scholarly journals Relating Dike Geometry and Injection Rate in Analogue Flux-Driven Experiments

2021 ◽  
Vol 9 ◽  
Author(s):  
Federico Galetto ◽  
Alessandro Bonaccorso ◽  
Valerio Acocella
Keyword(s):  
Real Time ◽  
Hazard Assessment ◽  
Volcanic Hazard ◽  
Injection Rate ◽  
Monitoring Data ◽  
Geometric Features ◽  
Injection Time ◽  
Vertically Propagating

Dikes feed most eruptions, so understanding their mechanism of propagation is fundamental for volcanic hazard assessment. The variation in geometry of a propagating dike as a function of the injection rate remains poorly studied. Here we use experiments injecting water into gelatin to investigate the variation of the thickness, width and length of a flux-driven dike connected to its source as a function of the injection time and intruded volume. Results show that the thickness of vertically propagating dikes is proportional to the injection rate and remains constant as long as the latter is constant. Neither buoyancy nor injected volume influence the thickness. The along-strike width of the dike is, however, proportional to the injected volume. These results, consistent with the inferred behavior of several dikes observed during emplacement, open new opportunities to better understand how dikes propagate and also to forecast how emplacing dikes may propagate once their geometric features are detected in real-time through monitoring data.

scholarly journals A retrospective study of the pre-eruptive unrest on El Hierro (Canary Islands): implications of seismicity and deformation in the short-term volcanic hazard assessment

2018 ◽  
Vol 18 (6) ◽  
pp. 1759-1770 ◽  
Author(s):  
Stefania Bartolini ◽  
Carmen López ◽  
Laura Becerril ◽  
Rosa Sobradelo ◽  
Joan Martí
Keyword(s):  
Canary Islands ◽  
Hazard Assessment ◽  
Surface Deformation ◽  
Volcanic Hazard ◽  
Volcanic Eruptions ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Correct Identification ◽  
Volcanic System ◽  
El Hierro

Abstract. The correct identification and interpretation of unrest indicators is useful for forecasting volcanic eruptions, delivering early warnings, and understanding the changes occurring in a volcanic system prior to an eruption. Such indicators play an important role in upgrading previous long-term volcanic hazard assessments and help explain the complexities of the preceding period of eruptive activity. In this work, we present a retrospective analysis of the 2011 unrest episode on the island of El Hierro, Canary Islands, that preceded a submarine eruption. We use seismic and surface deformation monitoring data to compute the susceptibility analysis (QVAST tool) and to study the evolution over time of the unrest (ST-HASSET tool). Additionally, we show the advantages to be gained by using continuous monitoring data and hazard assessment e-tools to upgrade spatiotemporal analyses and thus visualize more simply the development of the volcanic activity.

scholarly journals A retrospective study of the pre-eruptive unrest on El Hierro (Canary Islands): implications of seismicity and deformation in the short-term volcanic hazard assessment

2017 ◽  
Author(s):  
Stefania Bartolini ◽  
Carmen López ◽  
Laura Becerril ◽  
Rosa Sobradelo ◽  
Joan Martí
Keyword(s):  
Hazard Assessment ◽  
Surface Deformation ◽  
Volcanic Hazard ◽  
Volcanic Eruptions ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Correct Identification ◽  
Volcanic System ◽  
El Hierro ◽  
Spatio Temporal

Abstract. The correct identification and interpretation of unrest indicators are useful for forecasting volcanic eruptions, delivering early warnings, and understanding the changes occurring in a volcanic system prior to an eruption. Such indicators play an important role in upgrading previous long-term volcanic hazard assessments and help grasp the complexities of the preceding period of eruptive activity. In this work, we present a retrospective analysis of the 2011 unrest episode on the island of El Hierro that preceded a submarine eruption. We use seismic and surface deformation monitoring data to compute the susceptibility analysis (QVAST tool) and to study the evolution over time of the unrest (ST-HASSET tool). Additionally, we show the advantages to be gained by using continuous monitoring data and hazard assessment e-tools to upgrade spatio-temporal analyses and thus visualize more simply the development of the volcanic activity.

