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ردیف عنوان نوع
1 Estimation of seismic wave velocities of metamorphic rocks using artificial neural network
برآورد سرعت موج لرزه ای سنگهای دگرگونی با استفاده از شبکه عصبی مصنوعی-2020
Effective physical parameter evaluation of rocks is of engineering and scientific importance and bears application in conventional and non-conventional energy production. Based on the laboratory and field-scale experiments, many mathematical correlations, empirical and semiempirical are developed considering mineralogy, microstructure and chemical composition. Laboratory experiments measuring the seismic velocities offers natural correlation among physical parameters. However, retrieving an intact lab scale size sample from depth is a challenging task. In this paper, we present an artificial neural network (ANN) trained with chemical composition, temperature and pressure of metamorphic rocks recovered from surface to 10 km depth. The training data is collected from literature, and the network is trained for several combinations of hidden layers and neurons. The best predicting network is used, and predictions are made for rocks from a different location. The results of the model predicted velocities are compared with laboratory measurements reported in the literature. A good agreement is found between the model prediction and measurements. The method could be a viable option for early prediction of seismic wave velocities parameters from chemical composition, temperature and pressure for drilling application and Martian or Lunar planetary heat and seismic studies.
Keywords: Artificial neural network | Seismic wave method | High temperature and pressure | Rock physics Deep learning
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