Quakefinder : A Scalable Data Mining System for detecting Earthquakes from Space

A paper byPaul Stolorz and Christopher Dean

Presented by,Naresh Baliga

Presentation Flow• Introduction to Quakefinder

• Quakefinder’s Inference Engine

• Imageodesy Algorithm

• Quakefinder Architecture

• Implementation Details

• Results for Lander’s Earthquake

• Advantages and Disadvantages

• Conclusions and Future Directions

• References

What does Quakefinder do?

• Analyzes the earth’s crustal dynamics

• Enables automatic detection and measurement of earthquake faults from satellite imagery

Problems that Quakefinder addresses:

• Design of a statistical inference engine that can reliably infer the fundamental processes to acceptable precision

• Development and Implementation of scalable algorithms for massive datasets

• A system that performs that performs all the computations involved automatically and presents scientists with useful scientific products

Inference Engine

Purpose: To detect small systematic differences between

a pair of images

Concept used: Imageodesy, developed by Crippen and Blom

Imageodesy Algorithm

1. Break the before image and after image into many

non-overlapping templates of size, say 100 * 100 pixels

2. Measure correlation between the before template and

after template

3. Determine the best template offset from the maximum

correlation value from above

4. Repeat 2 and 3 at successively higher resolution using

bilinear interpolation to generate new templates offset

by half a pixel in each direction

Inferring displacement maps between image pairs

Quakefinder Architecture

Adaptive Learning

•The E-step evaluates a probability distribution for the data given the model parameters from the previous iteration•The M step then finds the new parameter set that maximizes the probability distribution

•E-step: Redefine the sizes and shapes of those templates that overlap the estimated fault. •M-step: Recompute the displacement map with updated template parameters

Implementation Details

• Quakefinder is implemented on a 256-node Cray T3D at JPL

• Each of the 256 computing nodes are based on a DEC Alpha processor running at

150MHz

• The nodes are arranged as a 3-dimensional tori, allowing each node to communicate with up to 6 nodes

Satellite Image input for Quakefinder

Results for the Lander’s Earthquake

Advantages

• Quakefinder is one of the first kind of data mining

systems to be applied to temporal events in nature

• Fulfilled the necessity of area-mapped information about 2D tectonic processes

• Can be used as a component in other data mining systems. E.g. SKICAT

Disadvantages

• Is not completely automated, still requires a geologist to determine whether results are accurate enough• Geometric corrections are assumed to be negligible

Future Directions

• Being applied to detect subtle motions on Europa• Can be applied to monitoring global climate changes and natural hazard monitoring• Can be applied to detect sand-dune activities on Mars

References

• mishkin.jpl.nasa.gov/spacemicro/SCALABLE_PAPER

•www-aig.jpl.nasa.gov/public/mls/quakefinder/

•www.cacr.caltech.edu/Publications/annreps/annrep97/space.html

•www-aig.jpl.nasa.gov/public/mls/news/sf_examiner_article.html

Tidbits

•Early Warning Systems for detecting Earthquakes www-ep.es.llnl.gov/www-ep/ghp/signal-process/web_p1.html

•Earthquake Prediction: Science on shaky ground? www.the-scientist.library.upenn.edu/yr1992/july/research_920706.html