kNN-based Gas-bearing Prediction Using Local Waveform Similarity Gas-indication Attribute - An Application to a Tight Sandstone Reservoir

2021 ◽  
pp. 1-45
Author(s):  
Zhaohui Song ◽  
Sanyi Yuan ◽  
Zimeng Li ◽  
Shangxu Wang
Keyword(s):  
Field Data ◽  
Nearest Neighbor ◽  
Prediction Method ◽  
K Nearest Neighbor ◽  
Tight Sandstone ◽  
Unlabeled Sample ◽  
Sensitive Attribute ◽  
Waveform Similarity ◽  
Intelligent Models ◽  
Gas Bearing

Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k Nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.

PREDICTION OF CORONARY ARTERY DISEASE BASED ON ENSEMBLE LEARNING APPROACHES AND CO-EXPRESSED OBSERVATIONS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640010 ◽  
Author(s):  
YING-TSANG LO ◽  
HAMIDO FUJITA ◽  
TUN-WEN PAI
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Nearest Neighbor ◽  
Prediction Method ◽  
Medical Decision ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Artery Disease ◽  
Voting Mechanism

Background: Coronary artery disease (CAD) is one of the most representative cardiovascular diseases. Early and accurate prediction of CAD based on physiological measurements can reduce the risk of heart attack through medicine therapy, healthy diet, and regular physical activity. Methods:Four heart disease datasets from the UC Irvine Machine Learning Repository were combined and re-examined to remove incomplete entries, and a total of 822 cases were utilized in this study. Seven machine learning methods, including Naïve Bayes, artificial neural networks (ANNs), sequential minimal optimization (SMO), k-nearest neighbor (KNN), AdaBoost, J48, and random forest, were adopted to analyze the collected datasets for CAD prediction. By combining co-expressed observations and an ensemble voting mechanism, we designed and evaluated a new medical decision classifier for CAD prediction. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm was applied to determine the best prediction method for CAD diagnosis. Results: Features of systolic blood pressure, cholesterol, heart rate, and ST depression are considered to be the most significant differences between patients with and without CADs. We show that the prediction capability of seven machine learning classifiers can be enhanced by integrating combinations of observed co-expressed features. Finally, compared to the use of any single classifier, the proposed voting mechanism achieved optimal performance according to TOPSIS.

scholarly journals AN INFECTIOUS DISEASE PREDICTION METHOD BASED ON K-NEAREST NEIGHBOR IMPROVED ALGORITHM

2019 ◽  
Vol 11 (01) ◽  
pp. 19-35
Author(s):  
Yaming Chen ◽  
Weiming Meng ◽  
Fenghua Zhang ◽  
Xinlu Wang ◽  
Qingtao Wu
Keyword(s):  
Infectious Disease ◽  
Nearest Neighbor ◽  
Prediction Method ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Improved Algorithm

scholarly journals Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

2019 ◽  
Vol 9 (21) ◽  
pp. 4638 ◽  
Author(s):  
Moayedi ◽  
Bui ◽  
Kalantar ◽  
Foong
Keyword(s):  
Machine Learning ◽  
Slope Stability ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Stability Assessment ◽  
K Nearest Neighbor ◽  
The Stability ◽  
Intelligent Models ◽  
Improved Support Vector Machine

In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.

scholarly journals MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Yu-Tian Wang ◽  
Qing-Wen Wu ◽  
Zhen Gao ◽  
Jian-Cheng Ni ◽  
Chun-Hou Zheng
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Experimental Studies ◽  
Prediction Method ◽  
Disease Association ◽  
High Dimensionality ◽  
K Nearest Neighbor ◽  
Hypergraph Learning ◽  
Disease Associations

Abstract Background MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time‐consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. Methods This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. Result Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. Conclusion MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA–disease association predication.

scholarly journals Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL

2021 ◽  
Vol 11 (20) ◽  
pp. 9389
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Decai Xue ◽  
Shuqing Zhang
Keyword(s):  
Nearest Neighbor ◽  
Prediction Method ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Kernel Principal Component Analysis ◽  
Gear Pump ◽  
K Nearest Neighbor ◽  
Multiple Characteristic ◽  
Gear Pumps ◽  
Useful Life

