Preprints

Filtering by Subject: Artificial Intelligence and Robotics

Forecasting the localized bilateral effects of ocean acidification on the counter carbonate pump using recurrent neural networks

Eshan Ramesh

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)

Suihong Song, Tapan Mukerji, Jiagen Hou

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

Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, et al.

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

Christopher Ren, Claudia Hulbert, Paul A. Johnson, et al.

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

Ardiansyah Koeshidayatullah, Michele Morsilli, Daniel J. Lehrmann, et al.

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

Gregory James Ewing, Ibrahim Demir

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)

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 [...]

Hidden Stories: Topic Modeling in Hydrology Literature

Mashrekur Rahman, Jonathan Frame, Jimmy Lin, et al.

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 [...]

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 Michael Kelp, Daniel J. Jacob, J. Nathan Kutz, et al.

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 [...]

Realistic River Image Synthesis using Deep Generative Adversarial Networks

Akshat Gautam, Muhammed 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, [...]

Advanced ML and AI Approaches for Proxy-based Optimization of CO2-Enhanced Oil Recovery in Heterogeneous Clastic Reservoirs

Watheq J Al-Mudhafar, Dandina N Rao, Sanjay Srinivasan

Published: 2019-12-04
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Design of Experiments and Sample Surveys, Earth Sciences, Multivariate Analysis, Other Earth Sciences, Physical Sciences and Mathematics, Programming Languages and Compilers, Statistics and Probability

Constructing a simpler model to represent a complex reservoir simulation that will be employed to define the optimum future development plans have been achieved through the use of different simulation techniques that include EOS-compositional reservoir simulation, Proxy Modeling as well as Design of Experiments. Once reliable history matching was achieved, the key five operational decision [...]

Analog forecasting of extreme-causing weather patterns using deep learning

Ashesh Chattopadhyay, Pedram Hassanzadeh, Ebrahim Nabizadeh

Published: 2019-07-31
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computational Engineering, Computer Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based [...]

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM

Ashesh Chattopadhyay, Pedram Hassanzadeh, Devika Subramanian

Published: 2019-06-20
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamic Systems, Earth Sciences, Fluid Dynamics, Non-linear Dynamics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory [...]

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