Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural network via pinball loss. Here, we incorporate this technique into seismic inversion for purposes of CO2 monitoring. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also propose a novel data-augmentation method by sampling the uncertainty to further improve prediction accuracy. The developed methodology is tested on synthetic Kimberlina data, which are created by the Department of Energy and based on a CO2 capture and sequestration (CCS) project in California. The results prove that the proposed network can estimate the subsurface velocity rapidly and with sufficient resolution. Furthermore, the computed uncertainty quantifies the prediction accuracy. The method remains robust even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error.
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Department of Data Science, Norwegian University of Life Sciences（数据科学系，挪威生命科学大学）；Department for Mathematics and Cybernetics, SINTEF Digital（数学与 cybernetics 系，SINTEF 数字）
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
Iowa State University（爱荷华州立大学）；The Hong Kong Polytechnic University（香港理工大学）；Swiss Federal Institute of Technology Lausanne（洛桑联邦理工学院）；National Institute of Standards and Technology（美国国家标准与技术研究院）；GE Research（通用电气研究中心）；Seoul National University（首尔国立大学）；University of California, San Diego（加州大学圣地亚哥分校）；Vanderbilt University（范德堡大学）
Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Michigan State University（密歇根州立大学）
4D震波成像已被广泛用于CO $_2$封存项目，以监测未通过井采样的体积地下区域中的流体流动。理想情况下，实时监测和近期预测将为现场运营商提供深刻的见解，以了解地下储层的动态并评估任何潜在风险。然而，由于诸如高部署成本、采集设备的可用性、表面结构周围的禁区等障碍，在监测期间只能获得非常稀疏的震波成像数据。随着时间的推移，这导致了不可避免的和不断扩大的知识差距。运营商需要了解整个项目生命周期内的流体流动，而地震资料只能在有限的时间内获得。这对于理解储层动态是不够的。为了克服这些挑战，我们开发了基于时空神经网络的模型，可以有效和高效地产生高保真的内插或外推图像。具体来说，我们的模型建立在自动编码器上，并将长短期记忆（LSTM）结构与光流正则化的新损失函数相结合。我们验证我们的模型使用真实的4D叠后震波成像数据获得的Sleipner CO $_2$封存领域的性能。我们采用两种不同的策略来评估我们的模型。在数值上，我们使用经典的基于像素的指标将我们的模型与不同的基线方法进行比较。我们还进行了一项盲测，并从领域专家那里收集了总共20份回复，以评估我们的模型生成的数据质量。通过数值和专家评估，我们得出结论，我们的模型可以产生高质量的二维/三维震波成像数据在一个合理的成本，提供了实时监测，甚至不久的将来预测的CO $_2$存储水库的可能性。
MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data
Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Electrical Engineering Department, Polytechnique Montreal（蒙特利尔理工学院电气工程系）；GERAD；State Key Lab of General AI, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology（广东石油化工学院广东省石化装备故障诊断重点实验室）
Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（人工智能研究所、医学数据科学中心、维也纳医学大学，奥地利）；Comprehensive Center for AI in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心、维也纳医学大学，奥地利）；ELLIS Unit Linz, LIT AI Lab and Institute for Machine Learning, Johannes Kepler University Linz, Austria（林茨ELLIS单位、LIT人工智能实验室和机器学习研究所、林茨约瑟夫·冯·克拉夫特大学，奥地利）；Institute for Machine Learning, Johannes Kepler University Linz, Austria（机器学习研究所、林茨约瑟夫·冯·克拉夫特大学，奥地利）；Clinical Research Center for Medical AI, Johannes Kepler University Linz, Austria（医学人工智能临床研究中心、林茨约瑟夫·冯·克拉夫特大学，奥地利）
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Stellenbosch University（斯泰伦博斯大学）；The Nature Conservancy（大自然保护协会）；University of Cape Town（开普敦大学）；Norwegian Institute for Nature Research—NINA（挪威自然研究所—NINA）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；South African Environmental Observation Network（南非环境观测网络）
TCUQ: Single-Pass Uncertainty Quantification from Temporal Consistency with Streaming Conformal Calibration for TinyML
Multidisciplinary Faculty of Nador University Mohammed Premier Oujda（穆罕默德·布雷德·奥杰达大学多学科学院）；Laboratory LaSTI, ENSAK Sultan Moulay Slimane University Khouribga（库尔希格大学拉斯特伊实验室）
Tackling Incomplete Data in Air Quality Prediction: A Bayesian Deep Learning Framework for Uncertainty Quantification
School of Intelligent Systems Engineering, Sun Yat-sen University（智能系统工程学院，中山大学）；Guangdong Provincial Key Laboratory of Intelligent Transportation System（广东省智能交通系统重点实验室）；Guangdong Provincial Engineering Research Center for Traffic Environmental Monitoring and Control（广东省交通环境监测与控制工程研究中心）