860-P: Benefits of Real-Time Sharing and Following of Continuous Glucose Monitoring Data from Youth

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 860-P
Author(s):  
ANDREW PARKER ◽  
MARK DERDZINSKI ◽  
SARAH PUHR ◽  
JOHN WELSH ◽  
TOMAS C. WALKER ◽  
...  
Keyword(s):  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Time Sharing

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

Investigation on Polymer Adsorption and Retention in a Polymer Flooded Reservoir

2013 ◽  
Vol 734-737 ◽  
pp. 1200-1203
Author(s):  
Shu Qiang Liu ◽  
Ji Cheng Zhang ◽  
Jin Cheng Xu
Keyword(s):  
Polymer Solution ◽  
Influencing Factors ◽  
Polymer Adsorption ◽  
Solution Concentration ◽  
Injection Rate ◽  
The Other ◽  
Injection Time ◽  
Factors Affecting ◽  
Dynamic Factors ◽  
Polymer Retention

During polymer flooding, certain amount of polymer would be lost. Polymer retention causes sweep volume expanding on one side, it also causes polymer loss on the other. Therefore, it is a very important topic to study the influencing factors of polymer retention. There are many factors affecting polymer retention process. This paper mainly studied the influence from dynamic factors such as polymer solution concentration, injection rate, injection time, injected pv number. This paper investigated the influence of these factors on polymer retention process, and optimized these factors to minimize polymer loss in reservoir.

Numerical simulation and optimization of CO2 enhanced gas recovery in homogeneous and vertical heterogeneous reservoir models

2021 ◽  
pp. 1-36
Author(s):  
Shuyang Liu ◽  
Ramesh Agarwal ◽  
Baojiang Sun
Keyword(s):  
Natural Gas ◽  
Injection Rate ◽  
Injection Time ◽  
Upward Migration ◽  
Enhanced Gas Recovery ◽  
Simulation And Optimization ◽  
Heterogeneous Reservoirs ◽  
Gas Recovery ◽  
Heterogeneous Reservoir ◽  
Optimal Injection

Abstract CO2 enhanced gas recovery (CO2-EGR) is a promising, environment-friendly technology with simultaneously sequestering CO2. The goals of this paper are to conduct simulations of CO2-EGR in both homogeneous and heterogeneous reservoirs to evaluate effects of gravity and reservoir heterogeneity, and to determine optimal CO2 injection time and injection rate for achieving better natural gas recovery by employing a genetic algorithm integrated with TOUGH2. The results show that gravity segregation retards upward migration of CO2 and promotes horizontal displacement efficiency, and the layers with low permeability in heterogeneous reservoir hinder the upward migration of CO2. The optimal injection time is determined as the depleted stage, and the corresponding injection rate is optimized. The optimal recovery factors are 62.83 % and 64.75 % in the homogeneous and heterogeneous reservoirs (804.76 m × 804.76 m × 45.72 m), enhancing production by 22.32 × 103 and 23.00 × 103 t of natural gas and storing 75.60 × 103 and 72.40 × 103 t CO2 with storage efficiencies of 70.55 % and 67.56 %, respectively. The cost/benefit analysis show that economic income of about 8.67 and 8.95 million USD can be obtained by CO2-EGR with optimized injection parameters respectively. This work could assist in determining optimal injection strategy and economic benefits for industrial scale gas reservoirs.

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

2020 ◽  
Vol 11 (4) ◽  
pp. 57-71
Author(s):  
Qiuxia Liu
Keyword(s):  
Data Fusion ◽  
Environmental Monitoring ◽  
Real Time ◽  
Monitoring System ◽  
Intelligent System ◽  
Evidence Synthesis ◽  
Environmental Parameters ◽  
Monitoring Data ◽  
Sensor Data ◽  
Environmental Monitoring System

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

scholarly journals LAV@HAZARD: a web-GIS interface for volcanic hazard assessment

2011 ◽  
Vol 54 (5) ◽  
Author(s):  
Annamaria Vicari ◽  
Giuseppe Bilotta ◽  
Sergio Bonfiglio ◽  
Annalisa Cappello ◽  
Gaetana Ganci ◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Volcanic Hazard ◽  
Web Gis ◽  
Gis Interface
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