Preprints

Filtering by Subject: Computer Sciences

Geological Facies Modeling Based on Progressive Growing of Generative Adversarial Networks (GANs)

Suihong Song, Tapan Mukerji, Jiagen Hou

Published: 2020-06-22
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geology, Geophysics and Seismology, Hydrology, Physical Sciences and Mathematics, Sedimentology, Stratigraphy

Geological facies modeling has long been studied to predict subsurface resources. In recent years, generative adversarial networks (GANs) have been used as a new method for geological facies modeling with surprisingly good results. However, in conventional GANs, all layers are trained concurrently, and the scales of the geological features are not considered. In this study, we propose to train [...]

Spatial Statistics on the Geospatial Web

Matthias Hinz, Daniel Nüst, Benjamin Proß, Edzer Pebesma

Published: 2020-06-17
Subjects: Computational Engineering, Computer Sciences, Engineering, Physical Sciences and Mathematics

The Geospatial Web provides data as well as processing functionality using web interfaces. Typical examples of such processes are models and predictions for spatial data, known as spatial statistics. Such analyses are written by domain experts in scripting languages and rarely exposed as web services. We present a concept of script annotations for automatic deployment in server runtime [...]

Large model parameter and structural uncertainties in global projections of urban heat waves

Zhonghua Zheng, Lei Zhao, Keith W. Oleson

Published: 2020-06-10
Subjects: Atmospheric Sciences, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Risk Analysis, Statistics and Probability

Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Reliable projections of these extremes are pressingly needed for local actions in the context of extreme event preparedness and mitigation. Such information, however, is not available because current multi-model projections largely lack a representation of urban areas. Here, we use a newly-developed urban climate emulator [...]

Stress Recovery for the Particle-in-cell Finite Element Method

Haibin Yang, Louis Moresi, John Mansour

Published: 2020-05-26
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Mathematics, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Tectonics and Structure

The interelement stress in the Finite Element Method is not continuous in nature, and stress projections from quadrature points to mesh nodes often causes oscillations. The widely used particle-in-cell method cannot avoid this issue and produces worse results when there are mixing materials of large strength (e.g., viscosity in Stokes problems) contrast in one element. The post-processing methods [...]

Hidden Stories: Topic Modeling in Hydrology Literature

Mashrekur Rahman, Jonathan Frame, Jimmy Lin, Grey Nearing

Published: 2020-05-25
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Hydrology, Library and Information Science, Physical Sciences and Mathematics, Social and Behavioral Sciences

Hydrologic research generates large volumes of peer-reviewed literature across a number of evolving sub-topics. It’s becoming increasingly difficult for scientists and practitioners to synthesize this full body of literature. This study explores topic modeling as a form of unsupervised learning applied to 42,154 article-abstracts from six high-impact (Impact Factor > 0.9) journals (Water [...]

Automated Seismic Source Characterisation Using Deep Graph Neural Networks

Martijn van den Ende, Jean-Paul Ampuero

Published: 2020-05-25
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics

Most seismological analysis methods require knowledge of the geographic location of the stations comprising a seismic network. However, common machine learning tools used in seismology do not account for this spatial information, and so there is an underutilised potential for improving the performance of machine learning models. In this work, we propose a Graph Neural Network (GNN) approach that [...]

Addressing Model Data Archiving Needs for the Department of Energy’s Environmental Systems Science Community

Maegen Simmonds, William J. Riley, Shreyas Cholia, Charuleka Varadharajan

Published: 2020-05-09
Subjects: Computer Sciences, Environmental Sciences, Environmental Studies, Physical Sciences and Mathematics, Social and Behavioral Sciences

Researchers in the Department of Energy’s ESS program use a variety of models to advance robust, scale-aware predictions of terrestrial and subsurface ecosystems. ESS projects typically conduct field observations and experiments coupled with modeling exercises using a model-experimental (ModEx) approach that enables iterative co-development of experiments and models, and ensures that experimental [...]

Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

Zhonghua Zheng, Jeffrey H. Curtis, Yu Yao, Jessica T. Gasparik, Valentine G. Anantharaj, Lei Zhao, Matthew West, Nicole Riemer

Published: 2020-05-07
Subjects: Atmospheric Sciences, Civil and Environmental Engineering, Computational Engineering, Computer Sciences, Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

This study integrates machine learning and particle-resolved aerosol simulations to develop emulators that predict sub-micron aerosol mixing state indices from the Earth System Model (ESM) simulations. The emulators predict aerosol mixing state using only ESM bulk aerosol species concentrations, which do not by themselves carry mixing state information. Here we used PartMC as the [...]

SymAE: an autoencoder with embedded physical symmetries for passive time-lapse monitoring

Pawan Bharadwaj, Matt Li, Laurent Demanet

Published: 2020-04-14
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics

We introduce SymAE, an auto-encoder architecture that learns to separate multichannel passive-seismic datasets into qualitatively interpretable components: one component corresponds to path-specific effects associated with subsurface properties while the other component corresponds to the spectral signature of the passive sources. This information is represented by two latent codes produced by [...]

RainDisaggGAN - Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

Sebastian Scher, Stefanie Peßenteiner

Published: 2020-03-31
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Hydrology, Physical Sciences and Mathematics

Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is an important topic in many geoscientific applications. From a statistical perspective, this presents a high-dimensional and highly under-determined problem. However, recent advances in unsupervised machine learning provide methods for learning such high-dimensional probability [...]

Toward stable, general machine-learned models of the atmospheric chemical system

Makoto Kelp, Daniel J. Jacob, J. Nathan Kutz, Julian D. Marshall, Christopher W. Tessum

Published: 2020-03-23
Subjects: Artificial Intelligence and Robotics, Chemistry, Computer Sciences, Earth Sciences, Environmental Chemistry, Environmental Sciences, Physical Sciences and Mathematics

Atmospheric chemistry models—used as components in models that simulate air pollution and climate change—are computationally expensive. Previous studies have shown that machine-learned atmospheric chemical solvers can be orders of magnitude faster than traditional integration methods but tend to suffer from numerical instability. Here, we present a modeling framework that reduces error [...]

A Serious Gaming Framework for Decision Support on Hydrological Hazards

Yusuf Sermet, Ibrahim Demir, Marian Muste

Published: 2020-03-17
Subjects: Civil and Environmental Engineering, Computer Sciences, Databases and Information Systems, Engineering, Environmental Education, Environmental Engineering, Environmental Sciences, Physical Sciences and Mathematics, Software Engineering, Sustainability, Water Resource Management

In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process [...]

Realistic River Image Synthesis using Deep Generative Adversarial Networks

Akshat Gautam, Muhammed Ali Sit, Ibrahim Demir

Published: 2020-02-20
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Engineering, Environmental Engineering, Physical Sciences and Mathematics

In this paper, we investigate an application of image generation for river satellite imagery. Specifically, we propose a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support models in surface water estimation, river meandering, wetland loss and other hydrological research studies. First, we summarized an augmented, [...]

Enabling high-performance cloud computing for Earth science modeling on over a thousand cores: application to the GEOS-Chem atmospheric chemistry model

Jiawei Zhuang, Daniel J. Jacob, Haipeng Lin, Elizabeth Lundgren, Robert Yantosca, Judit Flo Gaya, Melissa P. Sulprizio, Sebastian D. Eastham

Published: 2020-02-20
Subjects: Computer Sciences, Earth Sciences, Physical Sciences and Mathematics

Cloud computing platforms can facilitate the use of Earth science models by providing immediate access to fully configured software, massive computing power, and large input datasets. However, slow inter-node communication performance has previously discouraged the use of cloud platforms for massively parallel simulations. Here we show that recent advances in the network performance on the Amazon [...]

Hydro-morphodynamics 2D modelling using a discontinuous Galerkin discretisation

Mariana C A Clare, James Percival, Athanasios Angeloudis, Colin Cotter, Matthew Piggott

Published: 2020-01-10
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geomorphology, Numerical Analysis and Computation, Partial Differential Equations, Physical Sciences and Mathematics, Sedimentology

The development of morphodynamic models to simulate sediment transport accurately is a challenging process that is becoming ever more important because of our increasing exploitation of the coastal zone, as well as sea-level rise and the potential increase in strength and frequency of storms due to a changing climate. Morphodynamic models are highly complex given the non-linear and coupled nature [...]

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