Time Series Analysis in the Geosciences - Concepts, Methods and Applications
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
The mid-Pleistocene Transition (MPT) is a crucial changes in climate dynamics, leading us into our current regime of long, asymmetric glacial cycles. However, evidence about the differences in how climate behaved before and after the MPT remains sparse and we also lack evidence to decide between theories that aim to explain the MPT. Here we hope to gather new datasets that compare climate on either side of the MPT or that offer new evidence about glacial cycles before it. Modelling and conceptual work about the causes of the MPT are also wlecome. Finally we would like to hear about work that paves the way for new projects, including plans and methodologies to obtain pre-MPT ice cores such as (but not limited to) the IPICS Oldest Ice challenge, like Beyond EPICA and other endeavours.
New frontiers of multiscale monitoring, analysis and modeling of environmental systems
Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented in collecting studies relevant to understand multiscale aspects of these systems and in proposing adequate multi-platform surveillance networks monitoring tools systems. It is especially aimed to emphasize the interaction between environmental processes occurring at different scales. In particular, a special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring high natural risk areas, such as: volcanic, seismic, slope instability and other environmental context.
We expect contributions derived from several disciplines, such as applied geophysics, seismology, geodesy, geochemistry, remote sensing, volcanology, geotechnical and soil science. In this context, the contributions in analytical and numerical modeling of geodynamics processes are also welcome.
Finally, a special reference is devoted to the integration through the use of GeoWeb platforms and the management of visualization and analysis of multiparametric databases acquired by different sources
Earthquake foreshocks: identification, observation, modeling, and lessons to be learned
Over the past several years, interest in earthquake foreshocks has experienced considerable growth. This can, on one side, be explained by a largely improved observational database that spans all seismic scales. A development that is driven by a growing number of permanent seismic stations and large-scale campaign networks, the development of advanced detection and analysis techniques, and by the improvement of laboratory equipment and techniques. In addition, the ongoing endeavor to better understand induced seismicity has been contributing to this upgrowth with densely-monitored underground lab-scale experiments and enhanced microseismic monitoring. On the other side, earthquake foreshocks are widely perceived as one of the few and, as of now, most direct observations of earthquake nucleation processes.
Foreshocks are generally thought to arise by one of two mechanisms: cascading failure or preslip. The cascading model proposes that a mainshock following a foreshock has an identical origin to that of aftershocks. In this case, earthquake frequency-magnitude statistics predict that occasionally an aftershock will be larger than the prior event, which makes the prior event a foreshock only after the fact. The mechanism proposed by the preslip model is that premonitory processes - perhaps fault creep related to mainshock nucleation - result in stress changes that drive the foreshock process. Seismologists have found no agreement so far; this is made more difficult by two facts: that no agreed-upon, universal strategy to identify foreshocks in a seismic catalog exists and that data quality and quantity vary considerably over spatial and temporal scales.
In this session, we want to bring together scientists from all disciplines working on, or interested in, earthquake foreshock occurrence. We invite reports on observational and theoretical studies on all scales. This includes laboratory and deep underground experimental earthquakes, as well as microseismic to megathrust earthquakes. We also encourage submissions from colleagues working on advanced detection and analysis techniques for improved foreshock identification.
Challenges, potential and results from large-scale compilations of palaeoclimate data (co-sponsored by SISAL)
As the number of palaeoclimate data from glacial, marine, and continental archives is growing continuously, large-scale compilation and cross-comparison of these data is the imperative next phase in paleoclimate research. Large data sets require meticulous database management and new analysis methodologies to unlock their potential for revealing supra-regional and global trends in palaeoclimate conditions. The compilation of large scale datasets from proxy archives faces challenges related to record quality and data stewardship. This requires record screening and formulation of principles for quality check, as well as transparent communication.
This session aims to bring together contributions from paleoclimatic studies benefiting from the existence of such large data sets, e.g., providing a novel perspective on a proxy and the represented climate variables from the local to the global scale. We want to bridge the gap between data generation and modelling studies. In particular, comparing such large proxy-based datasets with climate modelling data is crucial for improving our understanding of palaeoclimate archives (e.g., bias effects and internal processes), to identify signal and noise components and their temporal dynamics, and to gain insight into the quality of model data comparisons.
We encourage submissions on data compilations, cross-comparison and modelling studies utilizing data repositories and databases (e.g., SISAL, PAGES2k, ACER, EPD), including, but not limited to:
-Comparative studies using one or several archives (e.g., including tests of temporal and spatial synchronicity of past regional to global climate changes)
-Proxy system models (and their tuning)
-Model data comparisons (including isotope enabled models or local calibration studies)
-Integrative multi-proxy/multi archive approaches at multiple study sites
-Large scale age model comparisons and record quality assessment studies, including methods aimed at cross validation between different records and variable spatial and temporal scales.
Imaging geodesy using InSAR for Earth System Science and Engineering
The availability of high spatial resolution Synthetic Aperture Radar (SAR) data, the advances in SAR processing techniques (e.g. interferometric, polarimetric, and tomographic processing), and the fusion of SAR with optical imagery as well as geophysical modelling allow ever increasing use of Imaging Geodesy using SAR/InSAR as a geodetic method of choice for earth system monitoring and investigating geohazard, geodynamic and engineering processes. In particular, the exploitation of data from new generation SAR missions such as Sentinel-1 that provide near real-time measurements of deformation and changes in land cover/use has improved significantly our capabilities to understand natural and anthropogenic hazards and then helped us mitigate their impacts. The development of high-resolution X-band SAR sensors aboard missions such as Italian COSMO-SkyMed (CSK) and German TerraSAR-X (TSX) has also opened new opportunities over the last decade for very high-resolution radar imaging from space with centimetre geometric accuracy for detailed analysis of a variety of processes in the areas of the biosphere, geosphere, cryosphere and hydrosphere. All scientists exploiting radar data from spaceborne, airborne and/or ground-based SAR sensors are cordially invited to contribute to this session. The main objective of the session is to present and discuss the progress, state-of-the-art and future perspectives in scientific exploitation of SAR data, mitigating atmospheric effects and error sources, cloud computing, machine learning and big data analysis, and interpretation methods of results obtained from SAR data for various types of disasters and engineering applications such as earthquakes, volcanoes, landslides and erosion, infrastructure instability and anthropogenic activities in urban areas. Contributions addressing scientific applications of SAR/InSAR data in biosphere, cryosphere, and hydrosphere are also welcome.