Observational Seismology

(Machine Learning Seismology)

Large destructive earthquakes

Hayward Fault

History

The California Memorial Stadium

An estimated magnitude of 6.3 or greater.

Many more small earthquakes

• California
• Alaska
• Hawaii
• Oklahoma & Texas

Seismic Networks

• California
• Alaska
• Hawaii
• Oklahoma & Texas

GPS Networks

Large-N and Large-T challenge

IRIS dataset

Mining the IRIS dataset

What information can we get from seismic data?

• Take a look at seismic waveforms:
  ncedc.org/waveformDisplay/

Station Channel Codes

Station Channels Codes IRIS

• Can you find an earthquake?

Raspberry Shake Network

What information can we get from seismic data?

• Take a look at a recent earthquake: M 5.1 - 7 km SE of Ojai, CA
  

How are information extracted/determined?

• Detection of earthquakes
• Earthquake origin time and location
• Earthquake magnitude
• Earthquake focal mechanism/moment tensor
• Shake map/ground motion prediction
• Earthquake early warning
• "Did you feel it?"

What additional information can we get from millions of earthquakes?

• Earthquake catalog
• Earthquake statistics
• Earthquake triggering
• Earthquake forecasting
• Fault zone structure
• Seismic tomography
• Volcano, glacier, and landslide monitoring

How to use these information?

• Monitoring earthquakes and earthquake early warning
• Understand earthquake source physics
• Understanding the Earth's structure
• Applying seismology to environmental science, planetary science, climate science, etc.

Earthquake monitoring and earthquake rick?

• Before an earthquake
• A few seconds after an earthquake
• Hours/days after an earthquake
• Years after an earthquake

Before an earthquake

• Eathquake Hazard Map
  

• Simulating earthquake scenarios
  Hayward Fault Scenarios
  

A few seconds after an earthquake

• MyShake
  https://myshake.berkeley.edu/

• Mobile phones as seismometers
  Android EEW
  

Hours/days after an earthquake

• Emergency response and damage assessment
  Fault Dimensions

• Aftershock prediction

Years after an earthquake

• Understand earthquake rupture process
• Improve ground motion prediction models (GMPE)
• Improve hazard map and building codes
• Earthquake forecasting models
  

How can we better monitor earthquakes?

Instrument side
(How to collect more and better data?)

• Dense seismic networks
• New sensors: broadband seismometer, nodal array, and DAS (Distributed Acoustic Sensing)
• Remote sensing, LiDAR, etc.

How can we better monitor earthquakes?

Algorithm side
(New techniques for processing data and extracting information?)

• Many signal processing algorithms, such as, STA/LTA, template matching, filtering, etc.

• Machine learning & deep learning

• Numerical simulation

• Inverse theory

• Statistical analysis

Things to learn in this course

• Faimilar with seismic data
• Learn the state-of-the-art machine learning methods for seismic data processing
• Process seismic data, build seismic catalogs, and analyzing seismicity
• Learn basic inverse theory for earthquake location, focal mechanism, seismic tomography, etc.

The advantages of machine learning

Deep Learning (Deep Neural Networks) is a new paradigm of software development

• Software 2.0

• Universal Approximation Theorem

Applications of deep learning in seismology

• Neural Networks
• Automatic Differentiation
• Optimization/Inversion

Machine Learning and Inversion

Grading

• Attendance and participation (50%)
• Final project (50%)
  • Project proposal (10%)
  • Project presentation (20%)
  • Project report (20%)
• Extra credit (up to 10%)

Questions?