Earth quakes - combining physical models with information visualization
Earthquakes are often reported using a single number — magnitude.
But the real impact of an earthquake depends on much more: depth, distance, and how energy spreads through the earth.
This project explores a simple idea:
what happens when we combine physical models with interactive visualization?
Instead of showing raw data, the visualization allows you to shape the model itself — and explore how different assumptions change what we see.
Check out the website: Earth Quakes and source on Github
What this visualization shows
Each earthquake is represented geographically, but its visual appearance is not fixed.
Circle size encodes energy
Outer rings represent felt impact over distance
Depth reduces surface impact
Color and intensity reflect relative strength
Rather than presenting a single “truth”, the system exposes the underlying relationships — allowing comparison across events.
Interaction: exploration and comparison
The key idea is not just to look at earthquakes, but to explore them.
You can:
Adjust how depth attenuates energy
Control how magnitude translates into visual size
Tune how impact spreads geographically
Compare multiple earthquakes under the same assumptions
This turns the visualization into an analytic interface, where the user can test hypotheses:
How much does depth really matter?
Do smaller shallow earthquakes have more local impact than deeper large ones?
How sensitive is our perception to scaling choices?
Technical approach
The visualization combines simplified physical modeling with perceptual encoding.
Energy estimation
Earthquake energy is derived from magnitude using a logarithmic relationship:
E ∝ 10¹·⁵M
This reflects the exponential increase in released energy with magnitude.
Depth attenuation
To approximate how depth reduces surface impact, an attenuation function is applied:
Eₛ = E · e^(−γ · depth)
Where:
γ controls how strongly depth reduces impact
Higher values → deep earthquakes become less visible
Impact radius
The perceived affected area is modeled as a function of energy:
R ∝ √Eₛ
This follows the idea that area grows with energy, while radius grows with the square root.
Visual encoding
Radius → proportional to impact (not raw magnitude)
Outer ring → estimated felt area (in km)
Color → relative comparison within the current view
All mappings are continuous and responsive to parameter changes.
Why this matters
Most visualizations fix the mapping from data to visuals.
Here, the mapping itself becomes interactive.
This allows a shift from:
“What happened?”
to“How do different assumptions change what we see?”
The goal is not just to communicate information,
but to provide a space for exploration, comparison, and understanding.