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just-in-time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.

scholarly journals Quantitative Evaluation of Tight Sandstone Reservoir Based on Diagenetic Facies—A Case of Lower Silurian Kepingtage Formation in Shuntuoguole Low Uplift, Tarim Basin, China

2021 ◽  
Vol 8 ◽  
Author(s):  
Bin Li ◽  
Hanbing Zhang ◽  
Qingsong Xia ◽  
Jun Peng ◽  
Qiang Guo
Keyword(s):  
Tarim Basin ◽  
Nearest Neighbor ◽  
Sedimentary Facies ◽  
K Nearest Neighbor ◽  
Tight Sandstone ◽  
Reservoir Properties ◽  
Lower Silurian ◽  
Sandstone Reservoir ◽  
Sandstone Reservoirs ◽  
Diagenetic Facies

The tight sandstone reservoirs of the Lower Silurian Kepingtage Formation are important exploratory targets for tight gas resources in the Shuntuoguole Low Uplift of Tarim Basin. How to evaluate tight sandstone reservoir is an urgent problem to be solved. In this study, we investigated the effects of diagenesis on the heterogeneity of tight sandstone deposits in similar sedimentary facies and established the relationship between the diagenetic facies and reservoir quality. Cores of the tight sandstone reservoirs of Lower Silurian Kepingtage Formation in Shuntuoguole Low Uplift are studied with thin section observation, SEM, XRD, and mercury injection. Quantification of diagenesis influencing porosity suggests that sandstone densification is mainly controlled by compaction, cementation, and hydrocarbon charging (bitumen charging), and the reservoir properties are effectively improved by dissolution, based on which 6 types of diagenetic facies are classified. Interpretation of the log data from individual wells with “K nearest neighbor” algorithm concludes that top and base of the upper member of Kepingtage Formation are believed to have favorably diagenetic reservoirs mainly falling in Type V; favorably diagenetic facies develop best in the lower member of Kepingtage Formation predominated by Types V and VI which mainly distribute in its top. Composite analysis of diagenetic facies, sedimentary facies, and porosity distribution shows that the favorable area of further exploration and development is east of Well SH903 and north of Well SH10. The quantitative identification of diagenetic facies based on logging information can provide reasonable results for the evolution of the tight sandstone reservoirs for a similar area in the Tarim Basin.

Comparison of the k-nearest neighbor technique with geographical calibration for estimating forest growing stock volumeThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

2011 ◽  
Vol 41 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Jong Su Yim ◽  
Young Hwan Kim ◽  
Sung Ho Kim ◽  
Jin Hyun Jeong ◽  
Man Yong Shin
Keyword(s):  
Sample Size ◽  
Small Area ◽  
Field Data ◽  
Nearest Neighbor ◽  
Small Area Estimation ◽  
K Nearest Neighbor ◽  
Area Estimation ◽  
Growing Stock ◽  
Significant Difference ◽  
Stock Volume

National Forest Inventories (NFIs) have been used in many countries to assess forest resources at the national level. To facilitate the estimation of forest growing stock volume at more regional scales, the k-nearest neighbor (k-NN) technique was applied in this research to obtain estimates for unmeasured areas by using NFI field data and optical satellite data. The NFI field data were assigned to data sets of three different sample sizes to evaluate the effect of sample size on the accuracy of k-NN estimates. In small-area estimation, calibration techniques, in which samples surveyed outside a county of interest are employed to produce estimates for the county, are often adopted due to the lack of sample observations for the county of interest. Thus, the k-NN estimates, forest growing stock volume and areal proportions by forest types, were compared with estimates obtained from field data with and without calibration. The results indicated that the accuracy of k-NN estimates could be improved as sample size increased. Also, the k-NN technique provided acceptable estimates for small-area estimation. Although there was no significant difference with the calibration approach (p > 0.18), k-NN has potential for small-area estimation and is useful to generate thematic maps of forest attributes.

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