The IRISSeismic
package for seismic data analysis was developed by Mazama Science for the IRIS DMC (Incorporated Research Institutions for Seismology - Data Management Center). This development is part of the MUSTANG project for automated QC of seismic data.
The goal of this package is to make it easy to obtain and work with data from IRIS DMC web services. This introduction will demonstrate some of the core functionality of the IRISSeismic
package and how it can be used in interactive sessions. Detailed information about object properties and function arguments can be found in the package documentation.
The core objects in this package, especially Trace
and Stream
objects, borrow heavily from concepts and features found in the Python ObsPy package. References to specific ObsPy classes can be found in the source code.
For those who are not used to working with R
, the Using R series of blog posts offers tips on how to get started and includes links to other introductory documentation.
Users of the IRISSeismic
package are encouraged to first download and install the RStudio integrated development environment for R. Newcomers to R
will find RStudio a much friendlier environment in which to work.
Once you have an R environment up and running, the first step is to load the IRISSeismic
package. Then you can create a new IrisClient
object that will be responsible for all subsequent communication with DMC provided web services.
library(IRISSeismic)
iris <- new("IrisClient")
In order to get data from one of the IRIS DMC web services we must specify all the information needed to create a webservice request: network, station, location, channel, starttime, endtime
. Each unique combination of these elements is known as a SNCL. These elements are passed to the getDataselect()
method of the IrisClient
as a series of character strings except for the times which are of type POSIXct
. The user is responsible for creating datetime objects of class POSIXct
.
The first three commands in the following code chunk use the IrisClient
object to communicate with web services and return a Stream
object full of data from the IRIS DMC. The fourth line checks to see how many distinct chunks of seismic data exist. The last line passes this Stream
object to a function that will plot the times at which this channel was collecting data.
starttime <- as.POSIXct("2002-04-20", tz="GMT")
endtime <- as.POSIXct("2002-04-21", tz="GMT")
result <- try(st <- getDataselect(iris,"US","OXF","","BHZ",starttime,endtime))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
length(st@traces)
plotUpDownTimes(st, min_signal=1, min_gap=1)
}
This station had a few minor data dropouts, causing the data to be broken up into several separate signals that the IRISSeismic
package stores in Trace
objects.
We can get more information on the gaps between traces with the getGaps()
function. The duration (secs) of gaps between traces is displayed along with the number of samples that were missed during the gap.
if (exists("st")){
getGaps(st)
}
## $gaps
## [1] 0.0000 58.7750 57.0749 47.5750 52.1750 0.0000
##
## $nsamples
## [1] 0 2351 2283 1903 2087 0
Next we can examine various statistics for each individual trace with the following parallel-
functions.
if (exists("st")){
parallelLength(st)
parallelMax(st)
parallelSd(st)
}
## [1] 101.14186 97.03080 484.53911 135.00670 93.05572
It looks like the third trace, with a larger maximum and standard deviation, might have a signal. Metadata for this trace is stored in the stats
slot of the Trace
object.
if (exists("st")){
tr <- st@traces[[3]]
tr@stats
}
## Seismic Trace TraceHeader
## Network: US
## Station: OXF
## Location:
## Channel: BHZ
## Quality: M
## calib: 1
## npts: 2163653
## sampling rate: 40
## delta: 0.025
## starttime: 2002-04-20 04:43:03
## endtime: 2002-04-20 19:44:34
## latitude: 34.5118
## longitude: -89.4092
## elevation: 101
## depth: 0
## azimuth: 0
## dip: -90
## processing:
Finally, we can look at the seismic signal with the plot
method.
if (exists("tr")){
plot(tr)
}
This small seismic signal was recorded in Oxford, Mississippi and is from a quake that occurred in New York state
Note: By default, data are subsampled before plotting to greatly! improve plotting speed. You can sometimes improve the appearance of a plot by reducing the amount of subsampling used. The plot
method accepts a subsampling
parameter to specify this.
