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Modeling injection-induced fault slip using long short-term memory networks

Journal of Rock Mechanics and Geotechnical Engineering 2024年第11期16卷 4354-4368页

作者： Utkarsh Mital Mengsu Hu Yves Guglielmi James Brown Jonny Rutqvist Energy Geosciences Division Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Environmental Genomics&Systems Biology Lawrence Berkeley National LaboratoryBerkeleyCA94720USA

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the *** can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear *** activities are expected to rise in the future making it necessary to assess their short-and long-term ***,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is *** focus is on fault behavior near the injection *** capture the temporal dependencies in the data,long short-term memory(LSTM)networks are *** prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation *** numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid *** model also captured the decay in pressure and displacement during the injection shut-in ***,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

关键词： Machine learning long short-term memory networks Fault Fluid injection

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Prediction of discharge in a tidal river using the LSTM-based sequence-to-sequence models

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Acta Oceanologica Sinica 2024年第7期43卷 40-51页

作者： Zhigao Chen Yan Zong Zihao Wu Zhiyu Kuang Shengping Wang Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources East China University of TechnologyNanchang 330013China School of Surveying and Geoinformation Engineering East China University of TechnologyNanchang 330013China

The complexity of river-tide interaction poses a significant challenge in predicting discharge in tidal *** short-term memory(LSTM)networks excel in processing and predicting crucial events with extended intervals and time delays in time series ***,the sequence-to-sequence(Seq2Seq)model,known for handling temporal relationships,adapting to variable-length sequences,effectively capturing historical information,and accommodating various influencing factors,emerges as a robust and flexible tool in discharge *** this study,we introduce the application of LSTM-based Seq2Seq models for the first time in forecasting the discharge of a tidal reach of the Changjiang River(Yangtze River)*** study focuses on discharge forecasting using three key input characteristics:flow velocity,water level,and discharge,which means the structure of multiple input and single output is *** experiment used the discharge data of the whole year of 2020,of which the first 80%is used as the training set,and the last 20%is used as the test *** means that the data covers different tidal cycles,which helps to test the forecasting effect of different models in different tidal cycles and different *** experimental results indicate that the proposed models demonstrate advantages in long-term,mid-term,and short-term discharge *** Seq2Seq models improved by 6%-60%and 5%-20%of the relative standard deviation compared to the harmonic analysis models and improved back propagation neural network models in discharge prediction,*** addition,the relative accuracy of the Seq2Seq model is 1%to 3%higher than that of the LSTM *** assessment of the prediction errors shows that the Seq2Seq models are insensitive to the forecast lead time and they can capture characteristic values such as maximum flood tide flow and maximum ebb tide flow in the tidal cycle *** indicates the significance of the Seq2Seq models.

关键词： discharge prediction long short-term memory networks sequence-to-sequence(Seq2Seq)model tidal river back propagation neural network Changjiang River(Yangtze River)Estuary

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Recursive narrative alignment for movie narrating

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Science China(Information Sciences) 2020年第7期63卷 230-232页

作者： Zhongyi HAN Hongbo WU Benzheng WEI Yilong YIN Shuo LI School of Software Shandong University Computational Medicine Lab Shandong University of Traditional Chinese Medicine

Movie narrating can enable the capture of not only the subject matter but also the emotive essence of the subject. For example,"having a good time"or "feeling exhausted", which relates to the narrative of "holding a birthday party". Movie narrating converts the human-like expressivity in movie shots (i.e., sets of film frames) into a cohesive and

关键词： Movie Narrating Visual Captioning long short-term memory networks Deep Learning Narrative

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Detecting the backfill pipeline blockage and leakage through an LSTM-based deep learning model

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International Journal of Minerals,Metallurgy and Materials 2023年第8期30卷 1573-1583页

作者： Bolin Xiao Shengjun Miao Daohong Xia Huatao Huang Jingyu Zhang State Key Laboratory of High-Efficiency Mining and Metal Mine Safety Ministry of EducationUniversity of Science and Technology BeijingBeijing 100083China School of Advanced Engineering University of Science and Technology BeijingBeijing 100083China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China Yunnan Jinchengxin Mining Management Co. Ltd.Kunming 650217China Xinjiang Daming Mining Group Co. Ltd.Hami 839121China

