!!TOP!! Download EARTHQUAKE MAGNIDUTE DATA With Lambda (1) Xlsx
where \(\lambda_y\) is the mean annual rate of exceedance of the ground motion y; \(\nu_i\) is the mean annual rate of occurrence of earthquakes with magnitude larger than, or equal to, m0 in the seismogenic source i; fM(m) and fR(r) are the probability density functions of the magnitude m and distance r, respectively; and NS is the number of the seismogenic sources considered in the hazard computation. The above equation represents the seismic hazard curve, in terms of the annual rate (almost equivalent to probability) of exceeding a threshold shaking (McGuire 1995, 2004). The conditional probability P[Y>yM=m, R=r] is simply given by the GMM.
Download EARTHQUAKE MAGNIDUTE DATA with lambda (1) xlsx
The compilation of the catalogue followed two phases: the preparation of a data set for the Balkan area (Greece and Albania) and another for Italy and the Ionian Sea. The primary data sources are reported in Table 1. All the data have been checked (257 events modified) for the period preceding the year 1900 using the SHEEC catalogue (Stucchi et al. 2012), developed in the framework of the European project SHARE (Giardini et al. 2014) and released to the scientific community during the TAP project. Moreover, all Italian entries have been checked (187 events modified) with those of the most recent version of the Italian earthquake catalogue available at that time: i.e., CPT11 (Rovida et al. 2011). Double events have been eliminated following a quality criterion, giving preference to the local information.
The CE model was introduced by Schwartz and Coppersmith (1984) for the seismicity of the Wasatch and San Andreas faults in California and states that each fault can produce events of a specific narrow range of magnitude, in agreement with the geometric dimensions of the possible ruptures along the fault. A similar model (maximum magnitude model) was also suggested by Wesnousky (1986) for the seismicity associated with the faults in California. Meanwhile, Youngs and Coppersmith (1985) defined the probabilistic distribution for a model with a GR distribution for weak and moderate earthquakes and a CE (uniform) distribution for the high magnitudes: the authors called also this CE model. According to this model, Wells and Coppersmith (1994) developed scaling laws between the fault-rupture dimensions and the related characteristic magnitude. The difficulty of associating earthquakes to faults does not allow the seismologists to ultimately decide whether this model is valid worldwide, only for some faults or never. In any case, its likelihood is quite good and both DISS (Basili et al. 2008) and GreDaSS (Caputo and Pavlides 2013) are based on this model. Without entering into the scientific debate on the validity of the CE model, supported by observational data (Swan et al. 1980; Papageorgiou and Aki 1983) but also challenged (Grant 1996; Bakun et al. 2005), the estimation of the maximum expected magnitude for a fault is based on the expected rupture geometries, in terms of rupture length or rupture area, available from direct observations only for some recent events.
The approach by Kjiko and Graham (1998) computes Mmax for a source on a statistical basis using as input data: the maximum observed magnitude (Mmaxobs); the threshold magnitude considered complete in the catalogue; the average error in the magnitude estimates (fixed in our case arbitrarily at 0.2); the b-value of the GR relation and its SD; the annual rate (i.e., the number of earthquakes with magnitude greater than, or equal to, the threshold magnitude); and the catalogue time span which is considered complete. This last parameter was set equal to the completeness period of Mmaxobs. Four formulations for Mmax computation are implemented in the method; we applied here the Bayesian formula of Kijko and Sellevoll (1989). The Mmax values estimated for each SZ are reported in the electronic supplements ES3 and ES4 and mapped in Figs. 10a and b for the two zonations Z100 and Z200. The highest values of Mmax (larger than 7.0) are found along the Montenegro coast, in the easternmost sector of the TAP route in Greece and eastwards (Figs. 10a and b), where the North Anatolian Fault and its continuation along the North Aegean Trough dominate the tectonic framework. The highest expected Mmax, i.e., even larger than 7.5 is in the area north of Thessaloniki and the border area with Turkey (Figs. 10a and b) crossed by the TAP. Z200 determines that the whole of Albania shows an expected Mmax between 6.5 and 7.0 (Fig. 10b) instead of only the sector affecting the TAP route (Fig. 10a). In a few SZs, Mmax results were extremely low (see grey and pale blue SZs in Figs. 10a and b), mainly because of the poorly documented seismicity and, consequently, the limited range of magnitude classes. A check has been done by increasing Mmax to 5.5, but no difference was observed in the computed seismic hazard.
All volcano-structural datasets were processed in ArcMap with a Universal Transverse Mercator (UTM) projection. For point data (i.e., volcanic vents, thermal anomalies, and earthquake epicentres), northing and easting coordinates were extracted. For line data (i.e., structures and eruptive fissures), starting and ending coordinates were noted. Finally, all these layers were then exported to an Excel file in which each dataset was assigned to a separate sheet (Additional file 1).
MatHaz needs two Excel files (.xlsx extension) as inputs. The initial file has to contain as many sheets as there are volcano-structural datasets; with separate sheets required for volcanic vents, faults, fissures, and any other spatial feature considered important (Additional file 1). The second file has to be a Digital Elevation Model written in ASCII (American Standard Code for Information Interchange) format (Additional file 3). 041b061a72