Stream
and Trace
objectsIn order to work effectively with the IRISSeismic
package you must first understand the structure of the new S4
objects it defines. The package documentation gives a full description of each object but we can also interrogate them using the slotNames()
function.
if (exists("st")){
slotNames(st)
}
## [1] "url" "requestedStarttime" "requestedEndtime"
## [4] "act_flags" "io_flags" "dq_flags"
## [7] "timing_qual" "traces"
The Stream
object has the following slots (aka properties or attributes):
url
-- full web services URL used to obtain datarequestedStarttime
-- POSIXct datetime of the requested startrequestedEndtime
-- POSIXct datetime of the requested endact_flags
-- activity flags from the miniSEED recordio_flags
-- I/O flags from the miniSEED recorddq_flags
-- data quality flags from the miniSEED recordtiming_qual
-- timing quality from the miniSEED recordtraces
-- list of Trace
objectsWhen in doubt about what a particular slot contains, it is always a good idea to ask what type of object it is.
if (exists("st")){
class(st@url)
class(st@requestedStarttime)
class(st@traces)
}
## [1] "list"
The next code chunk examines the first Trace
in our Stream
.
Note: R
uses double square brackets, [[...]]
to access list items.
if (exists("st")){
slotNames(st@traces[[1]])
}
## [1] "id" "stats" "Sensor"
## [4] "InstrumentSensitivity" "SensitivityFrequency" "InputUnits"
## [7] "data"
The Trace
object has the following slots:
id
-- SNCLQ identifier of the form "US.OXF..BHZ.M"stats
-- TraceHeader
object (metadata from the trace)Sensor
-- instrument equipment nameInstrumentSensitivity
-- instrument total sensitivity (stage 0 gain)SensitivityFrequency
-- the frequency (Hz) at which the InstrumentSensitivity
is correctInputUnits
-- instrument data acquisition input unitsdata
-- vector of numeric
data (the actual signal)The TraceHeader
metadata and the actual signal come from the dataselect webservice. The instrument metadata are obtained from the station webservice.
Time stamps associated with seismic data should be given as "Universal" or "GMT" times. When specifying times to be used with methods of the IRISSeismic
package you must be careful to specify the timezone as R assumes the local timezone by default.
Also, R assumes that datetime strings are formatted with a space separating date and time as opposed to the ISO 8601 'T' separator. If an ISO 8601 character string is provided without specific formatting instructions, the time portion of the string will be lost without any warning! So it is very important to be careful and consistent if you write code that converts ASCII strings into times.
A few examples will demonstrate the issues:
as.POSIXct("2010-02-27", tz="GMT") # good
## [1] "2010-02-27 GMT"
as.POSIXct("2010-02-27 04:00:00", tz="GMT") # good
## [1] "2010-02-27 04:00:00 GMT"
as.POSIXct("2010-02-27T04:00:00", tz="GMT",
format="%Y-%m-%dT%H:%M:%OS") # good
## [1] "2010-02-27 04:00:00 GMT"
as.POSIXct("2010-02-27") # BAD -- no timezone
## [1] "2010-02-27 PST"
as.POSIXct("2010-02-27T04:00:00", tz="GMT") # BAD -- no formatting
## [1] "2010-02-27 GMT"
The example at the beginning of this vignette already demonstrated how to obtain seismic data from DMC web services, how to learn about the number and size of individual traces within the requested time range and how to generate a first plot of the seismic signal. This section will introduce more use cases that delve further into the capabilities of the IRISSeismic
package. For complete details on available functions, please see the package documentation.
help("IRISSeismic",package="IRISSeismic")
Once seismic data are in memory, performing mathematical analysis on those data can be very fast. All mathematical operations are performed on every data point.
But plotting can still be a slow process.
Note: The plot()
method of Stream
objects deals with gaps by first calling mergeTraces()
to fill all gaps with missing values (NA
). Then the single, merged trace is plotted with the plot()
method for Trace
objects. Any gaps of a significant size will be now visible in the resulting plot.
