scholarly journals RHEA v1.0: Enabling fully coupled simulations with hydro-geomechanical heterogeneity

2021 ◽  
Vol 14 (10) ◽  
pp. 6257-6272
Author(s):  
José M. Bastías Espejo​​​​​​​ ◽  
Andy Wilkins ◽  
Gabriel C. Rau ◽  
Philipp Blum
Keyword(s):  
Open Source ◽  
Heterogeneous Systems ◽  
Two Dimensions ◽  
Distributed Data ◽  
Rock Fracturing ◽  
Geomechanical Properties ◽  
Spatially Distributed ◽  
Tightly Coupled ◽  
Fully Coupled ◽  
Current Modelling

Abstract. Realistic modelling of tightly coupled hydro-geomechanical processes is relevant for the assessment of many hydrological and geotechnical applications. Such processes occur in geologic formations and are influenced by natural heterogeneity. Current numerical libraries offer capabilities and physics couplings that have proven to be valuable in many geotechnical fields like gas storage, rock fracturing and Earth resources extraction. However, implementation and verification of the full heterogeneity of subsurface properties using high-resolution field data in coupled simulations has not been done before. We develop, verify and document RHEA (Real HEterogeneity App), an open-source, fully coupled, finite-element application capable of including element-resolution hydro-geomechanical properties in coupled simulations. To extend current modelling capabilities of the Multiphysics Object-Oriented Simulation Environment (MOOSE), we added new code that handles spatially distributed data of all hydro-geomechanical properties. We further propose a simple yet powerful workflow to facilitate the incorporation of such data to MOOSE. We then verify RHEA with analytical solutions in one and two dimensions and propose a benchmark semi-analytical problem to verify heterogeneous systems with sharp gradients. Finally, we demonstrate RHEA's capabilities with a comprehensive example including realistic properties. With this we demonstrate that RHEA is a verified open-source application able to include complex geology to perform scalable, fully coupled, hydro-geomechanical simulations. Our work is a valuable tool to assess challenging real-world hydro-geomechanical systems that may include different levels of complexity like heterogeneous geology and sharp gradients produced by contrasting subsurface properties.

RHEA: A verified simulator for hydro-geomechanical heterogeneity

2021 ◽  
Author(s):  
Jose Bastias ◽  
Gabriel Rau ◽  
Andy Wilkins ◽  
Philipp Blum
Keyword(s):  
Open Source ◽  
Spatial Scales ◽  
Heterogeneous Systems ◽  
Two Dimensions ◽  
Rock Fracturing ◽  
Geomechanical Properties ◽  
Tightly Coupled ◽  
Geological Formations ◽  
Fully Coupled ◽  
Complex Geology

<p>Realistic modelling of tightly coupled hydro-geomechanical processes is relevant for the assessment of many hydrological and geotechnical applications. Such processes occur in geological formations and are influenced by natural heterogeneities. Current numerical libraries offer capabilities and physics coupling, that have proven to be valuable in simulating various applications in geotechnical fields such as underground gas storage, rock fracturing, land subsidence and Earth resources extraction. However, implementation and verification of full heterogeneity of subsurface properties using high resolution field data in coupled simulations has not been done yet. Hence, we develop, verify and document RHEA (Real HEterogeneity App), an open-source fully coupled finite element application capable of including node-resolution hydro-geomechanical properties in coupled simulations. We propose a simple, yet powerful workflow to allow the integration of fully distributed hydro-geomechanical properties. We then verify the code with analytical solutions in one and two dimensions and propose a benchmark semi-analytical problem to verify heterogeneous systems with sharp gradients. Finally, we exemplify RHEA's capabilities with a comprehensive example integrating realistic properties. With this we demonstrate that RHEA is a verified open-source application able to integrate complex geology to perform scalable fully coupled hydro-geomechanical simulations. Our work is a valuable tool to assess real world hydro-geomechanical challenging systems that may include different levels of complexity like heterogeneous geology with several time and spatial scales and sharp gradients produced by contrasting subsurface properties.</p>

scholarly journals RHEA v1.0: Enabling fully coupled simulations with hydro-geomechanical heterogeneity

2021 ◽  
Author(s):  
José M. Bastías Espejo ◽  
Andy Wilkins ◽  
Gabriel Rau ◽  
Philipp Blum
Keyword(s):  
Open Source ◽  
Spatial Scales ◽  
Heterogeneous Systems ◽  
Two Dimensions ◽  
Rock Fracturing ◽  
Geomechanical Properties ◽  
Tightly Coupled ◽  
Fully Coupled ◽  
Complex Geology ◽  
Different Levels

