Starting from version 2.5, the OpenQuake Engine is able to manage MultiPointSources, i.e. collections of point sources with specific properties. A MultiPointSource is determined by a mesh of points, a MultiMFD magnitude-frequency-distribution and 9 other parameters:

  1. tectonic region type
  2. rupture mesh spacing
  3. magnitude-scaling relationship
  4. rupture aspect ratio
  5. temporal occurrence model
  6. upper seismogenic depth
  7. lower seismogenic depth
  8. NodalPlaneDistribution
  9. HypoDepthDistribution

The MultiMFD magnitude-frequency-distribution is a collection of regular MFD instances (one per point); in order to instantiate a MultiMFD object you need to pass a string describing the kind of underlying MFD (‘arbitraryMFD’, ‘incrementalMFD’, ‘truncGutenbergRichterMFD’ or ‘YoungsCoppersmithMFD’), a float determining the magnitude bin width and few arrays describing the parameters of the underlying MFDs. For instance, in the case of an ‘incrementalMFD’, the parameters are min_mag and occurRates and a MultiMFD object can be instantiated as follows:

mmfd = MultiMFD('incrementalMFD',
              bin_width=[2.0, 2.0],
              min_mag=[4.5, 4.5],
              occurRates=[[.3, .1], [.4, .2, .1]])

In this example there are two points and two underlying MFDs; the occurrence rates can be different for different MFDs: here the first one has 2 occurrence rates while the second one has 3 occurrence rates.

Having instantiated the MultiMFD, a MultiPointSource can be instantiated as in this example:

npd = PMF([(0.5, NodalPlane(1, 20, 3)),
          (0.5, NodalPlane(2, 2, 4))])
hd = PMF([(1, 4)])
mesh = Mesh(numpy.array([0, 1]), numpy.array([0.5, 1]))
tom = PoissonTOM(50.)
rms = 2.0
rar = 1.0
usd = 10
lsd = 20
mps = MultiPointSource('mp1', 'multi point source',
                       'Active Shallow Crust',
                        mmfd, rms, PeerMSR(), rar,
                        tom, usd, lsd, npd, hd, mesh)

There are two major advantages when using MultiPointSources:

  1. the space used is a lot less than the space needed for an equivalent set of PointSources (less memory, less data transfer)
  2. the XML serialization of a MultiPointSource is a lot more efficient (say 10 times less disk space, and faster read/write times)

At computation time MultiPointSources are split into PointSources and are indistinguishable from those. The serialization is the same as for other source typologies (call write_source_model(fname, [mps]) or nrml.to_python(fname, sourceconverter)) and in XML a multiPointSource looks like this:

name="multi point source"
tectonicRegion="Stable Continental Crust"
            0.0 1.0 0.5 1.0
            2.0 2.0
            4.5 4.5
            0.10 0.05 0.40 0.20 0.10
            2 3
        <nodalPlane dip="20.0" probability="0.5" rake="3.0" strike="1.0"/>
        <nodalPlane dip="2.0" probability="0.5" rake="4.0" strike="2.0"/>
        <hypoDepth depth="14.0" probability="1.0"/>

The node <lengths> contains the lengths of the occurrence rates, 2 and 3 respectively in this example. This is needed since the serializer writes the occurrence rates sequentially (in this example they are the 5 floats 0.10 0.05 0.40 0.20 0.10) and the information about their grouping would be lost otherwise.

There is an optimization for the case of homogeneous parameters; for instance in this example the bin_width and min_mag are the same in all points; then it is possible to store these as one-element lists:

mmfd = MultiMFD('incrementalMFD',
                occurRates=[[.3, .1], [.4, .2, .1]])

This saves memory and data transfer, compared to the version of the code above.

Notice that writing bin_width=2.0 or min_mag=4.5 would be an error: the parameters must be vector objects; if their length is 1 they are treated as homogeneous vectors of size size. If their length is different from 1 it must be equal to size, otherwise you will get an error at instantiation time.