A support vector regression approach to predict geotechnical properties of soils from electrical spectra based on Jonscher parameterization

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. EN39-EN48
Author(s):  
Fred Kofi Boadu
Keyword(s):  
Support Vector Regression ◽  
Geophysical Methods ◽  
Geotechnical Properties ◽  
Unit Weight ◽  
Support Vector ◽  
Natural Soil ◽  
Regression Approach ◽  
Model Training ◽  
Engineering Geophysics ◽  
Conductivity Spectra

Electrical-conductivity spectra of soils contain valuable information about their texture, structure, and composition that can be linked to their geotechnical properties. Concurrent measurements of electrical spectra in the frequency range of 0.01 Hz to 10 kHz and geotechnical properties, that is, the dry unit weight [Formula: see text], modulus of elasticity [Formula: see text], and the hydraulic conductivity [Formula: see text], are performed on natural soil samples in a laboratory environment. The electrical spectra are modeled with the Jonscher fractal power law model characterized by three parameters: DC conductivity [Formula: see text], transition frequency [Formula: see text], and an exponent [Formula: see text]. We explore a machine-learning technique, the support vector regression (SVR) methodology, to model and predict the geotechnical properties from the Jonscher parameters, and we compare our results with the predictions of multiple linear regression (MLR). For model training and testing, the Jonscher parameters are used as the input, and a geotechnical parameter is used as the output. Model comparisons indicate that the developed SVR models predict [Formula: see text] with an [Formula: see text], predict [Formula: see text] with [Formula: see text], and predict [Formula: see text] with [Formula: see text]. In comparison, MLR models predict [Formula: see text] with an [Formula: see text], [Formula: see text] with [Formula: see text], and [Formula: see text] with [Formula: see text]. The results illustrate that the SVR models are more accurate, reliable, and achieve better performance for predicting the geotechnical properties from the electrical parameters in comparison to the predictions of the MLR models. Our study offers an opportunity in our quest in using noninvasive electrical geophysical methods to obtain geotechnical properties of soils, and it has broad implications in engineering geophysics.

scholarly journals Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Ming-Yi You ◽  
Yun-Xia Ye ◽  
Bin Jiang ◽  
An-Nan Lu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Training Data ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Model Training ◽  
Finding Method

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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