By default, the plot()
method of Trace
and Stream
objects subsamples the data so that approximately 5,000 points are used in the plot. This dramatically speeds up plotting. One of the first things you will want to do with a full day's worth of seismic signal is clip it to a region of interest. One way to do that would be to modify the starttime
and endtime
parameters to getDataselect
and then make a data request covering a shorter period of time. A simpler technique, if the signal is already in memory, is to use the slice()
method.
starttime <- as.POSIXct("2010-02-27", tz="GMT")
endtime <- as.POSIXct("2010-02-28", tz="GMT")
result <- try(st <- getDataselect(iris,"IU","ANMO","00","BHZ",starttime,endtime))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
start2 <- as.POSIXct("2010-02-27 06:40:00", tz="GMT")
end2 <- as.POSIXct("2010-02-27 07:40:00", tz="GMT")
tr1 <- st@traces[[1]]
tr2 <- slice(tr1, start2, end2)
layout(matrix(seq(2))) # layout a 2x1 matrix
plot(tr1)
plot(tr2)
layout(1) # restore original layout
}
Access to triggering algorithms for detecting events is provided by the STALTA()
method of Trace
objects. ( cf A Comparison of Select Trigger Algorithms for Automated Global Seismic Phase and Event Detection). The STALTA()
method has the following arguments and defaults:
x
-- Trace
being analyzedstaSecs
-- size of the short window in secsltaSecs
-- size of the long window in secsalgorithm
-- named algorithm (default="classic_LR")demean
-- should the signal have the mean removed (default=TRUE
)detrend
-- should the signal have the trend removed (default=TRUE
)taper
-- portion of the seismic signal to be tapered at each end (default=0.0)increment
-- integer increment to use when sliding the averaging windows to the next location (default=1)The STALTA()
method returns a picker, a vector of numeric values, one for every value in the Trace@data
slot. Note that this is a fairly compute-intensive operation. This picker can then be used with the triggerOnset()
function to return the approximate start of the seismic signal.
We'll test this with our original seismic signal.
starttime <- as.POSIXct("2002-04-20", tz="GMT")
endtime <- as.POSIXct("2002-04-21", tz="GMT")
result <- try(st <- getDataselect(iris,"US","OXF","","BHZ",starttime,endtime))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
tr <- st@traces[[3]]
picker <- STALTA(tr,3,30)
threshold <- quantile(picker,0.99999,na.rm=TRUE)
to <- triggerOnset(tr,picker,threshold)
}
NOTE: The STALTA()
method is intended to be used for crude, automatic event detection, not precise determination of signal arrival. Optimal values for the arguments to the STALTA()
method will depend on the details of the seismic signal.
The eventWindow()
method allows you to focus on the region identified by the picker by automatically finding the trigger onset time and then slicing out the region of the trace centered on that time. This method has the following arguments and defaults:
x
-- Trace
being analyzedpicker
-- picker returned by STALTA()
threshold
-- threshold value as used in triggerOnset()
windowSecs
-- total window size (secs)if (exists("tr")){
layout(matrix(seq(3))) # layout a 3x1 matrix
closeup1 <- eventWindow(tr,picker,threshold,3600)
closeup2 <- eventWindow(tr,picker,threshold,600)
plot(tr)
abline(v=to, col='red', lwd=2)
plot(closeup1)
abline(v=to, col='red', lwd=2)
plot(closeup2)
abline(v=to, col='red', lwd=2)
layout(1) # restore original layout
}
The IrisClient
also provides functionality for interacting with other web services at the DMC. The getAvailability()
method allows users to query what SNCLs are available, obtaining that information from the station webservice.
Information is returned as a dataframe containing all the information returned by ws-availability. Standard DMC webservice wildcards can be used as in the example below which tells us what other 'B' channels are available at our station of interest during the time of the big quake above.