Abstract. Realistic modelling of tightly coupled hydro-geomechanical processes is relevant for the assessment of many hydrological and geotechnical applications. Such processes occur in geologic formations and are influenced by natural heterogeneity. Current numerical libraries offer capabilities and physics couplings that have proven to be valuable in many geotechnical fields like gas storage, rock fracturing and Earth resources extraction. However, implementation and verification of full heterogeneity of subsurface properties using high resolution field data in coupled simulations has not been done before. We develop, verify and document RHEA (Real HEterogeneity App), an open-source, fully coupled, finite-element application capable of including element-resolution hydro-geomechanical properties in coupled simulations. We propose a simple, yet powerful workflow to allow the incorporation of fully distributed hydro-geomechanical properties. We then verify the code with analytical solutions in one and two dimensions, and propose a benchmark semi-analytical problem to verify heterogeneous systems with sharp gradients. Finally, we demonstrate RHEA's capabilities with a comprehensive example including realistic properties. With this we demonstrate that RHEA is a verified open-source application able to include complex geology to perform scalable, fully coupled, hydro-geomechanical simulations. Our work is a valuable tool to assess challenging real world hydro-geomechanical systems that may include different levels of complexity like heterogeneous geology with several time and spatial scales and sharp gradients produced by contrasting subsurface properties.

scholarly journals Treatment of deterministic perturbations and stochastic processes within a quality control scheme

2017 ◽  
Author(s):  
Birgit Eibl ◽  
Reinhold Steinacker
Keyword(s):  
Quality Control ◽  
Two Dimensions ◽  
Distributed Data ◽  
Data Quality Control ◽  
Enhanced Resolution ◽  
Control Scheme ◽  
Spatially Distributed ◽  
Resolution Model ◽  
Resolution Analysis ◽  
Quality Control Scheme

Abstract. Meteorological in situ observational data comes with a variety of errors and uncertainties. Any further usage of this data requires a sophisticated quality control to detect, quantify and possibly eliminate or at least to reduce errors and to increase the value of the information. It must be assumed, that each observational value Ψobs is contaminated by errors Ψerr so that the true state Ψtrue is not known. Different kinds of errors can be identified. Each of them has different characteristics and therefore has to be detected through appropriate methods. For years, various methods as a self consistency test, clustering and nearest neighbour techniques have been implemented in the complex quality control scheme of the Vienna Enhanced Resolution Analysis (VERA). Thereby former elaborations adressed the elimination and treatment of gross errors. In successioon the present investigation adresses the determination of stochastic and deterministic perturbations. In a first step we implemented the method to split up the observational value to smooth out the stochastic errors to the best and retain deterministic perturbations thereafter. Through controlled experiments on two dimensions the performance and limitations of the complex quality control scheme has been investigated. The treatment of errors and signals on different scales and the limit of the usability of this property is the main focus of the presented investigation. We highly recommend to use the method for data quality control within a high resolution model analysing spatially distributed data in highly complex terrain.

scholarly journals Water insecurity, illness and other factors of everyday life: A case study from Choma District, Southern Province, Zambia

Water SA ◽  
2018 ◽  
Vol 44 (4 October) ◽  
Author(s):  
Richard A Marcantonio
Keyword(s):  
Drinking Water ◽  
Everyday Life ◽  
Water Resource ◽  
Water Safety ◽  
Water Insecurity ◽  
Water Storage Capacity ◽  
Southern Province ◽  
Spatially Distributed ◽  
Tightly Coupled ◽  
Specific Outcome

