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Tutorial_Votemaps.jl
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Tutorial_Votemaps.jl
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# # Votemaps
#
# ## Aim
# In this tutorial, your will learn how to create Votemaps that compare different tomographic models and look for similarities between different models.
#
# ## Steps
#
# #### 1. Load data
# We assume that you have all tomographic models already available as *.JLD2 files. In our example we use the data uploaded to
# [https://seafile.rlp.net/d/22b0fb85550240758552/](https://seafile.rlp.net/d/22b0fb85550240758552/)
#
# Specifically, we will use the tomographic models of Paffrath, Zhao and Koulakov, as we have them all available in processed form
# Download the corresponding *.jld2 files to the same directory
using JLD2, GeophysicalModelGenerator
Pwave_Zhao = load("Zhao_Pwave.jld2","Data_set_Zhao2016_Vp")
PSwave_Koulakov = load("Koulakov_Europe.jld2","DataKoulakov_Alps")
Pwave_Paffrath = load("Paffrath_Pwave.jld2","Data_set_Paffrath2021_Vp")
# The 3 data sets all contain tomographic models for roughly the alpine area, but as you can see, they all have a different resolution and the fields are sometimes called different as well:
Pwave_Paffrath
GeoData
size : (162, 130, 42)
lon ϵ [ -13.3019 : 35.3019]
lat ϵ [ 30.7638 : 61.2362]
depth ϵ [ -606.0385 km : 31.0385 km]
fields: (:dVp_Percentage, :Vp, :Resolution)
PSwave_Koulakov
GeoData
size : (108, 81, 35)
lon ϵ [ 4.0 : 20.049999999999997]
lat ϵ [ 37.035928143712574 : 49.01197604790419]
depth ϵ [ -700.0 km : -10.0 km]
fields: (:dVp_percentage, :dVs_percentage)
# #### 2. Creating a votemap
# The idea of `Votemaps` is rather simple:
# - assume that a certain perturbation describes a feature (say, P wave anomalies >3% are interpreted to be slabs in the model of Paffrath)
# - Everything that fulfills this criteria gets the number 1, everything else 0.
# - Do the same with other tomographic models (using the same criteria or a different one).
# - Make sure that all tomographic models are interpolated to the same grid.
# - Sum up the 3 models.
# - The resulting 3D map will have the number `3` at locations where all 3 models see a high-velocity anomaly (say a slab), implying that this feature is consistent between all models. Features that have a number 1 or 2 are only seen in a single or in 2/3 models.
#
# The result of this gives a feeling which features are consistent between the 3 models. It is ofcourse still subjective, as you still have to choose a criteria and as we are assuming in this that the 3 tomographic models are comparable (which they are not as they are produced using different amounts of datam, etc. etc.)
#
# So how do we create Votemaps?
# Doing this is rather simple:
Data_VoteMap = votemap( [Pwave_Paffrath, PSwave_Koulakov, Pwave_Zhao],
["dVp_Percentage>3.0","dVp_percentage>2.0","dVp_Percentage>2.0"], dims=(100,100,100))
# This will look at the common `lon`,`lat`,`depth` ranges between all 3 models, interpret each of the models to a common grid of size `(100,100,100)` and apply each of the criteria specified
# The resulting `GeoData` struct looks like:
GeoData
size : (100, 100, 100)
lon ϵ [ 4.0 : 18.0]
lat ϵ [ 38.0 : 49.01197604790419]
depth ϵ [ -606.0385 km : -10.0 km]
fields: (:votemap,)
# And from this, we can generate profiles, visualize 3D features in Paraview etc. etc.
write_paraview(Data_VoteMap, "VoteMap_Alps")
# In paraview, this gives
# 
# You can ofcourse argue that newer tomographic models include more data & should therefore count more
# A simple way to take that into account is to list the model twice:
Data_VoteMap = votemap( [Pwave_Paffrath, Pwave_Paffrath, PSwave_Koulakov, Pwave_Zhao],
["dVp_Percentage>3.0","dVp_Percentage>3.0", "dVp_percentage>2.0","dVp_Percentage>2.0"],
dims=(100,100,100))
# Similarly, you could only look at a single model (even when that is perhaps easier done directly in paraview, where you can simply select a different isocontour value)