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Quakefinder: Scalable Earthquake Detection from Space

This paper by Paul Stolorz and Christopher Dean, presented by Naresh Baliga, introduces Quakefinder, a system that analyzes the earth's crustal dynamics and automatically detects earthquake faults from satellite imagery. It addresses the design of a statistical inference engine and implements scalable algorithms for massive datasets. Quakefinder's Imageodesy Algorithm breaks images into templates for correlation measurement and displacement map inference. The architecture involves an adaptive learning process with an E-step and M-step. Implemented on a Cray T3D at JPL, Quakefinder provides useful scientific products. Advantages include application to temporal events and tectonic processes, while limitations involve manual verification by geologists. Future directions involve applications in Europa, global climate monitoring, and Mars dune activities.

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Quakefinder: Scalable Earthquake Detection from Space

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  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

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