Comparative Performance Assessments of Machine-Learning Methods for Artificial Seismic Sources Discrimination

Abstract

Mankind is vulnerable to artificial seismic sources and accompanying explosions' consequences. Recently, seismicity catalog contamination is among the main problems faced by seismologists. Since identifying artificial seismic sources is the first and always challenging stage, it is imperative to develop an automated control system that will discriminate tectonic from non-tectonic events. Detection and removal of the artificial seismic sources have become urgent. Early treatments and cleaning of contaminated seismicity catalogs are crucial to assist in accurate seismic hazard identification and enhance the planning of future urban developments. With the advancement of machine learning (ML) techniques, artificial seismic source detection accuracy has been improved. Today, there are different kinds of methods, ML techniques, and diverse processes like knowledge discovery are developed for discriminating artificial seismic sources and earthquakes. ML techniques offer various probabilistic and statistical methods that allow intelligent systems to learn from reoccurring experiences to detect and identify patterns from a dataset. This study aims to build an automated system that is able to detect the existence of artificial seismic sources in seismicity catalogs. More concretely, we classify seismic activity reports into two classes using classical and ensemble ML algorithms. Classical seismicity parameters or features are supplied to linear and nonlinear ML classifiers. The proposed scheme based on the four features (Latitude, Longitude, depth, and Magnitude) can enhance the performance. To assure the enhanced performance, we have examined the proposed scheme by both the accuracy of each model, ROC curves, Precision-Recall, and Calibration. The obtained results prove that the ensemble learning algorithms exhibit better results compared to other classical ML algorithms by having 98.14% testing accuracy.

Analysis of Blasting Vibration Effect of Railway Tunnel and Determination of Reasonable Burial Depth

“…A large number of scholars [1][2][3][4] have conducted related studies on the analysis of the blasting vibration effect of mountain tunnels. Qin and Zhang [5] found that there is a good fitting relationship between the peak blasting vibration velocity and the total charge.…”

“…Wang et al [8] elaborated the blasting vibration control of a three-dimensional intersection tunnel in detail and achieved a good control effect. In addition, modern technologies [2] represented by machine learning [9] are also widely used in research in this field. Hasanipanah et al [10] made a prediction and analysis of ground vibration caused by blasting construction based on particle swarm optimization and achieved good results.…”

Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network

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Toward Secured IoT-Based Smart Systems Using Machine Learning

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“…Data mining and reassembling irregular or missing data using ML is another useful application of ML. In [17] and [16], ML algorithms have been proposed to assess the peak particle velocity (PPV) and discriminate the quarry blasts. The peak ground acceleration and its impact on urban planning have both been predicted using ML [36].…”