Interactive Receiver Functions Forward
Modeller (IRFFM)
The Interactive Receiver Function Forward
Modeller (IRFFM) is 2 Java programs for interactive forward
modelling of teleseismic receiver functions (the first version, v1.0, was
released in 2009).
IRFFM 1.4 (IRFFM1 v1.3 and IRFFM2 v1.3 together) are the current
versions that have been available since November 2010.
Several
functionalities have been added since v1.0, most notably:
 the program now comes with a display indicating the goodness of fit
(variance reduction in percentages)
 an option to print the resulting plots to either a printer or a jpg
file.
Manuals
These describe the programs and the main requirements.
Please read them for some important information before
download and installation:
irffm11.4_info_and_manual.pdf
irffm21.4_info_and_manual.pdf
The IRFFM code is written in
Java, with helper programs in C, Fortran and C shell script. It depends on the
swt graphics library and the sac seismic package. It should run on most
computers that have access to Java, Java swt and GNU tools such as gfortran. The
complete source code, manual and example input files, can be downloaded here. You will need to register with iEarth prior to download. The README can be viewed separately. Enquires should be directed
to the author.
Screenshots
Screen snapshots showing the IRFFM1 and IRFFM2 interfaces
References
Tkalčić, H., Pasyanos, M., Rodgers, A., Gök, R., Walter, W. &
AlAmri, A. (2006), A multistep approach in joint modeling of surface wave
dispersion and teleseismic receiver functions: Implications for lithospheric
structure of the Arabian peninsula, J. Geophys. Res. 111, B11311,
doi:10.1029/2005JB004130.
IRRFM1 featured in:
Tkalčić, H., Y. Chen, R. Liu, Z. Huang, L. Sun and W. Chan,
MultiStep modelling of teleseismic receiver functions combined with constraints
from seismic tomography: Crustal structure beneath southeast China, Geophys.
J. Int., 187, doi:10.1111/j.1365246X.2011.05132.x,
303326, 2011.
IRFFM2 featured in:
Tkalčić, H., N. Rawlinson, P. Arroucau, A. Kumar and B.L.N.
Kennett, MultiStep modeling of receiverbased seismic and ambient noise data
from WOMBAT array: Crustal structure beneath southeast Australia, Geophys.
J. Int., doi:10.1111/j.1365246X.2012.05442.x,
189, 16811700, 2012.
MultiStep Modelling of Teleseismic Receiver Functions Combined With Constraints From Seismic Tomography:
Crustal Structure Beneath Southeast China
In this study, in which IRFFM software is featured for the first time, we perform a receiverbased study of the lithosphere
of southeast China using the waveform records of excellent quality from fourteen Chinese National Digital Seismic Network (CNDSN) and four Global
Seismic Network (GSN) stations. Receiver functions (RFs) are predominantly sensitive to the gradients in the lithospheric elastic
parameters, and it is impossible to determine a nonunique distribution of seismic parameters such as absolute shearwave speeds
as a function of depth unless other geophysical data are combined with RFs. Thus we combine RFs with independent information from
shear and compressionalwave speeds below the Mohorovičić discontinuity, available from the existing tomographic
studies. The preparation of RFs and consequent analysis consist of multiple steps. First, a statistical approach based on a
calculation of the crosscorrelation matrix is described and used to estimate averaged RFs for a large number of waveforms
available in this study (see Figure 1 below). Second, an interactive forward modelling software (IRFFM) is introduced and
applied to observed RFs to define a prior, physically acceptable range of elastic parameters in the lithosphere. This is followed
by a gridsearch for a simple crustal structure. An initial model for a linearised, iterative inversion is constructed from
multiple constraints, including results from the gridsearch for shearwave speed, the Mohodepth versus vp/vs ratio domain search
and tomography. We obtain 1D velocity profiles for all eighteen stations. The thickness of the crust constrained by the three
independent techniques appears to be more variable in comparison with tomographic studies, with the crust thinning significantly
towards the east (see Figure 2 below).
We used IRFFM to get a quick understanding of the features present in RFs, as well as a quantitative measure about the
range of parameters that produce theoretical RFs similar to the observed RFs. For example, one can explore how the crustal
thickness and the impedance contrast affect the P to S conversion, seen as the second peek in observed RFs. The estimated model
parameters using IRFFM are in a good agreement with the results from the Hk search.
Figure 1. Radial RFs calculated for one of the stations in the study from the southeastern azimuths for all earthquakes
without rejecting waveforms based on signaltonoise ratio are shown in black. Mutually coherent waveforms selected using the
crosscorrelation matrix approach are shown in blue. The selected waveforms are correlated with the crosscorrelation coefficient
0.9 or higher with a) at least 25% of other waveforms and b) at least 50% of other waveforms. The thick red line is the average
calculated from the selected RFs.

Figure 2. Comparison of interpolated maps of crustal thickness (Moho depth) for southeast China using eighteen data points
corresponding to the locations of the stations from this study. a) Pwave tomography (Sun and Toksöz, 2006) and b) this study,
using RFs inversion modelling results

MultiStep modeling of receiverbased seismic and ambient noise data
from WOMBAT array:
Crustal structure beneath southeast Australia
A limitation of most forms of passive seismic tomography using distant
earthquakes lies in the fact that crustal structure is poorly resolved. An
attempt is made here to address this issue by modelling teleseismic receiver
functions (RFs) and dispersion curves derived from ambient noise through a
multistep approach. The SEAL3 experiment in central and southern NewSouth Wales
(NSW) used here, represents one of 13 array deployments that so far comprise the
large WOMBAT project, which aims to cover a significant portion of the
Australian continent with a rolling array of seismometers. An interactive,
forwardmodelling software package (IRFFM2) is introduced and applied to the
observed RFs and surface wave dispersion curves to define a prior, physically
acceptable range of elastic parameters in the lithosphere, which is combined
with a gridsearch and a linearized inversion. Our results emphasize the
importance of a joint treatment of RFs and dispersion data as the predictions
from 1D velocity models at individual stations derived from only RFs display
large departures from the observed ambient noise dispersion curves. In total, 27
jointly constrained 1D shear wave models are produced, which provide sufficient
sampling of the crust beneath SEAL3 to permit detailed inferences about lateral
variations in structure to be made. Of particular note is the observation that
the Moho deepens towards the mountainous southeast, where it exceeds 50 km in
depth beneath the Southern Highlands of NSW, thus marking out some of the
thickest crust in Australia. The complex lateral variations in midlower crustal
velocity that we observe probably reflect the manifold interactions of a
thinning lithosphere, associated igneous underplating, recent hotspotrelated
volcanism and uplift. Our results image an important part of the lithosphere
that is poorly constrained by regional and teleseismic tomography, and
contribute to the understanding of the formation of the southern highlands and
the Palaeozoic Lachlan Orogen.
Figure 3. Comparison of interpolated maps of crustal thickness (Moho
depth) in southeast Australia from different datasets: a) Moho depth from
Collins et al. (2003) (COL) including Clitheroe et al. (2000) (CLI) and
Shibutani et al. (1996) (SHI) data; b) Moho depth from IRFFM2 and gridsearch (Step 2); c) Moho depth
from linearised inversion (Step 3); d) Moho depth from joint inversion of RFs
and ambient noise Rayleigh wave dispersion curves (Step 5); e) Same as d)
including CLI and SHI points; f) Same as d) including COL, CLI and SHI points.
Figure taken from Tkalčić et al. (2012).