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 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

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