Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification

The discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Naïve Bayes Classifier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were discussed and compared. The result of RF method clearly shows the best predictive power with a maximum area of 0.975 under the ROC among RF, SVM, and NBC. The discriminant accuracies of RF, SVM, and NBC for test samples are 92.86%, 85.71%, and 92.86%, respectively. It has been demonstrated that the presented RF model can not only identify seismic event automatically with high accuracy, but also can sort the discriminant indicators according to calculated values of weights

Theoretical and Case Studies of Interval Nonprobabilistic Reliability for Tailing Dam Stability

The stability of the operation of a tailing dam is affected by reservoir water level, phreatic line, and mechanical parameters of tailings. The values of these factors are not a definite value in different situations. Meanwhile, the existence of the phreatic line makes it a more complex issue to analyze the stability of the tailing dam. Additionally, it is very hard to give a definite limit to the state of tailing dam from security to failure. To consider the uncertainty when calculating the stability of the tailing dams, interval values are used to indicate the physical and mechanical parameters of tailings. An interval nonprobabilistic reliability model of the tailing dam, which can be used when the data is scarce, is developed to evaluate the stability of the tailing dam. The interval nonprobabilistic reliability analysis model of tailing dam is established in two cases, including with and without considering phreatic line conditions. The proposed model was applied to analyze the stability of two tailing dams in China and the calculation results of the interval nonprobabilistic reliability are found to be in agreement with actual situations. Thus, the interval nonprobabilistic reliability is a beneficial complement to the traditional analysis method of random reliability

An Analytical Solution for Acoustic Emission Source Location for Known P Wave Velocity System

This paper presents a three-dimensional analytical solution for acoustic emission source location using time difference of arrival (TDOA) measurements from N receivers, N⩾5. The nonlinear location equations for TDOA are simplified to linear equations, and the direct analytical solution is obtained by solving the linear equations. There are not calculations of square roots in solution equations. The method solved the problems of the existence and multiplicity of solutions induced by the calculations of square roots in existed close-form methods. Simulations are included to study the algorithms' performance and compare with the existing technique

Acoustic emission source location method and experimental verification for structures containing unknown empty areas

Acoustic emission (AE) localization plays an important role in the prediction and control of potential hazardous sources in complex structures. However, existing location methods have less discussion on the presence of unknown empty areas. This paper proposes an AE source location method for structures containing unknown empty areas (SUEA). Firstly, this method identifies the shape, size, and location of empty areas in the unknown region by exciting the active AE sources and using the collected AE arrivals. Then, the unknown AE source can be located considering the identified empty areas. The lead break experiments were performed to verify the effectiveness and accuracy of the proposed method. Five specimens were selected containing empty areas with different positions, shapes, and sizes. Results show the average location accuracy of the SUEA increased by 78% compared to the results of the existing method. It can provide a more accurate solution for locating AE sources in complex structures containing unknown empty areas such as tunnels, bridges, railroads, and caves in practical engineering

K

Seismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment. In this paper, seismic event location (X,Y,Z) and Euclidean distance were selected as the K-Means cluster, the Gaussian mixture model (GMM), and the self-organizing maps (SOM) input features and cluster determination measurement, respectively, and 1516 seismic events (M>-1.5) obtained from the Yongshaba mine (China) were chosen for the cluster analysis. In addition, a Silhouette and Krzanowski-Lai- (KL-) combined S-KL index was proposed to obtain the possible optimum cluster number and to compare the cluster methods. Results show that the K-Means cluster obtains the best cluster “quality” with higher S-KL indexes on the whole and meaningful clusters. Furthermore, the optimal number for detailed geological structure interpretation is confirmed as eleven clusters, and we found that two areas probably have faults or caves, and two faults may be falsely inferred by mine geologists. Seismic hazard assessment shows that C5 and C7 (K=11) have a high mean moment magnitude (mM) and C1, C2, C3, and C4 (K=11) have a relatively high mM, where special attention is needed when mining. In addition, C7 (K=11) is the most shear-related area with a mean S-wave to P-wave energy ratio (mEs/Ep) of 41.21. In conclusion, the K-Means cluster provides an effective way for mine seismicity partitioning, geological structure interpretation, and seismic hazard assessment

Quantitative Evaluation and Case Study of Risk Degree for Underground Goafs with Multiple Indexes considering Uncertain Factors in Mines

The accidents caused by underground goafs are frequent and destructive due to irregular geometric shapes and complex spatial distributions, which caused severe damage to the environment and public health. Based on the theories of uncertainty measurement evaluation (WME) and analytic hierarchy process (AHP), the comprehensive risk evaluation of underground goafs was carried out using multiple indexes. Considering the hydrogeological conditions, mining status, and engineering parameters of underground goafs, the evaluation index system was established to evaluate the risk degrees considering quantified uncertain factors. The single index measurement values were solved by the semiridge measurement function. The weights for evaluation vectors were calculated through the entropy theory and AHP. Finally, the risk level was evaluated according to the credible degree recognition criterion (CDRC) and the maximum membership principle. The risk levels of 37 underground goafs in Dabaoshan mine were evaluated using 4 coupled methods. The order for underground goafs risk degrees was ranked and classified on account of the uncertainty important degree. According to the ranked order, the reasonability of 4 coupled methods was evaluated quantitatively. Results show that the UME-CDRC can be applied in the practical engineering, which provides an efficient guidance to both reduce the accident risk and improve the mining environment

A Microseismic/Acoustic Emission Source Location Method Using Arrival Times of PS Waves for Unknown Velocity System

To eliminate the location error of MS/AE (microseismic/acoustic emission) monitoring systems caused by the measurement deviations of the wave velocity, a MS/AE source location method using P-wave and S-wave arrivals for unknown velocity system (PSAFUVS) was developed. Arrival times of P-wave and S-wave were used to calculate and fit the MS/AE source location. The proposed method was validated by numerical experimentations. Results show that the proposed method without the need for a premeasured wave velocity has a reasonable and reliable precision. Effects of arrival errors on location accuracy were investigated, and it shows location errors enlarged rapidity with the increase of arrival errors. It is demonstrated the proposed method can not only locate the MS/AE source for unknown velocity system but also determine the real time PS waves velocities for each event in rockmass

Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers

The relationships between geological features and rockmass behaviors under complex geological environments were investigated based on multiple intelligence classifiers. Random forest, support vector machine, bayes' classifier, fisher's classifier, logistic regression, and neural networks were used to establish models for evaluating the rockmass stability of slope. Samples of both circular failure mechanism and wedge failure mechanism were considered to establish and calibrate the comprehensive models. The classification performances of different modeling approaches were analyzed and compared by receiver operating characteristic (ROC) curves systematically. Results show that the proposed random forest model has the highest accuracy for evaluating slope stability of circular failure mechanism, while the support vector Machine model has the highest accuracy for evaluating slope stability of wedge failure mechanism. It is demonstrated that the established random forest and the support vector machine models are effective and efficient approaches to evaluate the rockmass stability of slope