Preprints

Filtering by Subject: Artificial Intelligence and Robotics

Using Synthetic Data Trained Convolutional Neural Network For Predicting Sub-Resolution Thin Layers From Seismic Data

Dongfang Qu, Klaus Mosegaard, Runhai Feng, et al.

Published: 2022-05-19
Subjects: Artificial Intelligence and Robotics, Geology, Geophysics and Seismology

Complex fault system revealed from 3-D seismic reflection data with deep learning and fault network analysis

Thilo Wrona, Indranil Pan, Rebecca E. Bell, et al.

Published: 2022-05-13
Subjects: Artificial Intelligence and Robotics, Geology, Geophysics and Seismology, Tectonics and Structure

Understanding where normal faults are is critical to an accurate assessment of seismic hazard, the successful exploration for and production of natural (including low-carbon) resources, and for the safe subsurface storage of CO2. Our current knowledge of normal fault systems is largely derived from seismic reflection data imaging intra-continental rifts and continental margins. However, [...]

Automated machine learning to evaluate the information content of tropospheric trace gas columns for fine particle estimates over India: a modeling testbed

Zhonghua Zheng, Arlene M. Fiore, Daniel M. Westervelt, et al.

Published: 2022-03-20
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Environmental Engineering, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology

India is largely devoid of high-quality and reliable on-the-ground measurements of fine particulate matter (PM2.5). Ground-level PM2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using [...]

Camera-Based Intelligent Stream Stage Sensing for Decentralized Environmental Monitoring

Yusuf Sermet, Ibrahim Demir

Published: 2022-03-02
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Electrical and Computer Engineering, Engineering, Environmental Monitoring, Water Resource Management

On average, flood damages cost $4.4 billion in the US annually. Accurate, vast, and real-time coverage of water level monitoring is crucial for the advancement of environmental research, specifically in the areas of climate change, water distribution, and natural disaster preparedness and management. According to a 2018 EPA report, there are 2.7 million streams and associated watersheds in the US [...]

GANSim-3D for conditional geomodelling: theory and field application

Suihong Song, Tapan Mukerji, Jiagen Hou, et al.

Published: 2021-12-22
Subjects: Artificial Intelligence and Robotics, Computational Engineering, Geology, Hydrology, Numerical Analysis and Scientific Computing, Oil, Gas, and Energy, Theory and Algorithms, Water Resource Management

Geomodelling of subsurface reservoirs is important for water resources, hydrocarbon exploitation, and Carbon Capture and Storage (CCS). Traditional geostatistics-based approaches cannot abstract complex geological patterns and are thus not able to simulate very realistic earth models. We present a Generative Adversarial Networks (GANs)-based 3D reservoir simulation framework, GANSim-3D, which can [...]

Bridging knowledge gaps with hybrid machine-learning forest ecosystem models (ML-FEMs): inferential simulation of past understory light regimes

Adam Michael Erickson, Craig Nistchke

Published: 2021-10-29
Subjects: Artificial Intelligence and Robotics, Biodiversity, Biogeochemistry, Computer Sciences, Earth Sciences, Ecology and Evolutionary Biology, Forest Sciences, Life Sciences, Physical Sciences and Mathematics, Plant Sciences

Soil moisture is a key limiting factor of plant productivity in boreal and montane regions, producing additional climate feedbacks through evaporation, regeneration, mortality, and respiration. Understory solar irradiation – the primary driver of surface temperature and evaporative demand – remains poorly represented in vegetation models due to a lack of 3-D canopy geometry. Existing models are [...]

Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data

Martijn van den Ende, André Ferrari, Anthony Sladen, et al.

Published: 2021-09-23
Subjects: Artificial Intelligence and Robotics, Geophysics and Seismology, Transportation Engineering

Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future "smart city" developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic [...]

Ten Simple Rules for Researchers Who Want to Develop Web Apps

Sheila M. Saia, Natalie G. Nelson, Sierra N. Young, et al.

Published: 2021-07-18
Subjects: Agricultural Science, Agriculture, Applied Statistics, Artificial Intelligence and Robotics, Bioresource and Agricultural Engineering, Computer Sciences, Databases and Information Systems, Environmental Monitoring, Graphics and Human Computer Interfaces, Natural Resources and Conservation, Software Engineering, Terrestrial and Aquatic Ecology, Water Resource Management

Growing interest in data-driven, decision-support tools across the life sciences and physical sciences has motivated development of web applications, also known as web apps. Web apps can help disseminate research findings and present research outputs in ways that are more accessible and meaningful to the general public--from individuals, to governments, to companies. Specifically, web apps enable [...]

Advancing flood warning procedures in ungauged basins with machine learning.

Zimeena Rasheed, Akshay Aravamudan, Ali Gorji Sefidmazgi, et al.

Published: 2021-05-27
Subjects: Artificial Intelligence and Robotics, Hydrology

Flood prediction across scales and more specifically in ungauged areas remains still a great challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. Building upon the recent success of Machine Learning (ML) models on streamflow prediction, this work presents a prototype ML-based framework for flood warning and flood peak prediction. The fundamental [...]

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Xiaogang Ma

Published: 2021-05-04
Subjects: Artificial Intelligence and Robotics, Databases and Information Systems, Earth Sciences, Environmental Sciences, Numerical Analysis and Scientific Computing, Theory and Algorithms

Data science receives increasing attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions have begun to increase in many geoscience conferences. Across academia, industry, and governmental sectors, there is a strong interest to know more about the [...]

Knowledge graph construction and application in geosciences: A review

Xiaogang Ma

Published: 2021-04-30
Subjects: Artificial Intelligence and Robotics, Computer Engineering, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Sciences

Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG [...]

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data

Martijn van den Ende, Itzhak Lior, Jean Paul Ampuero, et al.

Published: 2021-03-04
Subjects: Artificial Intelligence and Robotics, Geophysics and Seismology

Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterisation, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fibre-optic cables into seismic arrays with a spatial sampling [...]

Seasonal Arctic sea ice forecasting with probabilistic deep learning

Tom R. Andersson, J. Scott Hosking, Maria Pérez-Ortiz, et al.

Published: 2021-02-02
Subjects: Artificial Intelligence and Robotics, Earth Sciences, Statistics and Probability

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to [...]

An attempt at improving atmospheric corrections in InSAR using cycle-consistent adversarial networks

Guillaume Rongier, Cody M. Rude, Thomas Herring, et al.

Published: 2020-11-09
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geophysics and Seismology

Interferometry from satellite radar has thrived as a major asset to study surface deformations from earthquakes, volcanoes, aquifers, glaciers, landslides, etc. Most signals recorded in an interferogram have precise enough models to remove them almost completely. Yet, current models still fail to capture the full range and scales of variations of atmospheric perturbations. This work explores the [...]

Bridging the gap between geophysics and geology with Generative Adversarial Networks (GANs)

Suihong Song, Tapan Mukerji, Jiagen Hou

Published: 2020-08-16
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Geophysics and Seismology, Natural Resource Economics, Oil, Gas, and Energy, Other Earth Sciences, Physical Sciences and Mathematics, Sedimentology, Water Resource Management

Inverse mapping from geophysics to geology is a difficult problem due to the inherent uncertainty of geophysical data and the spatially heterogeneous patterns (structure) in geology. We describe GANSim, a type of generative adversarial networks (GANs) that discovers the mapping between remotely-sensed geophysical information and geology with realistic patterns, with a specially designed loss [...]

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