Recent reports from the UN find that 2.6 billion people have gained access to improved drinking water sources since 1990, but 663 million people still live without. Other recent work demonstrates that 4 billion people annually face severe water scarcity as a result of seasonal fluctuations in water availability and quality. How is it that, despite the significant development in water resource availability documented by the UN, literally billions of people are regularly experiencing water insecurity? To begin to understand how a lack of access to reliable water resources affects everyday life, I focus on a specific outcome of water insecurity: waterborne illness. Given the difficulty in linking illness to a particular source, this research focuses on perceptions of water safety. I ask participants about illness they perceive coming from their drinking water, conducting face-to-face surveys (N = 224) spatially distributed around Choma town, Southern Province, Zambia. In particular, I investigate how these perceptions affect everyday life and what intersecting factors are likely to increase or decrease the probability of a person perceiving drinking water as the source of their illness. Our findings demonstrate that individual perceptions of waterborne illness are tightly coupled with perceptions of water needs being met or not, water flexibility (water storage capacity and water resource type and number available), total water use, food security and distance to various services. My work identifies and qualifies intersecting relationships that are critical to the design of any policy or other means of intervention intended to reduce experienced and perceived waterborne illness and other everyday needs of subsistence farmers facing the challenges presented by climate change and other forms of environmental change.

scholarly journals WPS ENABLED SDI: AN OPEN SOURCE APPROACH TO PROVIDE GEOPROCESSING IN WEB ENVIRONMENT

2019 ◽  
Vol IV-5/W2 ◽  
pp. 119-126
Author(s):  
A. K. Tripathi ◽  
S. Agrawal ◽  
R. D. Gupta
Keyword(s):  
Open Source ◽  
Spatial Data ◽  
Geospatial Data ◽  
Distributed Data ◽  
Data Infrastructure ◽  
Web Environment ◽  
Local Resources ◽  
Processing Service ◽  
Server Architecture ◽  
The Web

Abstract. Sharing and management of geospatial data among different communities and users is a challenge which is suitably addressed by Spatial Data Infrastructure (SDI). SDI helps people in the discovery, editing, processing and visualization of spatial data. The user can download the data from SDI and process it using the local resources. However, large volume and heterogeneity of data make this processing difficult at the client end. This problem can be resolved by orchestrating the Web Processing Service (WPS) with SDI. WPS is a service interface through which geoprocessing can be done over the internet. In this paper, a WPS enabled SDI framework with OGC compliant services is conceptualized and developed. It is based on the three tier client server architecture. OGC services are provided through GeoServer. WPS extension of GeoServer is used to perform geospatial data processing and analysis. The developed framework is utilized to create a public health SDI prototype using Open Source Software (OSS). The integration of WPS with SDI demonstrates how the various data analysis operations of WPS can be performed over the web on distributed data sources provided by SDI.

scholarly journals Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data

2019 ◽  
Vol 33 ◽  
pp. 3967-3974 ◽  
Author(s):  
Neal Jean ◽  
Sherrie Wang ◽  
Anshul Samar ◽  
George Azzari ◽  
David Lobell ◽  
...  
Keyword(s):  
Natural Language ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Distributed Data ◽  
Simple Arithmetic ◽  
Latent Space ◽  
Wide Range ◽  
Spatially Distributed ◽  
Vector Representations ◽  
Spatially Distributed Data

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language — words appearing in similar contexts tend to have similar meanings — to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations for both image and non-image datasets. Our learned representations significantly improve performance in downstream classification tasks and, similarly to word vectors, allow visual analogies to be obtained via simple arithmetic in the latent space.

scholarly journals A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 328 ◽  
Author(s):  
Mahmood Safaei ◽  
Shahla Asadi ◽  
Maha Driss ◽  
Wadii Boulila ◽  
Abdullah Alsaeedi ◽  
...  
Keyword(s):  
Literature Review ◽  
Outlier Detection ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Detection Techniques ◽  
Distributed Data Processing ◽  
Spatially Distributed

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

scholarly journals Continuous partial trends and low-frequency oscillations of time series

2005 ◽  
Vol 12 (4) ◽  
pp. 451-460 ◽  
Author(s):  
A. R. Tomé ◽  
P. M. A. Miranda
Keyword(s):  
Time Series ◽  
Low Frequency ◽  
Distributed Data ◽  
Spatially Distributed ◽  
Geophysical Time Series ◽  
Slow Evolution ◽  
Using Data ◽  
The Times ◽  
Data Subject

Abstract. This paper presents a recent methodology developed for the analysis of the slow evolution of geophysical time series. The method is based on least-squares fitting of continuous line segments to the data, subject to flexible conditions, and is able to objectively locate the times of significant change in the series tendencies. The time distribution of these breakpoints may be an important set of parameters for the analysis of the long term evolution of some geophysical data, simplifying the intercomparison between datasets and offering a new way for the analysis of time varying spatially distributed data. Several application examples, using data that is important in the context of global warming studies, are presented and briefly discussed.

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