Preprints

Filtering by Subject: Computer Sciences

Goal-Oriented Error Estimation and Mesh Adaptation for Shallow Water Modelling

Joseph Wallwork, Nicolas Barral, Stephan C. Kramer, David Ham, Matthew Piggott

Published: 2019-12-31
Subjects: Applied Mathematics, Computer Sciences, Engineering, Non-linear Dynamics, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Partial Differential Equations, Physical Sciences and Mathematics

Numerical modelling frequently involves a diagnostic quantity of interest (QoI) - often of greater importance than the PDE solution - which we seek to accurately approximate. In the case of coastal ocean modelling the power output of a tidal turbine farm is one such example. Goal-oriented error estimation and mesh adaptation can be used to provide meshes which are well-suited to achieving this [...]

Advanced ML and AI Approaches for Proxy-based Optimization of CO2-Enhanced Oil Recovery in Heterogeneous Clastic Reservoirs

Watheq J. Al-Mudhafar, Dandina N Rao, Sanjay Srinivasan

Published: 2019-12-04
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Design of Experiments and Sample Surveys, Earth Sciences, Multivariate Analysis, Other Earth Sciences, Physical Sciences and Mathematics, Programming Languages and Compilers, Statistics and Probability

The EOS-compositional reservoir simulation, Design of Experiments, and Proxy Modeling were integrated to obtain the optimal future performance scenario and to construct the most accurate simplified model alternative to the complex reservoir flow simulation. This integrated workflow was adopted on a sector of the main pay/upper sandstone member in the South Rumaila oil field, located in Iraq. [...]

Quantifying Computational Efficiency of Adaptive Mesh Refinement for Shallow Water Solvers

Nicole Beisiegel, Cristóbal E. Castro, Jörn Behrens

Published: 2019-12-04
Subjects: Computer Sciences, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics

Non-uniform, dynamically adaptive meshes are a useful tool for reducing computational complexities for geophysical simulations that exhibit strongly localised features such as is the case for example for tsunami, hurricane or typhoon prediction. Theoretical insight for mesh-based numerical methods, however, is largely restricted to uniform meshes as they allow for a traditional definition of [...]

A grid for multidimensional and multivariate spatial representation and data processing.

Tobias Stål, Anya M. Reading

Published: 2019-11-22
Subjects: Computer Sciences, Earth Sciences, Geophysics and Seismology, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics

Researchers use 2D and 3D spatial models of multivariate data of differing resolutions and formats. It can be challenging to work with multiple datasets, and it is time consuming to set up a robust, performant, grid to handle such spatial models. We share a Python module that provides a framework for containing multidi- mensional data and functionality to work with those data. The module provides [...]

Visualizing the Availability of Temporally Structured Sensor Data

Daniel Nüst, Felix Bache, Arne Bröring, Christoph Stasch, Simon Jirka

Published: 2019-09-28
Subjects: Computer Sciences, Graphics and Human Computer Interfaces, Physical Sciences and Mathematics

With the development of new sensor technologies and the standardization of sensor data formats, sensor data from different sources becomes available for many applications. A crucial task is to get an overview on the spatial and temporal extent for which data is available before integrating the data into an application. This work presents an approach for accessing the necessary information about [...]

A GEO label for the Sensor Web

Daniel Nüst, Victoria Lush

Published: 2019-09-28
Subjects: Computer Sciences, Graphics and Human Computer Interfaces, Physical Sciences and Mathematics

The GEO label is a visual metadata summary that is designed to improve understandability of geospatial metadata. The amount of sensor data collected via and published in Sensor Webs is steadily increasing and thus the published metadata becomes more diverse, more complex and harder to understand. To mitigate this issue, we transfer the GEO label into the Sensor Web architecture in an encompassing [...]

Controls on the development and termination of failed continental rifts: Insights from the crustal structure and rifting style of the North Sea via ambient noise tomography

Emily Crowder, Nick Rawlinson, David G. Cornwell, Carmelo Sammarco, Erica Galetti, Andrew Curtis

Published: 2019-09-21
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Probability, Statistics and Probability

The mid to lower crust plays an important role in rift initiation and evolution, particularly when large scale sutures and/or terrane boundaries are present. These inherited features can focus strain or act as inhibitors to extensional deformation. Ancient tectonic features are known to exist beneath the iconic failed rift system of the North Sea making it the ideal location to investigate the [...]