Detecting a pipeline's abnormal status,which is typically a blockage and leakage accident,is important for the continuity and safety of mine *** pipeline system for gravity-transport high-density backfill(GHB)is *** designed,efficient,and accurate abnormal pipeline detection methods for GHB are *** work presents a long short-term memory-based deep learning(LSTM-DL)model for GHB pipeline blockage and leakage ***,an industrial pipeline monitoring system was introduced using pressure and flow ***,blockage and leakage field experiments were designed to solve the problem of negative sample *** pipeline's statistical characteristics with different working statuses were analyzed to show their ***,the architecture of the LSTM-DL model was elaborated on and ***,the LSTM-DL model was compared with state-of-the-art(SOTA)learning *** results show that the backfilling cycle comprises multiple working phases and is *** pressure and flow signals fluctuate stably in a normal cycle,their values are diverse in different *** causes a sudden change in interval signal features;leakage results in long variation duration and a wide fluctuation *** the SOTA models,the LSTM-DL model has the highest detection accuracy of98.31%for all states and the lowest misjudgment or false positive rate of 3.21%for blockage and leakage *** proposed model can accurately recognize various pipeline statuses of complex GHB systems.

关键词： mine backfill blockage and leakage pipeline detection long short-term memory networks deep learning

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Forecast electricity demand in commercial building with machine learning models to enable demand response programs

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Energy and AI 2022年第1期7卷 18-30页

作者： Fabiano Pallonetto Changhong Jin Eleni Mangina School of Computer Science University College DublinIreland School of Business Maynooth UniversityIreland

Electricity load forecasting is an important part of power system *** forecasting electricity load have great impact on a number of departments in power *** to electricity load simulation(white-box model),electricity load forecasting(black-box model)does not require expertise in building *** development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load *** developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear *** on the real load dataset,this paper evaluates and compares the two mainstream short-term load forecasting *** the experiment,this paper first enumerates the common methods of short-term load forecasting and explains the principles of long short-term memory networks(LSTMs)and Support Vector Machines(SVM)used in this ***,based on the characteristics of the electricity load dataset,data pre-processing and feature selection takes *** paper describes the results of a controlled experiment to study the importance of feature *** LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load *** predictive accuracy of these models are calculated based on the error between the actual and predicted loads,and the runtime of the model is *** results show that the LSTMs model have a higher prediction accuracy when the load data is ***,the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

关键词： Deep neural network Model assessment short-term load forecasting Feature selection Support Vector Machines Artificial Neural networks long short-term memory networks Demand response

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Machine learning based prediction of flyrock distance in rock blasting:A safe and sustainable mining approach

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Green and Smart Mining Engineering 2024年第3期1卷 346-361页

作者： Blessing Olamide Taiwo Yewuhalashet Fissha Shahab Hosseini Mohammad Khishe Esma Kahraman Babatunde Adebayo Mohammed Sazid Patrick Adeniyi Adesida Oluwaseun Victor Famobuwa Joshua Oluwaseyi Faluyi Adams Abiodun Akinlabi Department of Mining Engineering Federal University of Technology AkureAkure 340001Nigeria HNF Global Resources Limited Akoko EdoIgarra 312101Nigeria Department of Geosciences Geotechnology and Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita 010-8502Japan Department of Mining Engineering Aksum UniversityAksum 7080TigrayEthiopia Faculty of Engineering Tarbiat Modares UniversityTehran 14115-111Iran Department of Electrical Engineering Imam Khomeini Naval Science University of NowshahrNowshahr 46517Iran Applied Science Research Center Applied Science Private UniversityAmman 11937Jordan Department of Mining Engineering Cukurova UniversityAdana 01250Turkey Department of Mining Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mining West Virginia UniversityMorgantown 26506USA

