Main themes

  • Statistics
  • Geostatistics
  • Compositional Data Analysis
  • Fractals
  • Inverse problems and data assimilation
  • Machine Learning
  • Geohydrology
  • Geoinformatics
  • Computational Geophysics
  • Computational Geodynamics
  • Earth System modeling
  • Remote sensing and Image Analysis
  • Optimization
  • Geotechnical Engineering
  • Modeling of Coupled processes
  • Medical geology
  • Modeling of Earth Resources
  • Digital Outcrop models
  • Urban Geology
  • Risks and Uncertainties

Proposed sessions:

Click on the titles marked + to see a descriptions.
  1. +Beyond Gaussianity: what is the status of the GANs, MPS, Cumulants or Copula approaches?
    Julien Straubhaar, Thomas Mejer Hansen, Philippe Renard
    In this session, we invite authors to show their latest research in the field of non multi-Gaussian random fields. This includes for example Generative Adversarial Neural networks, Multiple-Point Statistics, High-order Cumulants, and Copula approaches. All aspects from new theoretical developments to practical applications, comparison of methods, success or failure stories are welcome.
  2. +Recent developments in machine learning techniques and quantum computing for geoscience applications
    Teeratorn Kadeethum, Daniel O’Malley, Hongkyu Yoon, Hamid M. Nick
    Recent advancements in machine learning techniques and quantum computing have made their way into geoscience research. These approaches have been adopted and proposed to tackle long-standing challenges in geoscience or an enhancement of classical methods that have been used in this field. This mini-symposium invites presentations on advances within areas of machine learning and/or quantum computing in geoscience research. Topics include, but are not limited to, (1) machine learning algorithms and applications for model-reduction, optimization, inverse problems, uncertainty quantification, highly parameterized problems (e.g., parametrization of heterogeneous fields), and efficient dimensionality reduction of nonlinear operators and (2) quantum computing applications in geoscience research; for instance, seismic inversion with quantum annealing, quantum-computational hydrologic inverse analysis, or quantum optimization. The mini-symposium will bring together researchers working on fundamental and applied aspects of machine learning and quantum computing to provide a forum for discussion, interaction, and assessment of their presented techniques.
  3. +Random patterns and shapes in spatio-temporal data
    Aila Sarkka, Radu Stoica
    This session aims to bring together specialists in point processes, spatial statistics, and stochastic geometry, who have a common research question: characterisation and detection of patterns and structures in spatio-temporal data. The complexity of the available data today requires rigorous stochastic methodologies based on probabilistic modelling, simulation algorithms, and Bayesian inference, in order to obtain reliable and interpretable results. The purpose of this session is to present some new mathematical methods which have been developed for answering questions in environmental sciences, forestry, image analysis, stereology, astronomy and other fields, and which can be valuable additions in the methodological toolbox for the statistical analysis of geological data.
  4. +Inverse problems
    Thomas Bodin, Kerry Gallagher
    Numerous advances in the Earth sciences are driven by innovative inverse modelling techniques, and the ever-increasing power of computational resources. To promote further progress, this session offers a platform to discuss and learn about current and future approaches to solve (nonlinear) inverse problems in the geosciences. Examples of novel applications are also very welcome. Of particular interest are contributions focusing on: - Seismic tomography and full-waveform inversion at all scales, - Uncertainty analysis, - Bayesian inference, - Optimization methods, - Joint inversion of disparate datasets, - Multi-scale, multi-parameter inversion, - Strategies for exploiting massive data volumes, - Effective medium theory, downscaling, and inverse homogenization.
  5. +Knowledge graphs in the cyberinfrastructure ecosystem of geosciences
    Xiaogang Ma, Chengbin Wang
    Vocabularies, schemas and ontologies have been increasingly created and applied in geosciences in the past decades, and have always been a topic of interest in geoinformatics. Inspired by the recent success of knowledge graphs in industry, the academia is beginning to use knowledge graph as an umbrella topic for works on vocabularies, schemas, and ontologies. In the geoscience community, we have seen many successful applications of knowledge graph in data curation and integration in the past decades, such as the global geologic map sharing enabled by OneGeology. Recently, there were also applications of knowledge graph together with machine learning algorithms to improve the quality of data analytics, such as those in hyperspectral remote sensing image processing, extension to mineral taxonomy, and petroleum exploration. Together, knowledge graphs have shown promising contribution to data science applications in geosciences, and more innovative developments are undergoing. This session welcomes submissions of all the topics mentioned above, to provide a venue for knowledge graph practitioners to share their results and experience, learn best practices, and discuss visions and potential collaborations for future work.
  6. +Fractured geological media and fracture networks: flow, graphs, morphology
    Rachid Ababou
    "Fractured geological media and fracture networks: flow, graphs, morphology." This session is an opportunity to present various approaches for analyzing geometrical, topological, and hydraulic properties of 3D Discrete Planar Fracture Networks (DPFN), or their 2D counterpart (e.g., discrete flow networks represented by intersecting segments in the plane), or other discrete sets of Boolean objects (conductors, barriers, cavities, etc.). These generalized geological "networks" may be described deterministically or statistically (e.g. as random Boolean objects), and/or, based on graph theory concepts. Extensions to other mechanisms focusing on morphology and structure of discrete fracture networks are also welcome (e.g., two-phase flow, mechanical deformation, electrical conduction…). This topic is also related to upscaling issues in fractured media and networks : these are relevant to this session in connexion with morphology and structure. Overall, this session focuses on mathematical and algorithmic approaches for characterizing geological media and/or discrete fracture networks, in terms of topology, morphology, hydraulic/conduction properties, based on field observations as well as models.
  7. +Computational Petrology and Geochemistry
    Pierre Lanari, Marion Garçon, Paolo Sossi
    Numerical modeling has become a critical aspect of modern research, especially in Earth Sciences as physical and chemical processes occurring in planetary interiors are not always directly observable from the surface. In petrology and geochemistry, a large variety of computational models have been developed to simulate and study these processes. To be realistic, computational experiments commonly rely on an algorithmic or mechanistic approach rather than deriving a mathematical analytical solution to the problem. In addition, the increase in the amount of geochemical data from state-of-the-art instruments (e.g. ICP-MS, microprobe, SIMS) has fostered the development of advanced software solutions for data reduction and interpretation. The ever-increasing size and availability of these datasets has, in turn, opened up new avenues for extracting statistical information on geological processes. The main goal of this session is to bring together geochemists, petrologists and data scientists who are either developing, using and/or applying numerical tools to understand geological processes. Topics of interest include (but are not limited to) geochemical and petrological modeling for fluids, melts and solids using major/trace elements or isotopes, thermodynamics and kinetics, thermo-mechanical simulations of petro/geochemical processes, provenance analysis, optimization and testing of databases. Model developers using machine learning, big data or minimization/inversion routines as well as those developing new techniques and tools for data visualization are particularly encouraged to submit an abstract.