scholarly journals Deep learning for earthquake detection and location

2021 ◽  
Author(s):  
Saumik Dana
Keyword(s):  
Deep Learning ◽  
Seismic Wave Propagation ◽  
The United States ◽  
Economic Losses ◽  
Climate Mitigation ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Underground Structures ◽  
Earthquake Detection ◽  
Earthquake Losses

Understanding the causality between the events leading to fault slip and the earthquake recording is important for seismic design and monitoring of underground structures, bridges and reinforced concrete buildings as well as climate mitigation projects like carbon sequestration and energy technologies like enhanced geothermal systems or oilfield wastewater disposal. The Federal Emergency Management Agency (FEMA) reported in 2017, that earthquake losses in the United States add up to about \$6.1 billion dollars annually. This number only addresses direct economic losses to buildings, and does not cover damage and losses to critical facilities, transportation and utility lifelines or indirect economic losses. A holistic framework to study earthquakes would incorporate seismic wave propagation and pressure perturbations, and have a dialogue with the deep learning framework for earthquake detection and location. In this document, we delve into the deep learning module.

scholarly journals Comparing poroelastostatics and poroelastodynamics: Numerics, solvers and algorithms

2021 ◽  
Author(s):  
Saumik Dana
Keyword(s):  
Seismic Design ◽  
Fault Slip ◽  
Climate Mitigation ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Underground Structures ◽  
Wastewater Disposal ◽  
Energy Technologies ◽  
Time Marching ◽  
Mitigation Projects

Understanding the causality between the events leading upto and post fault slip and the earthquake recording is important for seismic design and monitoring of underground structures, bridges and reinforced concrete buildings as well as climate mitigation projects like carbon sequestration and energy technologies like enhanced geothermal systems or oilfield wastewater disposal. While the events leading upto fault slip are typically governed by poroelastostatics, the events post fault slip can easily transition into poroelastodynamics territory due to runaway fault slip velocities. There are marked differences in the numerics of poroelastostatics and poroelastodynamics, and a simple switch from one algorithm to another based on fault slip velocities is not trivial. In fact, an understanding of expected fault slip velocities is critical apriori, as an algorithm which can seamlessly transition from time marching in poroelastostatics realm to poroelastodynamics realm and vice-versa is extremely difficult to achieve. We present the numerics of both physics and point out the differences between the two in this work.

scholarly journals Deep learning for extracting micro-fracture: Pixel-level detection by convolutional neural network

2020 ◽  
Vol 205 ◽  
pp. 03007
Author(s):  
Yejin Kim ◽  
Seong Jun Ha ◽  
Tae sup Yun
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Frequency Noise ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Hydraulic Stimulation ◽  
Roughness Analysis

Hydraulic stimulation has been a key technique in enhanced geothermal systems (EGS) and the recovery of unconventional hydrocarbon resources to artificially generate fractures in a rock formation. Previous experimental studies present that the pattern and aperture of generated fractures vary as the fracking pressure propagation. The recent development of three-dimensional X-ray computed tomography allows visualizing the fractures for further analysing the morphological features of fractures. However, the generated fracture consists of a few pixels (e.g., 1-3 pixels) so that the accurate and quantitative extract of micro-fracture is highly challenging. Also, the high-frequency noise around the fracture and the weak contrast across the fracture makes the application of conventional segmentation methods limited. In this study, we adopted an encoder-decoder network with a convolutional neural network (CNN) based on deep learning method for the fast and precise detection of micro-fractures. The conventional image processing methods fail to extract the continuous fractures and overestimate the fracture thickness and aperture values while the CNN-based approach successfully detects the barely seen fractures. The reconstruction of the 3D fracture surface and quantitative roughness analysis of fracture surfaces extracted by different methods enables comparison of sensitivity (or robustness) to noise between each method.

scholarly journals Geothermal Energy R&D: An Overview of the U.S. Department of Energy’s Geothermal Technologies Office

2021 ◽  
Vol 143 (10) ◽  
Author(s):  
Susan G. Hamm ◽  
Arlene Anderson ◽  
Douglas Blankenship ◽  
Lauren W. Boyd ◽  
Elizabeth A. Brown ◽  
...  
Keyword(s):  
United States ◽  
Geothermal Energy ◽  
The United States ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Geothermal Resources ◽  
Future Directions ◽  
Electricity Grid ◽  
Strategic Goals ◽  
The U.S

