Prediction of fracture density using genetic algorithm support vector machine based on acoustic logging data

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. D49-D60 ◽  
Author(s):  
Tianyang Li ◽  
Ruihe Wang ◽  
Zizhen Wang ◽  
Mingyuan Zhao ◽  
Lei Li
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Frequency Domain ◽  
Time Domain ◽  
Carbonate Reservoir ◽  
Support Vector ◽  
Core Analysis ◽  
Fracture Density ◽  
Acoustic Logging ◽  
The Time Domain

Existing methods of well-logging interpretation cannot be applied accurately for the exploration and evaluation of carbonate reservoirs because of the fracture development. Based on the fracture density obtained by core analysis in a carbonate reservoir located in the Ordos Basin, in northwest China, three types of fracture density (low fracture density, medium fracture density, and high fracture density) of the target formation were identified. We investigated the effect of fractures on acoustic logging signals in the time and frequency domains by the Hilbert-Huang transform (HHT) and extracted 11 features in the time domain and nine features in the frequency domain. Then, we reduced the features in the time and frequency domain to three principal components by principal component analysis. Finally, a new prediction model of genetic algorithm-support vector machine method based on HHT of acoustic logging data was reported to predict the fracture density. The results indicate that the fracture density has a greater effect on the attenuation of intrinsic mode function 2 (IMF2) and IMF3 components for three different types of formation by empirical-mode decomposition analysis. The energy of the Stoneley wave and S-wave has higher sensitivity than the P-wave. Compared with the time domain, the distribution in the high-frequency domain has a greater correlation with fracture density by the Hilbert spectrum and marginal spectrum. The correlation coefficients between the fracture density and nine features in the frequency domain ([Formula: see text]) are better than the coefficients with 11 features in the time domain ([Formula: see text]). The core analysis and interpretation of resistivity image logging support the validity and effectiveness of our model. The prediction accuracy using the features in the frequency domain can reach to 82%–90%, which is much higher than using the features in the time domain with accuracy of 52%–59%. The application with more information of original acoustic logging data in our model not only avoid the error in velocity picking but also point the direction for the future prediction.

Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features

2021 ◽  
pp. 095745652199983
Author(s):  
Purushottam Gangsar ◽  
Rohit Kumar Pandey ◽  
Manoj Chouksey
Keyword(s):  
Support Vector Machine ◽  
Frequency Domain ◽  
Time Domain ◽  
Diagnostic System ◽  
Support Vector ◽  
Rotating System ◽  
Artificial Intelligence Technique ◽  
Vibration Signals ◽  
Unbalanced Rotor ◽  
The Time Domain

The automated diagnostics of the unbalance in a rotor system has been presented in this study based on an artificial intelligence technique called support vector machine. In order to develop a support vector machine–based unbalance diagnosis, first the raw vibration signals in time and frequency domain are measured experimentally from healthy and unbalanced rotor installed on machine fault simulator. Then, three critical statistical features, namely, standard deviation, skewness, and kurtosis are extracted from the time and frequency domain vibration signals. Further, the features are used for training and testing of the support vector machine for building the automated diagnostic system for unbalance in a rotating system. The results from the present study show that the unbalance fault diagnosis can be effectively done based on the developed support vector machine–based methodology. The automated diagnosis of unbalance is possible with the time domain as well as frequency domain features. The results are better with time domain features than frequency domain features. In addition, the diagnosis is performed and found to be robust at most of the operating speeds of the rotor; however, the diagnosis should be avoided to attempt using the present methodology at very lower operating speeds.

Financial Index Prediction Based on Wavelet Analysis and SVM

2013 ◽  
Vol 303-306 ◽  
pp. 1114-1118
Author(s):  
Xian Tan
Keyword(s):  
Wavelet Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Financial Time Series ◽  
Support Vector ◽  
Frequency Domain Method ◽  
Signal Characteristics ◽  
Vector Machines ◽  
Financial Index ◽  
The Time Domain

The analysis of the time sequence can be two ways in the time domain and frequency domain. But many financial time series exhibit strong non-stationary and long memory, which makes many traditional individually focused on the research and analysis of the time domain or frequency domain method is no longer applicable. In this paper, wavelet analysis and support vector machines for use in the time domain and frequency domain have the ability to characterize the local signal characteristics, location and mutation of the singular points and irregular mutation analysis, these mutations detected the degree of significance.

scholarly journals ECG Signal Classification Based on Fusion of Hybrid CNN and Wavelet Features by D-S Evidence Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jixiang Zhang ◽  
Chengqin Wu ◽  
Chenzhao Ruan ◽  
Rongxia Zhang ◽  
Zengshun Zhao ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Frequency Domain ◽  
Time Domain ◽  
Recognition Performance ◽  
Human Life ◽  
Evidence Theory ◽  
Decision Fusion ◽  
Superior Performance ◽  
Support Vector ◽  
The Time Domain

