تخمین سرعت موج برشی به روش رگرسیون فرایند گاوسی، رگرسیون چندمتغیره و شبکه عصبی مصنوعی پرسپترون چند لایه

نویسندگان

1 استادیار گروه مهندسی صنایع، دانشگاه صنعتی بیرجند، بیرجند، ایران

2 استادیار دانشکده فنی و مهندسی، دانشگاه صنعتی ارومیه، ارومیه، ایران

3 استادیار گروه مهندسی معدن، دانشگاه صنعتی بیرجند، بیرجند، ایران

چکیده

تخمین سرعت امواج برای طراحی سازه­های ژئوتکنیکی و مدل­سازی حفاری­های عمیق اهمیت زیادی دارد. هدف از این مطالعه تخمین سرعت موج برشی به روش رگرسیون فرایند گاوسی با استفاده از تابع کرنل نمایی، شبکه عصبی مصنوعی و رگرسیون چندمتغیره می­باشد. به منظور انجام این مطالعه، 14 بلوک سنگی از شمال غرب شهر دماوند تهیه و بعد از انتقال به آزمایشگاه از آن­ها مغزه­گیری شد. جهت توسعه یک مدل پیش­بینی کننده، آزمایش­های شاخص بار نقطه­ای، سرعت موج تراکمی، تخلخل و دانسیته بر روی 61 نمونه مغزه سنگی انجام شد. شاخص بار نقطه­ای، سرعت موج تراکمی، تخلخل و چگالی به عنوان پارامترهای ورودی مدل­ها به منظور پیش­بینی سرعت موج برشی استفاده شدند. نتایج مطالعات سنگ­شناسی نشان داد که ماسه­سنگ­های مورد مطالعه، فلدسپاتیک لیتارنایت و ‌‌لیت‌آرنایت می­باشند. نتایج نشان داد که نسبت سرعت موج تراکمی به سرعت موج برشی بطور متوسط برابر با 70/1 می­باشد. نتایج شبکه عصبی مصنوعی پرسپترون چند لایه نشان داد که بیش­ترین دقت مدل­ها با استفاده از قانون آموزش لونبرگ مارکوارت حاصل شده است. دقیق­ترین مدل­ها با استفاده از این الگوریتم برای تخمین سرعت موج برشی در نورون شماره 2 (نورون بهینه) حاصل شد. روش رگرسیون فرایند گاوسی، شبکه عصبی مصنوعی و رگرسیون چندمتغیره سرعت موج برشی را به ترتیب با ضرایب همبستگی 97/0، 96/0 و 95/0 پیش بینی نمودند. روش رگرسیون فرایند گاوسی عملکرد بهتری در پیش­بینی سرعت موج برشی نسبت به سایر روش­ها نشان داد.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of shear wave velocity using Gaussian process regression, multivariate regression and multilayer perceptron artificial neural network

نویسندگان [English]

  • M. Saffarian 1
  • A. Iraji 2
  • A. Azadmehr 3
1 Assist. Prof., Dept., of Industrial Engineering, Birjand University of Technology, Birjand, Iran
2 Assist. Prof., Dept., of Civil Engineering, Urmia University of Technology, Urmia, Iran
3 Assist. Prof., Dept., of Mining Engineering, Birjand University of Technology, Birjand, Iran
چکیده [English]

Estimation of wave velocities is very important for designing geotechnical structures and modeling deep drillings. The purpose of this study is to estimate shear wave velocity (Vs) using Gaussian process regression (GPR), multilayer perceptron artificial neural network (MLP-ANN) and multivariate linear regression (MVLR) methods. In order to carry out this study, 14 rock blocks were prepared from the northwest of Damavand city and after being transferred to the laboratory, cores were extracted from them. In order to develop a predictive model, point load index, compressional wave velocity (Vp), porosity and density tests were performed on 61 rock core samples. Point load index, Vp, porosity and density were used as input parameters of models to predict Vs. The results of lithological studies showed that the studied sandstones are feldspathic litharnite and litharnite. The results showed that the ratio of Vp to Vs is equal to 1.70 on average. The results of the MLP-ANN showed that the highest accuracy of the models was obtained by using the Levenberg-Marquardt training algorithm. The most accurate models were obtained using this algorithm to estimate the Vs in neuron number 2 (optimal neuron). The GPR, MLP-ANN and MVLR predicted Vs with correlation coefficients of 0.97, 0.96 and 0.95, respectively. GPR method showed better performance in predicting Vs than other methods.

کلیدواژه‌ها [English]

  • Physical and mechanical properties
  • Sandstone rocks
  • Gaussian process regression (GPR)
  • Multilayer perceptron artificial neural network (MLP-ANN)
  • Multivariate linear regression (MVLR)

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  • تاریخ دریافت: 14 مرداد 1401
  • تاریخ بازنگری: 18 دی 1401
  • تاریخ پذیرش: 21 دی 1401

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