Abstract Geothermal energy can provide answers to many of America’s essential energy questions. The United States has tremendous geothermal resources, as illustrated by the results of the DOE GeoVision analysis, but technical and non-technical barriers have historically stood in the way of widespread deployment of geothermal energy. The U.S. Department of Energy’s Geothermal Technologies Office within the Office of Energy Efficiency and Renewable Energy has invested more than $470 million in research and development (R&D) since 2015 to meet its three strategic goals: (1) unlock the potential of enhanced geothermal systems, (2) advance technologies to increase geothermal energy on the U.S. electricity grid, and (3) support R&D to expand geothermal energy opportunities throughout the United States. This paper describes many of those R&D initiatives and outlines future directions in geothermal research.

scholarly journals Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1161
Author(s):  
Kuo-Hao Fanchiang ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Monitoring System ◽  
Absolute Difference ◽  
Generative Adversarial Networks ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Full Time ◽  
Noise Interference ◽  
In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

scholarly journals The Economic Effects of COVID-19 on the Producers of Ethanol, Corn, Gasoline, and Oil

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Andrew Schmitz ◽  
Charles B. Moss ◽  
Troy G. Schmitz
Keyword(s):  
United States ◽  
Cost Benefit ◽  
Economic Cost ◽  
The United States ◽  
Welfare Economic ◽  
Economic Effects ◽  
Economic Losses ◽  
Corn Ethanol ◽  
Empirical Estimation ◽  
Total Oil

AbstractThe COVID-19 crisis created large economic losses for corn, ethanol, gasoline, and oil producers and refineries both in the United States and worldwide. We extend the theory used by Schmitz, A., C. B. Moss, and T. G. Schmitz. 2007. “Ethanol: No Free Lunch.” Journal of Agricultural & Food Industrial Organization 5 (2): 1–28 as a basis for empirical estimation of the effect of COVID-19. We estimate, within a welfare economic cost-benefit framework that, at a minimum, the producer cost in the United States for these four sectors totals $176.8 billion for 2020. For U.S. oil producers alone, the cost was $151 billion. When world oil is added, the costs are much higher, at $1055.8 billion. The total oil producer cost is $1.03 trillion, which is roughly 40 times the effect on U.S. corn, ethanol, and gasoline producers, and refineries. If the assumed unemployment effects from COVID-19 are taken into account, the total effect, including both producers and unemployed workers, is $212.2 billion, bringing the world total to $1266.9 billion.

scholarly journals Environmentally friendly, rheoreversible, hydraulic-fracturing fluids for enhanced geothermal systems

Geothermics ◽  
2015 ◽  
Vol 58 ◽  
pp. 22-31 ◽  
Author(s):  
Hongbo Shao ◽  
Senthil Kabilan ◽  
Sean Stephens ◽  
Niraj Suresh ◽  
Anthon N. Beck ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Environmentally Friendly ◽  
Enhanced Geothermal Systems ◽  
Geothermal Systems ◽  
Fracturing Fluids

scholarly journals Extension Plant Pathology: Strengthening Resources to Continue Serving the Public Interest

2012 ◽  
Vol 102 (7) ◽  
pp. 652-655 ◽  
Author(s):  
K. L. Everts ◽  
L. Osborne ◽  
A. J. Gevens ◽  
S. J. Vasquez ◽  
B. K. Gugino ◽  
...  
Keyword(s):  
Plant Pathology ◽  
Plant Disease ◽  
Crop Yields ◽  
Economic Value ◽  
The United States ◽  
Emerging Diseases ◽  
Plant Diseases ◽  
Economic Losses ◽  
Global Competitiveness ◽  
Horticultural Crops

Extension plant pathologists deliver science-based information that protects the economic value of agricultural and horticultural crops in the United States by educating growers and the general public about plant diseases. Extension plant pathologists diagnose plant diseases and disorders, provide advice, and conduct applied research on local and regional plant disease problems. During the last century, extension plant pathology programs have adjusted to demographic shifts in the U.S. population and to changes in program funding. Extension programs are now more collaborative and more specialized in response to a highly educated clientele. Changes in federal and state budgets and policies have also reduced funding and shifted the source of funding of extension plant pathologists from formula funds towards specialized competitive grants. These competitive grants often favor national over local and regional plant disease issues and typically require a long lead time to secure funding. These changes coupled with a reduction in personnel pose a threat to extension plant pathology programs. Increasing demand for high-quality, unbiased information and the continued reduction in local, state, and federal funds is unsustainable and, if not abated, will lead to a delay in response to emerging diseases, reduce crop yields, increase economic losses, and place U.S. agriculture at a global competitive disadvantage. In this letter, we outline four recommendations to strengthen the role and resources of extension plant pathologists as they guide our nation's food, feed, fuel, fiber, and ornamental producers into an era of increasing technological complexity and global competitiveness.

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