At present, cardiovascular disease is regarded as one of the dangerous diseases that threaten human life. The morbidity and lethality caused by cardiovascular disease are constantly increasing every year. In this paper, we propose a two-stream style operation to handle the electrocardiogram (ECG) classification: one for time-domain features and another for frequency-domain features. For the time-domain features, convolutional neural networks (CNN) are constructed for feature learning and classification of ECG signals. For the frequency-domain features, support vector regression (SVR) machines are designed to perform the regression prediction on each signal. Finally, the D-S evidence theory is adopted to perform the decision fusion strategy on the time-domain and frequency-domain classification results. We confirm a recognition performance of 99.64% from the experiment result for the D-S evidence theory recognition system upon the MIT-BIH arrhythmia database. The analysis of various methods of ECG classification shows that the model delivers superior performance promotion across all these scenarios.

scholarly journals Application of Xgboost Feature Extraction in Fault Diagnosis of Rolling Bearing

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Xingang WANG ◽  
Chao WANG
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Diagnosis Accuracy

Due to the difficulty that excessive feature dimension in fault diagnosis of rolling bearing will lead to the decrease of classification accuracy, a fault diagnosis method based on Xgboost algorithm feature extraction is proposed. When the Xgboost algorithm classifies features, it generates an order of importance of the input features. The time domain features were extracted from the vibration signal of the rolling bearing, the time-frequency features were formed by the singular value of the modal components that were decomposed by the variational mode decomposition. Firstly, the extracted time domain and time-frequency domain features were input into the support vector machine respectively to observe the fault diagnosis accuracy. Then, Xgboost algorithm was used to rank the importance of features and got the accuracy of fault diagnosis. Finally, important features were extracted and the extracted features were input into the support vector machine to observe the fault diagnosis accuracy. The result shows that the fault diagnosis accuracy of rolling bearing is improved after important feature extraction in time domain and time-frequency domain by Xgboost.

CLASSIFICATION OF PHONOCARDIOGRAM SIGNALS BASED ON ENVELOPE OPTIMIZATION MODEL AND SUPPORT VECTOR MACHINE

2020 ◽  
Vol 20 (01) ◽  
pp. 1950062 ◽  
Author(s):  
LIJUN YANG ◽  
SHUANG LI ◽  
ZHI ZHANG ◽  
XIAOHUI YANG
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Time Domain ◽  
Heart Sounds ◽  
Medical Community ◽  
Support Vector ◽  
Svm Classifier ◽  
Envelope Model ◽  
The Time Domain ◽  
Pcg Signals

The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than [Formula: see text] on both datasets show the effectiveness of the proposed model.

scholarly journals Fetal Heart Rate Classification Based on Time-Frequency Domain Features

2020 ◽  
Author(s):  
Yuqing Que ◽  
Dingke Chen ◽  
Lei Tong ◽  
Chaomin Chen
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Frequency Domain ◽  
Time Domain ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Test Group ◽  
Training Group ◽  
Support Vector ◽  
Frequency Power

Abstract Background: Fetal heart rate(FHR) monitor provides an effective auxiliary diagnosis method for obstetricians, which greatly reduces the misdiagnosis rate.However, sometimes the obstetrician's subjective interpretation is prone to misinterpretation due to different cognition or poor interpretation of the graphics itself. At present, no fetal heart rate monitor can automatically determine the fetal intrauterine state.Based on actual clinical needs, this article mainly studies fetal heart rate signals, designs algorithms to extract feature parameters of fetal heart rate, provides doctors with more accurate and objective quantitative indicators, and reduces the rate of misdiagnosis caused by subjective interpretation.Method: This study uses the CTU-UHB database containing 552 CTG data, and focuses on the analysis of fetal heart rate data. According to the definition of terms in the consensus of electronic fetal heart monitoring experts, the design algorithm extracts the fetal heart rate characteristic parameters based on time domain analysis, such as fetal heart rate baseline, acceleration, deceleration, long variation and short variation, and extracts features based on frequency domain analysis Such as low frequency power, high frequency power, etc. In addition, the approximate entropy of nonlinear parameters is extracted. Then based on the 21 extracted feature parameters, the fetal intrauterine state is classified into two categories, and the training classifier model is established according to the principle of support vector machine. Randomly change the training group (387 cases) many times and enter the test group (165 cases) for prediction.Results: In this paper, the fetal umbilical artery pH value measured after delivery was selected as the FHR signal classification standard, and fetuses with pH greater than 7.05 were classified as normal group, and fetuses with pH≤7.05 were classified as suspicious group. Randomly select the training group and the test group, and after many training and testing of the classifier, the accuracy rate of the test group reaches 90.9%,sensitivity(Se) is 96.3%,specificity(Sp) is 63%.Conclusion：In this paper, support vector machine (SVM) is used to classify fetal heart rate, combined with time domain and frequency domain features, and the classification results are good, indicating that multi-modal features can improve the classification results.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

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