Flyrock is a significant environmental and safety concern in mining and *** arises from various geological and blast design factors,posing risks to workers,machinery,and nearby *** study examined how these factors affect the rate and distance of flyrock projections caused by *** address this issue,advanced machine learning(ML)models were used to predict flyrock distances in the Akoko Edo dolomite *** models examined included bidirectional recurrent neural networks(BRNNs),support vector regression(SVR)with different kernels(SVR-S,SVR-RBF,SVR-L,SVR-P),long short-term memory(LSTM)networks,and random forest(RF)algorithms.A case study was conducted using 258 blasting data samples to develop these *** factors influencing flyrock were identified:blast hole burden distance,maximum instantaneous charge,and rock brittleness *** these factors,a flyrock possibility assessment chart was created to enhance the safety of small-scale mining *** model’s prediction accuracy was evaluated using correlation coefficients and four performance *** LSTM model stood out,achieving the highest coefficient of correlation(R2=0.99)for both training and testing *** indicates that the LSTM model accurately predicts blast-induced flyrock *** study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants(SVR-S,SVR-L,and SVR-P).In conclusion,the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

关键词： Blasting Flyrock Safety chart Mine production reliability long short-term memory networks

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Optimizing the LSTM Deep Learning Model for Arctic Sea Ice Melting Prediction

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Atmospheric and Climate Sciences 2024年第4期14卷 429-449页

作者： Victoria Pegkou Christofi Xiaodi Wang Department of Mathematics Western Connecticut State University Danbury USA

The National Oceanic and Atmospheric Administration reports a 95% decline in the oldest Arctic ice over the last 33 years [1], while the National Aeronautics and Space Administration states that summer Arctic Sea Ice Extent (SIE) is shrinking by 12.2% per decade since 1979 due to warmer temperatures [2]. Given the rapidly changing Arctic conditions, accurate prediction models are crucial. Deep learning models developed for Arctic forecasts primarily focus on exploring convolutional neural networks (CNN) and convolutional long short-term memory (LSTM) networks, while the exploration of the power of LSTM networks is limited. In this research, we focus on enhancing the performance of an LSTM network for predicting monthly Arctic SIE. We leverage five climate and atmospheric variables, validated for their correlation with SIE in prior studies [3]. We utilize the Spearman’s rank correlation and ExtraTrees regressor to enhance our understanding of the importance of the five variables in predicting SIE. We further enhance our predictor variables with seasonal information, lagged time steps, and a linear regression simulated SIE that accounts for the influence of past SIE on current SIE. Statistical methods guide our selection of data scalers and best evaluation metrics for our model. By experimenting with hyperparameter optimization and advanced deep learning training techniques, such as batch sizes, number of neurons, early stopping, and model checkpoint, our model achieved a Mean Absolute Error (MAE) of 0.191 and R2 of 0.996, underscoring its ability to account for nearly all the variance in our data and holds great promise for the prediction of SIE.

关键词： Arctic Sea Ice Extent Deep Learning long short-term memory networks Climate Change

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Photovoltaic power forecasting based on weather classification using LSTM

Photovoltaic power forecasting based on weather classificati...

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第32届中国过程控制会议(CPCC2021)

作者： Lei Guo Xiangjie Liu Xiaobing Kong School of Control and Computer Engineering North China Electric Power University

In order to reduce possible negative impacts of photovoltaic(PV) power penetration into modern power grids,an accurate forecasting of PV power output is ***,photovoltaic power forecasting(PVPF) is a rather difficult challenge because of its highly varying and complex *** this article,a weather-based hybrid PVPF method is proposed,combining weather classification,long short-term memory network,and the fuzzy inference approach to make 5-minutes-ahead forecasting of PV power *** correlation coefficient is adopted as a feature selection *** effectiveness of the proposed model is evaluated by comparing with the multilayer perceptron neural network and traditional long short-term memory *** results show that the proposed model shows better forecasting accuracy.

关键词： Photovoltaic power forecasting Weather classification long short-term memory networks Fuzzy inference approach Pearson correlation coefficient

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