Research on the Source Parameters and Correlation Coefficients of Focal Mechanisms for the Yingjiang Earthquake Sequences in 2008

The source parameters of the Yingjiang earthquake sequences in 2008 are obtained by applying spectral analysis and Brunes source model,based on the digital waveform data recorded by the Yunnan Digital Seismic Network.The correlation coefficients are calculated using the low-frequency spectral amplitudes of 2 events recorded by a same station,then,events with similar focal mechanism are grouped using the clustering analysis method.Compared to the obtained focal mechanisms,it is found that there are good correlations with the azimuth of P axes in each clustering group,and the larger the correlation coefficient,the closer the azimuths of P axes.We divide the Yingjiang area into 3 regions to analyze the stress level and stress direction by combining the source parameters and the mean focal mechanism of each group.The results show:The change and transformation of the focal mechanism types at different stages can represent the temporal characteristics of the regional stress field.If the earthquake focal mechanism types are concentrated in a time period and switch to the direction of regional stress field,it may be a sign of strong earthquake.There is some relationship between the stress drop and the type of focal mechanism.Those earthquakes with stress fields revealed by focal mechanism types closer to the regional tectonic stress field will have higher stress drop,while those with the focal mechanism-revealed stress fields differing a lot from the regional tectonic stress field will generally have a lower stress drop.

Can environmental sustainability be decoupled from economic growth? Empirical evidence from Eastern Europe using the common correlated effect mean group test

The European Union(EU) and Organisation for Economic Co-operation and Development(OECD) aim to develop long-term policies for their respective member countries. Having observed increasing dangers to the environment posed by rising economic growth, they are seeking pathways to enable policy action on economic growth and environmental sustainability. Given the facts in theoretical and empirical studies, this study assessed the validity of the decoupling hypothesis by investigating asymmetricity in the relationship between environmental sustainability and economic growth in nine Eastern European countries from 1998 to 2017 using the cross-section augmented Dickey-Fuller(CADF) unit root, panel corrected standard error(PCSE), common correlated effect mean group(CCEMG), and Dumitrescu Hurlin causality approaches. Both population growth and drinking water are used as controlled variables. The outcomes establish strong cointegration among all the variables of interest. According to the results of CCEMG test, economic growth exerts short-term environmental degradation but has long-term environmental benefits in Eastern Europe;and population growth and drinking water exert a positive effect on environmental sustainability in both the short-and long-run. The results of Dumitrescu Hurlin causality test indicate that environmental sustainability is unidirectionally affected by economic growth. Based on these outcomes, we suggest the following policies:(1) the EU and OECD should implement member-targeted policies on economic growth and fossil-fuel use towards regulating industrial pollution, water use, and population control;and(2) the EU and OECD member countries should invest in environmental technologies through green research and development(R&D) to transform their dirty industrial processes and ensure productive energy use.

Frequency correlation and group time delay of ambient noise and ship radiated-noise in the sea

The characteristics of frequency correlation and group time delay of ambient noise and ship radiated-noise in the sea are studied. The theoretical and experimental results show that the frequency correlation of ship radiated-noise is much greater than that of ambient noise,and the frequency correlation of ship radiated-noise at long distance has obvious group time delay

Multivariate normal spatial scan statistic for detecting the most severe cluster of a disease

This paper focuses on the problem of detecting the geographical cluster with the most severe status in multiple groups of population given limited medical resources.Populations are grouped based on characteristics such as age,gender,and race.In the early stages of a disease,an outbreak may only present in specific population groups.Therefore,to efficiently detect the outbreak,we are particularly interested in monitoring and evaluating such groups.We define the objective of detection as the most severe cluster(MSC).Taking into account the interactions between population groups,a multivariate normal scan statistic is proposed to simultaneously determine the location and size of a significant MSC,as well as the specific population groups in which the MSC is located.The proposed method is applied to an example of lung cancer in New York State,where the MSC with the highest mortality rate at the aggregate level is detected.Further,the detection capacity of this method is evaluated using a simulation study based on the lung cancer example.