Study On Inner Mongolia Region Earthquake Monitoring Ability Evaluation

Currently earthquake monitoring depends on the global seismic stations in the seismicnetworks, as opposed to using a single seismic stations. Seismic network has manyadvantages and determine the location and magnitude of seismic events more reliable. Thescientific assessment of seismic network monitoring capability for thet is an importantfoundation for regional velocity structure of exploration, seismic hazard analysis and otherearth science.The Magnitude of Completeness is an important indicator of seismic networkmonitoring capability assessment instrument in accordance. Theoretical estimates stationresponse curvethe can be calculated from appropriate parameters, the correspondingparameter space station and earth noise, also theory minimum completeness magnitude.Using Network actual recorded earthquake reports, the magnitude of the smallest integritycan be estimate by statistical methods. Statistical methods to calculate the minimummagnitude of completeness can be divided into two categories:one is assumed to be notless than Mc magnitude earthquake in magnitude-frequency distribution meet G-Rrelationship (Gutenberg and Richter,1944), and that the record of these earthquakes arecomplete (Wiemer and Wyss,2000; Cao and Gao,2002; Marsan,2003; Woessner andWiemer,2005; Amorèse,2007); another species is based on the method of non-G-Rrelationship, such as " probability-based integrity magnitude "(Probability-basedMagnitude of Completeness, PMC) method (Schorlemmer and Woessner,2008), and "Bayesian integrity magnitude "(Bayesian Magnitude of Completeness, BMC) method(Mignan, et al,2011;. Mignan, et al,2013;) and so on.According to instrument parameters of seismometers, seismic stations venueresponse and noise power spectrum can be measured, and theory monitoring capabilitiescan be estimated with noise ratio assumption identification. However, this method is basedon the the SNR assumptions for phases to be can clearly identify, and the uncertainties instation network operation can not be consider. Methods based on the magnitude of therelationship between the frequency of assessment, using G-R distribution assumption,maynot hold in small samples or deviations, resulting assessment bias. BMC and PMC andother non-G-R based methods have to examine the relationship between seismic stationsto locate the artificial selection station network monitoring capability caused by thedecline, avoid the advantages of traditional statistical algorithms based G-R relationshipbecause the number is too small earthquakes can not be assessed, but these methods are based on the network report integrity assumption, yet in poor monitoring capabilities areathis assumption is unreasonable.There are three typical methods (noise power spectrum method, EMR methods andPMC method) used in this paper. Station network monitoring capabilities to assess theresults were compared various methods to explore the applicability of the method for theimprovement program. For poor regions do not satisfy the monitoring station networkintegrity assumptions used herein Network PMC method detection rate model results werecorrected. This article also applied to assess the results of seismic network monitoringcapability Inner distribution and variation in space and time, and proposed station networkoptimization recommendations.In this paper, the following works have been done:1Inner seismic data reduction.2Use the noise power spectrum method, EMR method and PMC method to calculatethe integrity magnitude and compare the results.3Use Network detection rate model to correct the PMC method result, and analyzethe results.4Analyze seismic network monitoring capability variation in space and time.