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This documentation is not for the latest stable Salvus version.

This tutorial is presented as Python code running inside a Jupyter Notebook, the recommended way to use Salvus. To run it yourself you can copy/type each individual cell or directly download the full notebook, including all required files.

Data Adaptive Mesh Masking

In many seismologic applications, the source reveiver distribution does not follow a simple geometry such as a square or a circle. Due to the fact that sources are mostly confined to plate boundaries and stations are almost always on land, very complicated domain shapes are common. See [1] for an application.
In this tutorial, we show two different ways of adapting a mesh to the source receiver distribution my removing elements from a larger mesh that are not passed by waves of interest. The example we use here is a quake in Turkey recorded on the Search Results USArray Reference Network (_US-REF).
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%matplotlib inline
%config Completer.use_jedi = False

from salvus.mesh.simple_mesh import SmoothieSEM, Globe3D
from salvus import namespace as sn

# a quake in Turkey that we will use in this tutorial, original data from IRIS spud:
# http://service.iris.edu/fdsnws/event/1/query?eventid=2847365
# http://ds.iris.edu/spudservice/momenttensor/gcmtid/C201003241411A/quakeml#momenttensor
source = sn.simple_config.source.seismology.SideSetMomentTensorPoint3D(
    latitude=38.82,
    longitude=40.14,
    depth_in_m=4500,
    side_set_name="r1",
    mrr=5.47e15,
    mtt=-4.11e16,
    mpp=3.56e16,
    mrt=2.26e16,
    mrp=-2.25e16,
    mtp=1.92e16,
)

receivers = sn.simple_config.receiver.seismology.parse(
    "data/inventory.xml", dimensions=3, fields=["displacement"]
)

# prepare an event collection that will later be used to mask the mesh to a region of interest
event_collection = sn.EventCollection.from_sources(
    sources=[source], receivers=receivers
)

Method 1: Surface Based

In the first approach, we only mask out element based on there lateral position on the sphere and ignore the depth (this approach is used in [1]). To achieve this, a covex hull is built from the sources and receivers and all elements within that hull as well as those within a specified distance are retained in the mesh.
from salvus.mesh.mask_generators import SurfaceMaskGenerator

sm = Globe3D()
sm.basic.model = "prem_iso_one_crust"
sm.basic.min_period_in_seconds = 100.0
sm.basic.elements_per_wavelength = 2.0
sm.spherical.min_radius = 4000.0


# use event collection to create a surface mask
smg = SurfaceMaskGenerator(
    event_collection,
    number_of_points=1000,
    distance_in_km=1000.0,
)

# hand over the mask as a callback funcion
sm.create_mesh(mesh_processing_callback=smg)