scholarly journals RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6571
Author(s):  
Timothy I. Anderson ◽  
Kelly M. Guan ◽  
Bolivia Vega ◽  
Saman A. Aryana ◽  
Anthony R. Kovscek
Keyword(s):  
Source Rock ◽  
Binary Data ◽  
Broad Class ◽  
Imaging Techniques ◽  
Image Data ◽  
Generative Models ◽  
Training Data ◽  
Grayscale Image ◽  
Flow Models ◽  
Rock Characterization

Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

INTEGRATED RESERVOIR AND SOURCE ROCK CHARACTERIZATION: REFINED DOWNHOLE ANALYSES THROUGH ADVANCED SURFACE LOGGING TECHNOLOGY

2019 ◽  
Author(s):  
Alessandro Pozzi ◽  
Filippo Casal ◽  
Roselin Ebiakobo-Ngoma-Itoba ◽  
Antonio Bonetti ◽  
Audrey Aymar Nkie-Ndion ◽  
...  
Keyword(s):  
Source Rock ◽  
Rock Characterization

scholarly journals U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2611
Author(s):  
Andrew Shepley ◽  
Greg Falzon ◽  
Christopher Lawson ◽  
Paul Meek ◽  
Paul Kwan
Keyword(s):  
Deep Learning ◽  
Intellectual Property ◽  
Object Detection ◽  
Image Data ◽  
Learning Technologies ◽  
Training Data ◽  
Learning Models ◽  
Ecological Data ◽  
Single Class ◽  
Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

scholarly journals Multi-Energy X-Ray Computed Tomography for Source Rock Characterization

2021 ◽  
Vol 27 (S1) ◽  
pp. 1030-1031
Author(s):  
Airdias Korolkovas ◽  
Seongjin Yoon ◽  
Alexander Katsevich ◽  
Shannon Eichmann ◽  
Qiushi Sun ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Source Rock ◽  
X Ray ◽  
X Ray Computed ◽  
Rock Characterization

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice

2020 ◽  
Vol 17 (12) ◽  
pp. 4424
Author(s):  
Kuofeng Hung ◽  
Andy Wai Kan Yeung ◽  
Ray Tanaka ◽  
Michael M. Bornstein
Keyword(s):  
Treatment Planning ◽  
3D Imaging ◽  
Imaging Techniques ◽  
Current Trend ◽  
Three Dimensional ◽  
Image Data ◽  
Anatomical Landmarks ◽  
Automated Diagnosis ◽  
Dental Practitioners ◽  
Facial Scanning

The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.

scholarly journals Source rock characterization of mesozoic to cenozoic organic matter rich marls and shales of the Eratosthenes Seamount, Eastern Mediterranean Sea

2018 ◽  
Vol 73 ◽  
pp. 49 ◽  
Author(s):  
Sebastian Grohmann ◽  
Susanne W. Fietz ◽  
Ralf Littke ◽  
Samer Bou Daher ◽  
Maria Fernanda Romero-Sarmiento ◽  
...  
Keyword(s):  
Organic Matter ◽  
Mediterranean Sea ◽  
Source Rock ◽  
Eastern Mediterranean ◽  
Source Rocks ◽  
Eastern Mediterranean Sea ◽  
Potential Source Rock ◽  
Levant Basin ◽  
Rock Characterization ◽  
Rock Eval

Several significant hydrocarbon accumulations were discovered over the past decade in the Levant Basin, Eastern Mediterranean Sea. Onshore studies have investigated potential source rock intervals to the east and south of the Levant Basin, whereas its offshore western margin is still relatively underexplored. Only a few cores were recovered from four boreholes offshore southern Cyprus by the Ocean Drilling Program (ODP) during the drilling campaign Leg 160 in 1995. These wells transect the Eratosthenes Seamount, a drowned bathymetric high, and recovered a thick sequence of both pre- and post-Messinian sedimentary rocks, containing mainly marine marls and shales. In this study, 122 core samples of Late Cretaceous to Messinian age were analyzed in order to identify organic-matter-rich intervals and to determine their depositional environment as well as their source rock potential and thermal maturity. Both Total Organic and Inorganic Carbon (TOC, TIC) analyses as well as Rock-Eval pyrolysis were firstly performed for the complete set of samples whereas Total Sulfur (TS) analysis was only carried out on samples containing significant amount of organic matter (>0.3 wt.% TOC). Based on the Rock-Eval results, eight samples were selected for organic petrographic investigations and twelve samples for analysis of major aliphatic hydrocarbon compounds. The organic content is highly variable in the analyzed samples (0–9.3 wt.%). TS/TOC as well as several biomarker ratios (e.g. Pr/Ph < 2) indicate a deposition under dysoxic conditions for the organic matter-rich sections, which were probably reached during sporadically active upwelling periods. Results prove potential oil prone Type II kerogen source rock intervals of fair to very good quality being present in Turonian to Coniacian (average: TOC = 0.93 wt.%, HI = 319 mg HC/g TOC) and in Bartonian to Priabonian (average: TOC = 4.8 wt.%, HI = 469 mg HC/g TOC) intervals. A precise determination of the actual source rock thickness is prevented by low core recovery rates for the respective intervals. All analyzed samples are immature to early mature. However, the presence of deeper buried, thermally mature source rocks and hydrocarbon migration is indicated by the observation of solid bitumen impregnation in one Upper Cretaceous and in one Lower Eocene sample.

scholarly journals Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7834
Author(s):  
Christopher Hecht ◽  
Jan Figgener ◽  
Dirk Uwe Sauer
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Training Data ◽  
Gradient Boosting ◽  
Traffic Density ◽  
Learning Models ◽  
Charging Infrastructure ◽  
Ensemble Models ◽  
Charging Station ◽  
Machine Learning Models

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

scholarly journals Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models

2019 ◽  
Vol 2019 (4) ◽  
pp. 232-249 ◽  
Author(s):  
Benjamin Hilprecht ◽  
Martin Härterich ◽  
Daniel Bernau
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Generative Models ◽  
Generative Model ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Success Rates ◽  
Model Quality ◽  
Type Formalization

Abstract We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

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