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

1. Quakefinder : A Scalable Data Mining System for detecting Earthquakes from Space A paper by Paul Stolorz and Christopher Dean Presented by, Naresh Baliga

2. 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

3. What does Quakefinder do? • Analyzes the earth’s crustal dynamics • Enables automatic detection and measurement of earthquake faults from satellite imagery

4. 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

5. Inference Engine Purpose: To detect small systematic differences between a pair of images Concept used: Imageodesy, developed by Crippen and Blom

6. Imageodesy Algorithm • Break the before image and after image into many • non-overlapping templates of size, say 100 * 100 pixels • Measure correlation between the before template and • after template • Determine the best template offset from the maximum • correlation value from above • Repeat 2 and 3 at successively higher resolution using • bilinear interpolation to generate new templates offset • by half a pixel in each direction

7. Inferring displacement maps between image pairs

8. Quakefinder Architecture

9. 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

10. 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

11. Satellite Image input for Quakefinder

12. Results for the Lander’s Earthquake

13. 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

14. 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

15. 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

16. 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