Geological and geophysical data provide quantitative information which permit the advancement of our understanding of the present, and past, interior of the Earth. Examples of such processes span from the internal structure of the Earth, plate kinematics, composition of geomaterials, estimation of physical conditions and dating of key geological events, thermal state of the Earth to more shallow processes such as reservoir geomechanics, or nuclear waste storage.

A quantitative understanding of the dynamics and the feedbacks between geological processes requires the integration of geological data with process oriented numerical models. Innovative inverse methods, linking forward dynamic models with observables, are topics of growing interest within the community. Improving our knowledge of the governing physical parameters can thus be addressed while reconciling models and observables.

Resolving the interactions between various processes occurring at scales differing from each other over several orders of magnitude in space and time represents a computational challenge. Hence, simulating such coupled, nonlinear physics-based forward models requires both the development of new approaches and the enhancement of established numerical schemes.

The majority of geological processes combine several physical mechanisms such as hydrological, thermal, chemical and mechanical processes (e.g. thermo-mechanical convection). Understanding the tight couplings among those processes represents a challenging and essential research direction. The development of novel numerical modelling approaches, which resolve multi-physics feedbacks, is vital in order to provide accurate predictions and gain deeper understanding of geological processes.

We invite contributions from the following two complementary themes:

#1 Computational advances associated with
- alternative spatial and/or temporal discretisations for existing forward/inverse models
- scalable HPC implementations of new and existing methodologies (GPUs / multi-core)
- solver and preconditioner developments
- code and methodology comparisons (“benchmarks”)
- open source implementations for the community

#2 Physics advances associated with
- development of partial differential equations to describe geological processes
- inverse and adjoint-based methods
- numerical model validation through comparison with natural observations and geophysical data
- scientific insights enabled by 2D and 3D modelling
- utilisation of coupled models to address nonlinear interactions

Analogue experiments and numerical simulation have become an integral part of the Earth explorer's toolbox to select, formulate, and test hypotheses on the origin and evolution of geological phenomena. In addition, a growing body of structural ground truth and geophysical observations as well as profound advances in remote sensing techniques offers to compare the modeled predictions with nature

To foster synergy between modelers and geologists focusing on field and geophysical or remote sensing data, we provide a multi-disciplinary platform to discuss research on tectonics, structural geology, rock mechanics, geodynamics, volcanology, geomorphology, and sedimentology.

We therefore invite contributions demonstrating the state-of-the-art in analogue and numerical / analytical modelling on a variety of spatial and temporal scales, varying from earthquakes and volcanic eruptions to plate tectonics and landscape evolution, as well as contributions focusing on remote sensing, geophysical and geodetic studies, with a specific focus on transpression. Local to crustal scale transpression is the most common deformation regime recognized at active and ancient plate boundaries formed by oblique plate convergence, and although the concept of strain partitioning is well established, the heterogeneity of transpressive deformation continues to be an important topic.

We especially welcome those presentations that discuss model strengths and weaknesses, challenge the existing limits, or compare/combine the different modelling techniques with observations from the natural world to realistically simulate and better understand the Earth's behavior.

This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, including deep learning and other advanced approaches
• Visualization and visual analytics of big data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

A key goal within geomorphic research is understanding the links between topographic form, erosion rates, and sediment production, transport and deposition. Numerical modelling, by allowing the creation of controlled analogues of natural systems, provides exciting opportunities to explore landscape evolution and generate testable predictions. Furthermore, the advancement of Earth surface monitoring capabilities in recent decades, such as the increasing availability of high-resolution topographic data and new techniques for constraining rates of erosion and deposition, allows the direct testing of numerical models at larger spatial and temporal scales than previously possible. Combining these different techniques provides exciting opportunities for furthering our understanding of Earth surface processes.

In this session, we invite contributions that use numerical modelling to investigate landscape evolution in a broad sense, and over a range of spatial and temporal scales. We welcome studies using models to constrain one or more of: erosion rates and processes, sediment production, transport and deposition, and sediment residence times. We also particularly wish to highlight studies that combine numerical modelling with direct Earth surface process monitoring techniques, such as topographic, field, stratigraphic, or geochronological data. There is no geographical restriction: studies may be focused on mountain environments or sedimentary basins, or they may establish links between the two; studies beyond planet Earth are welcome too.

Numerical modeling of earthquakes provides new approaches to apprehend the physics of earthquake rupture and the seismic cycle, seismic wave propagation, fault zone evolution and seismic hazard assessment.
Recent advances in numerical algorithms and increasing computational power enable unforeseen precision and multi-physics components in physics-based earthquake simulation but also pose challenges in terms of fully exploiting modern supercomputing infrastructure, realistic parameterization of simulation ingredients and the analysis of large synthetic datasets.
This session aims to bring together modelers and data analysts interested in the physics and computational aspects of earthquake phenomena. We welcome studies focusing on all aspects of the physics of various earthquakes - from slow slip events, fault mechanics and rupture dynamics, to wave propagation and ground motion analysis, to the seismic cycle and inter seismic deformation - and studies which further the state-of-the art in the related computational and numerical aspects.
We further encourage studies linking earthquake source processes to rock mechanics and the laboratory scale.

Increase in the amount of high quality seismic data and advances in high-performance computing in recent years have been transformative to explore Earth’s interior at all scales through seismic modelling, both in theory and practice. The goal of this session is to bring seismologists and computational scientists together to discuss recent advances and future directions in innovative forward & inverse modelling techniques, HPC systems & computational tools as well as the related theory and scientific outcomes.

We encourage contributions in the field of theoretical and computational seismology highlighting, but not limited to;

- advancements in numerical solvers and techniques,
- seismic codes on emerging CPU/GPU architectures
- full-waveform inversion from local to global scales,
- Bayesian inverse problems,
- machine learning algorithms for seismic problems,
- big data (seismic & computational) problems,
- large-scale workflows on HPC systems and their automatization,
- optimization strategies,
- uncertainty analysis for large-scale imaging,
- seismological results of HPC applications from passive (earthquakes and noise) and active seismic sources,
- visualization (parallel, VR platforms, etc. ).