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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.

Surface topography

The effects of free-surface topography on seismic waveforms are significant, and any local study considering higher-frequency data must take topography into account. In this tutorial we'll explore how to add realistic free-surface topography to a SimulationConfiguration object, and investigate how high-order accurate topography / material interpolation can be used to improve simulation performance. To accomplish this, we'll focus on a real-life use case using the area around Mt. St. Helens in Washington state as an example. Of course please feel free to adapt the geographical area.
As always, we'll start by importing the Salvus namespace.
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%matplotlib inline
import os
import pathlib
import salvus.namespace as sn
--> Server: 'https://data.mondaic.com/license_server/licensing_server', User: '_MONDAIC_INTERNAL_', Group: '_MONDAIC_INTERNAL_'.
--> Negotiating 1 license instance(s) for 'SalvusMesh' [license version 1.0.0] for 1 seconds ...
--> Success! [Total duration: 0.88 seconds]
# This notebook will use this variable to determine which
# remote site to run on.
SALVUS_FLOW_SITE_NAME = os.environ.get("SITE_NAME", "local")
PROJECT_DIR = "project"

Getting topography files

There are a number of ways topography can be specified in Salvus. In addition it has two built-in ways to acquire real world digital elevation data at fairly high resolution:
  1. Via the GMRT web service
  2. Via the AppEEARS web service
We focus on the first approach for the purpose of this tutorial. At the end there is an appendix to demonstrate how to use data acquired via the AppEEARs web service.

Using the GMRT web service

The first step is to define the geographical domain of interest. A convenient way to do this is to use the specialized UtmDomain.from_spherical_chunk constructor that takes WGS84 coordinates and converts them to an appropriate UTM domain. The UTM zone and coordinates could of course also be specified directly.
d = sn.domain.dim3.UtmDomain.from_spherical_chunk(
    min_latitude=46.15,
    max_latitude=46.30,
    min_longitude=-122.28,
    max_longitude=-122.12,
)

# Have a look at the domain to make sure it is correct.
d.plot()