Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning

Zhu, Linqi; Zhang, Zhansong; Nie, Xiaodong; Zhou, Xueqing; Liu, Weinan; Wang, Xiu

doi:10.1016/j.asoc.2019.105596

Cited by 65 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, deep learning algorithm attracts researchers' attention to solve the regression problem based on a certain amount of labeled training data, which has been successfully applied in resource exploration area [23] and equipment health management area [24] etc. For the area of mechanical property prediction of steels, Lakshmi et al [25] has applied a multiple layer neural network to predict yield strength (YS), tensile strength (TS), strain hardening coefficient, elongation (EL) and strength coefficient of austenite stainless steel, achieving good results.…”

Section: Introductionmentioning

confidence: 99%

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Yang

Cao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In the present work, to solve the problem of the lacking enough labeled training data for deep learning, a safe semi-supervised regression supporting Bayesian optimization deep neural network (SAFER-BODNN) model was proposed to establish mechanical property prediction model of hot-rolled strips. The Pearson correlation coefficient was applied to reduce the data dimension. The safe semi-supervised regression was implemented to add the pseudo labels to the unlabeled data for training dataset expansion. The deep neural network was trained with Bayesian optimization to determine the optimal hyper-parameters of the network. The results show that the SAFER-BODNN model achieves good accuracy for mechanical property prediction of hot-rolled strips with R of 0.9610 for yield strength, 0.9682 for tensile strength, and 0.8619 for elongation, respectively. Compared with the deep neural network trained on the labeled dataset, the SAFER-BODNN model obtains stable smaller predicted errors. Among all the variables, C content and Mn content have large influence on the yield strength and tensile strength, coiling temperature has the largest influence on the elongation. The investigation makes full use of unlabeled data to elevate the accuracy of the deep neural network, and also provides a way for deep learning modeling when the data are insufficient.

show abstract

Section: Introductionmentioning

confidence: 99%

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Yang

Cao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The most recent and current studies focus on estimating the TOC by improving the accuracy of ∆logR model [11][12][13] or by applying machine learning techniques [14][15][16].…”

Section: His Correlation In Equationmentioning

confidence: 99%

Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

et al. 2019

View full text Add to dashboard Cite

Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.

show abstract

“…This study focuses on the learning algorithm of DBM. DBM is a probabilistic deep learning model based on a pairwise Boltzmann machine [3,4]; it has been applied to various tasks, for example, 3D image recognition [5], speech recognition [6], emotion recognition [7], feature extraction [8], generative model [9], multimodal learning [10], and prediction of total organic carbon content [11]. A heuristic optimization method for the hyperparameters of DBM such as the sizes of hidden layers and the learning rate was proposed [12].…”

Section: Introductionmentioning

confidence: 99%

Effective fine-tuning training of deep Boltzmann machine based on spatial Monte Carlo integration

Katsumata

Yasuda

2021

NOLTA

View full text Add to dashboard Cite

A deep Boltzmann machine (DBM) is a probabilistic deep learning model; DBM learning consists pretraining and fine-tuning stages. This study focuses on the fine-tuning stage, and it develops a new and effective fine-tuning method based on spatial Monte Carlo integration (SMCI), which is an extension of the standard Monte Carlo integration (MCI). It has been proved that SMCI is statistically more accurate than the standard MCI. Finetuning methods based on first-order and semi-second-order SMCI methods are formulated.The numerical experiments demonstrate that the proposed fine-tuning methods are superior to the conventional method in terms of both training and generalization errors.

show abstract

Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning

Cited by 65 publications

References 33 publications

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning

Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Effective fine-tuning training of deep Boltzmann machine based on spatial Monte Carlo integration

Contact Info

Product

Resources

About