Inversion of focal mechanism and events identification using an improved relative amplitude method

The relative amplitude method （RAM） is more suitable for source inversion of low magnitude earthquakes because it avoids the modeling of short-period waveforms. We introduced an improved relative amplitude method （IRAM） which is more robust in practical cases. The IRAM uses a certain function to quantify the fitness between the observed and the predicted relative amplitudes among direct P wave, surface reflected pP and sP waves for a given focal mechanism. Using the IRAM, we got the fault-plane solutions of two earthquakes of mb4.9 and mb3.8, occurred in Issyk-Kul lake, Kyrgyzstan. For the larger event, its fault-plane solutions are consistent with the Harvard＇s CMT solutions. As to the smaller one, the strikes of the solution are consistent with those of the main faults near the epicenter. The synthetic long period waveforms and the predicted P wave first motions of the solutions are consistent with observations at some of regional stations. Finally, we demonstrated that fault-solutions cannot interpret the characteristics of teleseismic P waveforms of the underground nuclear explosion detonated in Democratic People＇s Republic of Korea （DPRK） on October 9, 2006.

Bad Data Detection Algorithm for PMU Based on Spectral Clustering

Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.