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. ).

Ambient seismic noise, once regarded as a nuisance, is now a core part of the seismological toolkit. Tomographic images are constructed from surface waves within small arrays and on the continental scale. Reflected waves are recovered from cross- and autocorrelations of the ambient field at local and teleseismic distances. Temporal variations of wave velocities and impedance structure are observed in the very shallow subsurface and at significant depth and led to the discovery of dynamic processes in the subsurface with relevance for earthquake triggering and relaxation, volcano and landslide dynamics as well as for the production from hydrocarbon reservoirs and geothermal fields. Established techniques are now routinely applied but new types of applications and continuing developments of new processing strategies constantly extend the capabilities of the noise based techniques. In addition, there are many unknowns related to the distributed and temporally variable sources of ambient vibrations. This variability affects the stability of seismic ‘noise correlation’ signals which leads to uncertainties in the seismic images and complicated time-lapse observations.

In this session, we focus on open questions and methodological advances in seismic interferometry and ambient noise based seismology. We invite (A) contributions on new methodological approaches in seismic interferometry and noise processing (B) studies of time variations of elastic material properties and (C) investigations of the sources of the ambient seismic noise.

This extends to evaluations of the accuracy of noise-based measurements for use in tomography or time-dependent imaging. It includes theoretical advances, such as the use of deconvolution or those exploring the role of source distribution or scattering, as well as methodological improvements and alternative processing techniques aimed at enhancing the quality of the correlations. Understanding the noise generation processes (microseisms, hum, microbaroms, etc) and causes of temporal variations of the noise field and the medium properties (dynamic and static stress changes, hydrology, etc.), and their effects on noise correlations is of fundamental interest in this context.

Invited speaker: Lise Retailleau (Stanford, USA)

The aim of this session is to present the latest research and case studies related to various data analysis and improvement methods and modeling techniques, and demonstrate their applications from the various fields of earth sciences like: hydrology, geology and paleogeomorphology, to geophysics, seismology, environmental and climate change.

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

This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series and associated uncertainties from all geoscientific disciplines.

Methods to be discussed include, but are not limited to:
- linear and nonlinear methods of time series analysis
- time-frequency methods
- predictive approaches
- statistical inference for nonlinear time series
- nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data
- nonlinear correlation analysis and synchronisation
- surrogate data techniques
- filtering approaches and nonlinear methods of noise reduction

We particularly encourage submissions addressing the problem of uncertainty of geoscientific time series and its treatment in the context of statistical and dynamical analysis, including:
- representation of time series with uncertain dating (in particular paleoclimatic records from ice cores, sediments, speleothems etc.)
- uncertainties in change point / transition detection
- uncertainty propagation in time series methods like correlation, synchronization, spectral analysis, PCA, networks, and similar techniques
- uncertainty propagation in empirical (i.e., data-derived) inverse models

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