Description Usage Arguments Details Value References See Also Examples
List the parameters of the default prior model of the Bayesian Magnitude of
Completeness (BMC) method, as defined in Mignan et al. (2011) for different
kth values (only kth = 3
, 4
and 5
allowed, otherwise returns NULL
).
1 | bmc.prior.default(kth)
|
kth |
the kth nearest seismic station used for
distance calculation (if not provided, |
The BMC default model is defined as the prior model evaluated for the Taiwan earthquake data
(Mignan et al., 2011), as it represents the best constrained data set so far (read more
on this in Mignan and Chouliaras, 2014). It often provides a better prior once calibrated
to other data than a new fit to the data (see example given for the function
bmc.prior
). It is also used for rapid mapping of the optimal
mc map (see function mc.geogr
).
the BMC prior parameter list
c1
, c2
, c3
the empirical parameters
sigma
the standard error
kth
the kth nearest seismic station used for
distance calculation
support
the information supporting the prior model (here
support = "default"
)
Mignan, A., Werner, M.J., Wiemer, S., Chen, C.-C., Wu, Y.-M. (2011), Bayesian Estimation of the Spatially Varying Completeness Magnitude of Earthquake Catalogs, Bull. Seismol. Soc. Am., 101, 1371-1385, doi: 10.1785/0120100223
Mignan, A., Chouliaras, G. (2014), Fifty Years of Seismic Network Performance in Greece (1964-2013): Spatiotemporal Evolution of the Completeness Magnitude, Seismol. Res. Lett., 85, 657-667 doi: 10.1785/0220130209
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # map the predicted mc for a set of simulated stations
box <- c(-5, 5, -5, 5); dbin <- 0.1 #degrees
sta.n <- 30
stations <- data.frame(lon = rnorm(sta.n), lat=rnorm(sta.n))
grid <- expand.grid(lon = seq(box[1], box[2], dbin), lat = seq(box[3], box[4], dbin))
grid.n <- nrow(grid)
kth <- 4
params <- bmc.prior.default(kth)
d <- sapply(1:grid.n, function(i) rseismNet::d.geogr2km(grid[i,], stations, method = "fast"))
d.kth <- sapply(1:grid.n, function(i) sort(d[,i])[kth])
mc.pred <- (params$c1 * d.kth ^ params$c2 + params$c3)
image(unique(grid$lon), unique(grid$lat),
matrix(mc.pred, nrow=length(unique(grid$lon)), ncol=length(unique(grid$lat))))
points(stations, pch = 2)
|
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