Preprints
Filtering by Subject: Artificial Intelligence and Robotics
Advancing flood warning procedures in ungauged basins with machine learning.
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
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
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
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
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
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)
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 [...]
Forecasting the localized bilateral effects of ocean acidification on the counter carbonate pump using recurrent neural networks
Published: 2020-07-08
Subjects: Artificial Intelligence and Robotics, Chemistry, Computer Sciences, Environmental Chemistry, Numerical Analysis and Scientific Computing, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
The counter carbonate pump(CCP) is responsible for carbon dioxide sequestration and cycling forms of carbon in the ocean. It is primarily driven by calcifying plankton, such as foraminifera, coccolithophores, and pteropods. These organisms are particularly vulnerable to ocean acidification, which can have disastrous effects on their skeletons and productivity, upsetting the marine carbon cycle in [...]
GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)
Published: 2020-07-05
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geology, Hydrology, Mathematics, Physical Sciences and Mathematics, Sedimentology, Statistical Models, Statistics and Probability
Conditional facies modeling combines geological spatial patterns with different types of observed data, to build earth models for predictions of subsurface resources. Recently, researchers have used generative adversarial networks (GANs) for conditional facies modeling, where an unconditional GAN is first trained to learn the geological patterns using the original GANs loss function, then [...]
Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence
Published: 2020-07-05
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamical Systems, Earth Sciences, Environmental Sciences, Fluid Dynamics, Geophysics and Seismology, Mathematics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics
A deep spatial transformer based encoder-decoder model has been developed to autoregressively predict the time evolution of the upper layers stream function of a two-layered quasi-geostrophic (QG) system without any information about the lower layers stream function. The spatio-temporal complexity of QG flow is comparable to the complexity of 500hPa Geopotential Height (Z500) of fully coupled [...]
Machine learning and fault rupture: a review
Published: 2020-07-02
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Mathematics, Physical Sciences and Mathematics, Theory and Algorithms
Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to [...]
Fully Automated Carbonate Petrography Using Deep Convolutional Neural Networks
Published: 2020-06-23
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geology, Paleobiology, Paleontology, Physical Sciences and Mathematics, Sedimentology
Carbonate rocks are important archives of past ocean conditions as well as hosts of economic resources such as hydrocarbons, water, and minerals. Geologists typically perform compositional analysis of grain, matrix, cement and pore types in order to interpret depositional environments, diagenetic modification, and reservoir quality of carbonate strata. Such information can be obtained primarily [...]
An Ethical Decision-Making Framework with Serious Gaming: Smart Water Case Study on Flooding
Published: 2020-06-23
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Civil Engineering, Computer Sciences, Databases and Information Systems, Engineering, Engineering Education, Environmental Engineering, Hydraulic Engineering, Physical Sciences and Mathematics, Theory and Algorithms
Sensors and control technologies are being deployed extensively in both urban water networks and rural river systems, leading to unprecedented ability to sense and control our water environment. Because these sensor networks and control systems allow for higher resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented [...]
Geological Facies Modeling Based on Progressive Growing of Generative Adversarial Networks (GANs)
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 [...]
Hidden Stories: Topic Modeling in Hydrology Literature
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 [...]