NSB: an expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy

Johan Renaudie, David Lazarus, Patrick Diver

Published: 2019-09-19
Subjects: Computer Sciences, Databases and Information Systems, Earth Sciences, Paleontology, Physical Sciences and Mathematics, Stratigraphy

Thirty years ago, the Neptune Database was created to synthesize microfossil occurrences from the deep-sea drilling record. It has been used in numerous studies by both biologists and paleontologists of the evolution and distribution in space and time of marine microplankton. After decades of discontinuous development in various institutions, a significant overhaul of the system was made during [...]

Methods and Test Cases for Linking Physics-Based Earthquake and Tsunami Models

Elizabeth Madden, Michael Bader, Jörn Behrens, Ylona van Dinther, Alice-Agnes Gabriel, Leonhard Rannabauer, Thomas Ulrich, Carsten Uphoff, Stefan Vater, Stephanie Wollherr

Published: 2019-09-06
Subjects: Computer Sciences, Earth Sciences, Geology, Geophysics and Seismology, Other Computer Sciences, Physical Sciences and Mathematics

Despite the inter-dependence of long term deformation, earthquakes and tsunamis, few modelling approaches bridge these processes. To advance the understanding of tsunami generation and earthquake-tsunami interactions, we present new methods for linking physics-based models of subduction zone geodynamics and seismic cycling, three-dimensional dynamic earthquake rupture, and tsunami generation, [...]

Analog forecasting of extreme-causing weather patterns using deep learning

Ashesh Kumar Chattopadhyay, Pedram Hassanzadeh, Ebrahim Nabizadeh

Published: 2019-07-31
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computational Engineering, Computer Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based [...]

Segmentation of the Main Himalayan Thrust inferred from geodetic observations of interseismic coupling

Luca Dal Zilio, Romain Jolivet, Ylona van Dinther

Published: 2019-07-04
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Geology, Geomorphology, Geophysics and Seismology, Physical Sciences and Mathematics, Probability, Statistics and Probability, Tectonics and Structure

Mapping the distribution of locked segments along subduction megathrusts is essential for improving quantitative assessments of seismic hazard. Previous geodetic studies suggest the Main Himalayan Thrust (MHT) is homogeneously locked (or coupled) along its complete length over a down-dip extent of ~100 km. However, an increasing number of seismological and geophysical observations suggests the [...]

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM

Ashesh Kumar Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian

Published: 2019-06-20
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamic Systems, Earth Sciences, Fluid Dynamics, Non-linear Dynamics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory [...]

Guerrilla Badges for Reproducible Geospatial Data Science (AGILE 2019 Short Paper)

Daniel Nüst, Lukas Lohoff, Lasse Einfeldt, Nimrod Gavish, Marlena Götza, Shahzeib Tariq Jaswal, Salman Khalid, Laura Meierkort, Matthias Mohr, Clara Rendel

Published: 2019-06-19
Subjects: Computer Sciences, Earth Sciences, Physical Sciences and Mathematics

The building blocks of research are developing at an unprecedented pace. Data collection, analysis, interpretation, presentation, review, and publication take place completely on computers. The final product often is still a static document with only limited links to the underlying digital material, making transparency and reproducibility a challenge. In this work we apply the mechanism of badges [...]

Geometry and topology of estuary and braided river channel networks automatically extracted from topographic data

Matthew Hiatt, Willem Sonke, Elisabeth Addink, Wout Matthijs van Dijk, Marc van Kreveld, Tim Ophelders, Kevin Verbeek, Joyce Vlaming, Bettina Speckmann, Maarten G. Kleinhans

Published: 2019-06-18
Subjects: Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics

Automatic and objective extraction of channel networks from topography in systems with multiple interconnected channels, like braided rivers and estuaries, remains a major challenge in hydrology and geomorphology. Representing channelized systems as networks provides a mathematical framework for analyzing transport and geomorphology. In this paper, we introduce a mathematically rigorous [...]

Understanding Low Cloud Mesoscale Morphology with an Information Maximizing Generative Adversarial Network

Tianle Yuan

Published: 2019-06-05
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computer Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Generative adversarial networks (GANs) are a class of machine learning algorithms with two neural networks, one generator and one discriminator, playing adversarial games with each other. Information maximizing GANs (InfoGANs) is a particular GAN type that tries to maximize mutual information between a subset of latent variables and generated samples, thereby establishing a mapping between the [...]

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