Determination of the elastic parameters of a VTI medium from sonic logging data using deep learning

Bazulin, Maksim; Sabitov, Denis; Charara, Marwan

doi:10.1016/j.cageo.2021.104759

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…Deep learning was first proposed by Hinton et al to study the optimal representation of information and its acquisition method. In the case of neural networks or belief networks, it is the process of machine learning of the mapping between inputs and outputs based on deep structures or network representations [ 19 ]. Currently, deep learning is a hold expression for a group of pattern recognition techniques.…”

Section: Related Theorymentioning

confidence: 99%

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Feng

2022

Computational Intelligence and Neuroscience

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Currently, under the impact of the COVID-19, college students are facing increasingly elevated employment pressure and higher education pressure. This can easily cause a huge psychological burden on them, causing affective cognition problems such as anxiety and depression. In the long run, this is not conducive to students’ physical and mental health, nor is it conducive to the healthy development of the school and even the whole society. Therefore, it is imperative to build a novel adaptive affective cognition analysis model for college students. In particular, in the context of smart cities and smart China, many universities have opened the smart campus mode, which provides a huge data resource for our research. Due to problems of the low real-time evaluation and single data source in traditional questionnaire evaluation methods, evaluation errors are prone to occur, which in turn interferes with subsequent treatment. Therefore, for the purpose of alleviating the above deficiencies and improving the efficiency and accuracy of the affective cognition analysis model of college students, this paper studies the adaptive affective cognition analysis method of college students on basis of deep learning. First, because students’ psychological problems are often not sudden, on the contrary, most of these abnormalities will leave traces in their daily activities. Therefore, this paper constructs a multisource dataset with the access control data, network data, and learning data collected from the smart campus platform to describe the affective cognition status of students. Second, the multisource dataset is divided into two categories: image and text, and the CNN model is introduced to mine the psychological characteristics of college students, so as to provide a reference for the subsequent affective cognition state assessment. Finally, simulation tests are developed to confirm the viability of the technique suggested in this research. The experiments demonstrate that the accuracy of the assessment model is significantly increased because it can fully reflect the heterogeneity and comprehensiveness of the data. This also highlights that the new method has a wide range of potential applications in the modern campus setting and is also helpful in fostering the accuracy and depth of college students’ work on their affective cognition.

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Section: Related Theorymentioning

confidence: 99%

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Feng

2022

Computational Intelligence and Neuroscience

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Elaborate Evaluation of Salt Formation and Old Cavern Forecast in Y City Salt Cavern Underground Gas Storage

Zhang¹,

Gao²,

Chen³

et al. 2023

Springer Series in Geomechanics and Geoengineering

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Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling

Zhao

et al. 2021

Appl. Geophys.

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Determination of the elastic parameters of a VTI medium from sonic logging data using deep learning

Cited by 8 publications

References 34 publications

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Elaborate Evaluation of Salt Formation and Old Cavern Forecast in Y City Salt Cavern Underground Gas Storage

Backpropagation neural network method in data processing of ultrasonic imaging logging-while-drilling

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