Preprints

Filtering by Subject: Software Engineering

fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model

Jeremy James McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann Dahm, Eddie Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias Wicky, Christopher S. Bretherton, Oliver Fuhrer

Published: 2021-03-06
Subjects: Atmospheric Sciences, Software Engineering

Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, these codes are often hard to understand, hard to modify and hard to interface with high-productivity languages used for exploratory work. \texttt{fv3gfs-wrapper} is an open-source Python-wrapped version of NOAA's FV3GFS global [...]

Inversionson: Fully Automated Seismic Waveform Inversions

Solvi Thrastarson, Dirk-Philip van Herwaarden, Andreas Fichtner

Published: 2021-03-03
Subjects: Geophysics and Seismology, Numerical Analysis and Scientific Computing, Software Engineering

We present Inversionson, a Python package that fully automates modern full-waveform inversions (FWI). It supports traditional FWI, which uses the same set of events and a single simulation mesh in each iteration, as well as more advanced workflows that exploit the use of dynamic mini-batches and wavefield-adapted meshes. These recently introduced advancements can be time-consuming and [...]

LASIF: LArge-scale Seismic Inversion Framework, an updated version

Solvi Thrastarson, Dirk-Philip van Herwaarden, Lion Krischer, Andreas Fichtner

Published: 2021-01-07
Subjects: Geophysics and Seismology, Numerical Analysis and Scientific Computing, Software Engineering

Recent methodological advances and increases in computational power have made it feasible to perform full-waveform inversions (FWI) of large domains while using more sources. This trend, along with the increasing availability of seismic data has led to an explosion of the data volumes that can, and should, be used within an inversion. Similar to machine learning problems, the incorporation of [...]

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 [...]

Digital photogrammetry of historical aerial photographs using open-source software

Jose Ramon Martinez Batlle

Published: 2018-07-06
Subjects: Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics, Programming Languages and Compilers, Software Engineering

Several collections of aerial photographs have been acquired in the Dominican Republic during the last 70 years. Although many of these sources are increasingly becoming available as scanned images, limited digital photogrammetric processing has been done, mainly because of the unaffordable prices of proprietary software licenses and the lack of clear workflows for processing historical photos. [...]

Fish species classification in underwater video monitoring using Convolutional Neural Networks

Frederik Kratzert, Helmut Mader

Published: 2018-05-15
Subjects: Animal Sciences, Aquaculture and Fisheries Life Sciences, Computer Sciences, Environmental Monitoring, Environmental Sciences, Life Sciences, Other Life Sciences, Physical Sciences and Mathematics, Software Engineering

This report presents a case study for automatic fish species classification in underwater video monitoring of fish passes. Although the presented approach is based on the FishCam monitoring system, it can be used with any video-based monitoring system. The presented classification scheme in this study, is based on Convolutional Neural Networks that do not require the calculation of any [...]

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