenges in big data mining and machine learning in geoscience. It will cover the latest data science and machine learning advancements in combining such multi-disciplinary data to enhance sustainable and efficient decision-making in ore deposit exploration and environmental observations. Studies using new data-driven approaches for geochemical data analysis, geological modeling, geophysical inversion, mineral exploration, decision-making under geological uncertainty, are encouraged.
5. Geoinformatics for Mineral Exploration: Integrated Spatial Intelligence and Target Generation

   Mana Rahimi, Vesa Nykänen

   Geoinformatics brings together spatial data, digital mapping, and quantitative modelling to support mineral exploration from regional analysis to prospect-scale target definition. Today’s exploration teams work with multi-source datasets (geological, geophysical, geochemical, and Earth observation) across 2D, 3D, and time, and need integrated spatial intelligence to turn these complex data landscapes into actionable targets within realistic time and budget constraints. This session provides a forum for innovative concepts, methods, and operational workflows in applied geoinformatics for mineral exploration. We welcome contributions demonstrating how integrated spatial approaches—combining mapping, modelling, databases, visualization, and decision support—improve exploration targeting, support mineral systems thinking, and bridge the gap between research tools and exploration practice. Both methodological advances and significant case studies from industry, government, and academia are encouraged. Indicative topics include (but are not limited to): • Geoinformatics workflows for mineral exploration and target generation from multi-source data • Innovative approaches to spatial data processing and integration of geological, geophysical, geochemical, and remote sensing/Earth observation data • Mineral systems–based modelling applied to spatial prospectivity mapping for concept-to-target exploration workflows • Exploratory Data Analysis (EDA) to support data pre-preprocessing in mineral prospectivity mapping and target generation • Design and use of exploration geodatabases, data infrastructures, and FAIR geodata practices • Advanced methodologies for processing and analysing multi-/hyperspectral and multi-temporal remote sensing data in mineral exploration and target generation • GIS- and remote sensing–based methods for 2D/3D/4D geo-mapping and alteration/mineral system characterization • Data-, knowledge-, and hybrid-driven spatial models for exploration targeting and decision-making in mineral exploration • Multi-criteria and multi-scale spatial decision-support approaches in exploration • Interactive geo-visualization and dashboard-based tools for mineral exploration data and models. The emphasis is on advanced, practical geoinformatics solutions that increase the success of modern mineral exploration and help solve complex spatial problems in targeting and decision-making