Open AccessEarthquake detection capability of the Swiss Seismic Network
Author(s) -
Nanjo K. Z.,
Schorlemmer D.,
Woessner J.,
Wiemer S.,
Giardini D.
Publication year - 2010
Publication title -
geophysical journal international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.302
H-Index - 168
eISSN - 1365-246X
pISSN - 0956-540X
DOI - 10.1111/j.1365-246x.2010.04593.x
Subject(s) - induced seismicity , completeness (order theory) , seismology , geology , magnitude (astronomy) , seismic hazard , computation , probabilistic logic , geodesy , statistics , computer science , algorithm , mathematics , physics , mathematical analysis , astronomy
SUMMARY A reliable estimate of completeness magnitudes is vital for many seismicity‐ and hazard‐related studies. Here we adopted and further developed the Probability‐based Magnitude of Completeness (PMC) method. This method determines network detection completeness ( M P ) using only empirical data: earthquake catalogue, phase picks and station information. To evaluate the applicability to low‐ or moderate‐seismicity regions, we performed a case study in Switzerland. The Swiss Seismic Network (SSN) at present is recording seismicity with one of the densest networks of broad‐band sensors in Europe. Based on data from 1983 January 1 to 2008 March 31, we found strong spatio‐temporal variability of network completeness: the highest value of M P in Switzerland at present is 2.5 in the far southwest, close to the national boundary, whereas M P is lower than 1.6 in high‐seismicity areas. Thus, events of magnitude 2.5 can be detected in all of Switzerland. We evaluated the temporal evolution of M P for the last 20 yr, showing the successful improvement of the SSN. We next introduced the calculation of uncertainties to the probabilistic method using a bootstrap approach. The results show that the uncertainties in completeness magnitudes are generally less than 0.1 magnitude units, implying that the method generates stable estimates of completeness magnitudes. We explored the possible use of PMC: (1) as a tool to estimate the number of missing earthquakes in moderate‐seismicity regions and (2) as a network planning tool with simulation computations of installations of one or more virtual stations to assess the completeness and identify appropriate locations for new station installations. We compared our results with an existing study of the completeness based on detecting the point of deviation from a power law in the earthquake‐size distribution. In general, the new approach provides higher estimates of the completeness magnitude than the traditional one. We associate this observation with the difference in the sensitivity of the two approaches in periods where the event detectability of the seismic networks is low. Our results allow us to move towards a full description of completeness as a function of space and time, which can be used for hazard‐model development and forecast‐model testing, showing an illustrative example of the applicability of the PMC method to regions with low to moderate seismicity.