Microseism and Noise Source Mapping

ISEF Category: Physics and Astronomy

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

The ground is never really quiet. Oceans, storms, and traffic all leave tiny fingerprints in seismic noise. With the right tools, you can turn that background hum into a map of what is happening far away. That means your project can link Earth physics, weather, and city activity in one dataset.

What Is It?

Microseisms are small vibrations in the ground, often caused by ocean waves. Think of the Earth like a drum skin. When waves crash and interact, they shake the seafloor, and that energy travels through the ground as a low, steady signal. Urban noise works the same way in a different setting, with cars, buses, and trains creating patterns that show up in seismic records.

Source-mechanism inversion is the part that sounds fancy but follows a simple idea. You measure the signal at one or more locations, then ask what source pattern best explains it. In this project, you are not just recording noise. You are trying to connect the shape of the signal to possible source locations, like storm tracks over the ocean or traffic corridors near your city.

Why This Is a Good Topic

This is a strong science fair topic because it gives you a real signal, public comparison data, and room for original analysis. You can test whether seismic noise changes with weather or traffic patterns, then compare your results against NOAA and traffic sensor records. The project teaches you signal processing, data cleaning, and model fitting, which are useful skills in geophysics and data science.

Research Questions

• How does ocean wave activity reported by NOAA relate to low-frequency microseism power at your sensor site?
• What is the effect of nearby traffic volume on daytime versus nighttime seismic noise spectra?
• Does sensor distance from a highway change the strength of urban-noise peaks in the frequency domain?
• To what extent can Bayesian inversion identify the most likely source region of observed microseism signals?
• Which frequency bands best separate storm-driven microseisms from traffic-driven ground noise?
• How does weather type, such as calm, windy, or stormy conditions, affect the stability of the inferred source pattern?

Basic Materials

• Geophone or phone seismometer setup with a stable mounting method.
• Tripod, clamp, or ground-coupled base to hold the sensor steady.
• Laptop or tablet for data download and analysis.
• Notebook or spreadsheet for logging time, location, and weather.
• Access to public NOAA weather and wave data.
• Access to public traffic count or traffic speed data.
• Quiet outdoor test site and, if possible, a second site near traffic.

Advanced Materials

• Geophones with a data logger or digitizer.
• Raspberry Shake or similar seismic sensor access.
• Reference accelerometer for cross-checking sensor response.
• GPS timing source for synchronization across stations.
• Local traffic sensor feed or archived transportation dataset.
• NOAA wave buoy records and storm track archives.
• High-performance laptop or access to a university computing cluster for Bayesian modeling.

Software & Tools

• Python: Cleans seismic time series, computes spectra, and runs statistical models.
• ObsPy: Handles seismic data formats and time-series processing.
• NumPy and SciPy: Support filtering, spectral analysis, and curve fitting.
• Matplotlib: Plots frequency bands, source comparisons, and model outputs.
• ImageJ: Can help inspect screenshots or calibration images if you document sensor setup.

Experiment Steps

1. Define the source type you want to separate first, such as ocean microseisms, road traffic, or both.
2. Choose sensor locations that give you a clear contrast in distance from coastlines, highways, or dense streets.
3. Plan your data stream so every record can be matched to public NOAA, weather, and traffic data by time.
4. Build a frequency-based comparison method that turns raw vibration records into interpretable spectra.
5. Set up control cases that help you separate true source signals from wind, building vibration, or sensor drift.
6. Design a Bayesian model that links observed signal patterns to candidate source regions, then test how well it matches known events.

Common Pitfalls

• Mounting the sensor loosely, which adds building vibration that masks the signal you want.
• Comparing records from different times without matching weather, tides, or traffic conditions.
• Using raw amplitude alone, which misses the frequency patterns that distinguish ocean noise from urban noise.
• Ignoring instrument response, which can make one sensor look stronger or weaker than another for the wrong reason.
• Treating nearby construction or wind as ocean microseisms, which leads to a false source estimate.

What Makes This Competitive

A strong version of this project goes beyond simple noise plots. You would separate source types with clean controls, compare your inference against real NOAA and traffic records, and test how often the model gets the source region right. You could also compare two sensor types, or two neighborhoods, and show where the method works best and where it fails. That kind of careful validation makes the project feel like real geophysics, not just a data demo.

Project Variations

• Compare coastal and inland sites to see how storm-driven microseisms weaken with distance from the ocean.
• Test whether daytime and nighttime traffic produce different spectral signatures in a suburban area.
• Use two sensors at different distances from the same highway to estimate how fast urban noise decays with space.

Learn More

• ObsPy Documentation: Free tools and examples for seismic data analysis, found by searching for the ObsPy project documentation.
• IRIS Education and Outreach: Introductory seismology lessons and waveform tools, found on the IRIS website.
• NOAA National Data Buoy Center: Public wave, wind, and storm data for connecting ocean conditions to microseisms, found on NOAA's site.
• USGS Earthquake Hazards Program: Background on seismic waves, instruments, and noise records, found on the USGS site.
• PubMed: Search for review articles on microseisms, ambient seismic noise, and source inversion.
• NASA Earthdata: Access to weather and ocean-related datasets that can help with context and comparison, found on NASA Earthdata.