Filtering by Subject: Computer Sciences
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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, [...]
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 [...]
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 [...]