starttime <- as.POSIXct("2010-02-27", tz="GMT")
endtime <- as.POSIXct("2010-02-28", tz="GMT")
result <- try(availability <- getAvailability(iris,"IU","ANMO","*","B??",starttime,endtime))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
availability
}
## network station location channel latitude longitude elevation depth azimuth
## 1 IU ANMO 00 BH1 34.94598 -106.4571 1671.0 145.0 328
## 2 IU ANMO 00 BH2 34.94598 -106.4571 1671.0 145.0 58
## 3 IU ANMO 00 BHZ 34.94598 -106.4571 1671.0 145.0 0
## 4 IU ANMO 10 BH1 34.94591 -106.4571 1767.2 48.8 64
## 5 IU ANMO 10 BH2 34.94591 -106.4571 1767.2 48.8 154
## 6 IU ANMO 10 BHZ 34.94591 -106.4571 1767.2 48.8 0
## dip instrument scale scalefreq scaleunits
## 1 0 Geotech KS-54000 Borehole Seismometer 3456640000 0.02 m/s
## 2 0 Geotech KS-54000 Borehole Seismometer 3344400000 0.02 m/s
## 3 -90 Geotech KS-54000 Borehole Seismometer 3275110000 0.02 m/s
## 4 0 Guralp CMG3-T Seismometer (borehole) 32805700000 0.02 m/s
## 5 0 Guralp CMG3-T Seismometer (borehole) 32655100000 0.02 m/s
## 6 -90 Guralp CMG3-T Seismometer (borehole) 33067300000 0.02 m/s
## samplerate starttime endtime snclId
## 1 20 2008-06-30 20:00:00 2011-02-18 19:11:00 IU.ANMO.00.BH1
## 2 20 2008-06-30 20:00:00 2011-02-18 19:11:00 IU.ANMO.00.BH2
## 3 20 2008-06-30 20:00:00 2011-02-18 19:11:00 IU.ANMO.00.BHZ
## 4 40 2008-06-30 20:00:00 2011-02-19 06:53:00 IU.ANMO.10.BH1
## 5 40 2008-06-30 20:00:00 2011-02-19 06:53:00 IU.ANMO.10.BH2
## 6 40 2008-06-30 20:00:00 2011-02-19 06:53:00 IU.ANMO.10.BHZ
The getAvailability()
method accepts the following arguments:
obj
-- an IrisClient
objectnetwork
-- network codestation
-- station codelocation
-- location codechannel
-- channel codestarttime
-- POSIXct starttime (GMT)endtime
-- POSIXct endtime (GMT)includerestricted
-- optional flag whether to report on restricted data (default=FALSE
)latitude
-- optional latitude when specifying location and radiuslongitude
-- optional longitude when specifying location and radiusminradius
-- optional minimum radius when specifying location and radiusmaxradius
-- optional maximum radius when specifying location and radiusSeveral methods of the IrisClient
class work very similarly to the getAvailability()
method in that they return dataframes of information obtained from web services of the same name. The suite of methods returning dataframes includes:
getAvailability
getChannel
getDataselect
getEvalresp
getEvent
getNetwork
getSNCL
getStation
getTraveltime
getUnavailability
The following example demonstrates the use of several of these services together to do the following:
# Open a connection to IRIS DMC webservices
iris <- new("IrisClient")
# Two days around the "Nisqually Quake"
starttime <- as.POSIXct("2001-02-27", tz="GMT")
endtime <- starttime + 3600 * 24 *2
# Find biggest seismic event over these two days -- it's the "Nisqually"
result <- try(events <- getEvent(iris, starttime, endtime, minmag=5.0))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
bigOneIndex <- which(events$magnitude == max(events$magnitude))
bigOne <- events[bigOneIndex[1],]
}
# Find US stations that are available within 10 degrees of arc of the
# event location during the 15 minutes after the event
if (exists("bigOne")){
start <- bigOne$time
end <- start + 900
result <- try(av <- getAvailability(iris, "US", "", "", "BHZ", start, end,
latitude=bigOne$latitude, longitude=bigOne$longitude,
minradius=0, maxradius=10))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
# Get the station the furthest East
minLonIndex <- which(av$longitude == max(av$longitude))
snclE <- av[minLonIndex,]
}
}
# Get travel times to this station
result <- try(traveltimes <- getTraveltime(iris, bigOne$latitude, bigOne$longitude, bigOne$depth,
snclE$latitude, snclE$longitude))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
# Look at the list
traveltimes
# Find the P and S arrival times
pArrival <- start + traveltimes$travelTime[traveltimes$phaseName=="P"]
sArrival <- start + traveltimes$travelTime[traveltimes$phaseName=="S"]
# Get the BHZ signal for this station
result <- try(st <- getDataselect(iris,snclE$network,snclE$station,
snclE$location,snclE$channel,
start,end))
if (inherits(result,"try-error")) {
message(geterrmessage())
} else {
# Check that there is only a single trace
length(st@traces)
# Plot the seismic trace and mark the "P" and "S" arrival times
tr <- st@traces[[1]]
plot(tr, subsampling=1) # need subsampling=1 to add vertical lines with abline()
abline(v=pArrival, col='red')
abline(v=sArrival, col='blue')
}
}