Structural Knowledge Discovery Used to Analyze Earthquake Activity

Jesus A. GonzalezLawrence B. HolderDiane J. Cook

MOTIVATION AND GOAL

Need to analyze large amounts of information in

real world databases.

Information that standard tools can not detect.

Earthquake Database.

Previous knowledge: Spatio-Temporal relations.

SUBDUE KNOWLEDGE DISCOVERY SYSTEM

SUBDUE discovers patterns (substructures) in structural data sets.

SUBDUE represents data as a labeled graph.

Inputs: Vertices and Edges.

Outputs: Discovered patterns and instances.

EXAMPLE

objecttriangle

object

squareon

shape

shape

Vertices: objects or attributesEdges: relationships

4 instances of

EVALUATION CRITERION

Minimum Encoding.

Graph Compression.

Substructure Size (Tried but did not work).

EVALUATION CRITERIONMINIMUM DESCRIPTION LENGTH

Minimum Description Length (MDL) principle. The best theory to describe a set of data is the one that minimizes the DL of the entire data set.

DL of the graph: the number of bits necessary to completely describe the graph.

Search for the substructure that results in the maximum compression.

THE EARTHQUAKE DATABASE

Several catalogs.

Sources like the National Geophysical Data Center.

Each record with 35 fields describing the earthquake characteristics.

THE EARTHQUAKE DATABASEKNOWLEDGE REPRESENTATION

EVENT 2

EVENT 1

EVENT 3

EVENT m

PDE_W

1998

01

4.5

Near_in_distance

Near_in_time

Category

Year

Month

Magnitude

THE EARTHQUAKE DATABASEPRIOR KNOWLEDGE

Connections between events where its epicenters were close to each other in distance (<= 75 kilometers).

Connections between events that happened close to each other in time (<= 36 hours).

Spatio-Temporal relations represented with “near_in_distance” and “near_in_time” edges.

Geologist Dr. Burke Burkart. Study of seismology caused by the Orizaba Fault. Fault: A fracture in a surface where a displacement of

rocks also happened. Selection of the area of study, two squares:

First Longitude 94.0W through 101.0W and Latitude

17.0N through 18.0N. Second Longitude 94.0W through 98.0W and Latitude

18.0N through 19.0N.

DETERMINING EARTHQUAKE ACTIVITY

DETERMINING EARTHQUAKE ACTIVITY

Area of Study

DETERMINING EARTHQUAKE ACTIVITY

Divide the area in 44 rectangles of one half of a degree in

both longitude and latitude.

Sample the earthquake activity in each sub-area.

Run Subdue in each sub-area.

DETERMINING EARTHQUAKE ACTIVITY

Area CoordinatesAreaNumber

Latitude Longitude

AreaName

Number ofEvents

1 101.0W 100.5W 17.0N 17.5N Gue1 622 101.0W 100.5W 17.5N 18.0N Gue2 403 100.5W 100.0W 17.0N 17.5N Gue3 574 100.5W 100.0W 17.5N 18.0N Gue4 135 100.0W 99.5W 17.0N 17.5N Gue5 716 100.0W 99.5W 17.5N 18.0N Gue6 157 99.5W 99.0W 17.0N 17.5N Gue7 358 99.5W 99.0W 17.5N 18.0N Gue8 169 99.0W 98.5W 17.0N 17.5N Gue9 1310 99.0W 98.5W 17.5N 18.0N Gue10 14

26 95.0W 94.5W 17.5N 18.0N Ver1 4327 94.5W 94.0W 17.0N 17.5N Oaxver4 3528 94.5W 94.0W 17.5N 18.0N Ver2 2329 98.0W 97.5W 18.0N 18.5N Pue1 630 98.0W 97.5W 18.5N 19.0N Pue2 0

42 95.0W 94.5W 18.5N 19.0N Vergolf5 143 94.5W 94.0W 18.0N 18.5N Vergolf4 344 94.5W 94.0W 18.5N 19.0N Vergolf6 1

DETERMINING EARTHQUAKE ACTIVITY

33.00

Substructure 2, 8 instances.

Sub_1

N %

Depth Dept_ctl Coord_qual..

PDE

Substructure 1, 19 instances.

Event EventNear_in_distance

Category

PDE

Category

61.00 61.00

Region_numberRegion_number

Substructure 1 (with 19 instances) and substructure 2 (with

8 instances) found in sub-area 26.

DETERMINING EARTHQUAKE ACTIVITY

This pattern might give us information about the cause of

the earthquakes.

Subduction also affects this area but it affects at a specific

depth according to the closeness to the Pacific Ocean.

SUBDUE’S POTENTIAL

Subdue finds not only shared characteristics of events, but

also space relations between them. Dr. Burke Burkart is studying the patterns to give direction

to this research. Expect to find patterns representing parts of the paths of

the involved fault. Time relations not considered by Subdue.

Earthquake’s characteristics. Important for other areas.

CONCLUSION

Subdue successful in real world databases. Subdue used prior knowledge to guide search with

temporal and spatial relations. Subdue discovered interesting patterns using these

temporal and spatial relations. Subdue is being used as the data mining tool to study the

“Orizaba Fault” in Mexico.

FUTURE WORK

Concept Learning Subdue

Theoretical analysis.

Bounds on complexity (e.g. PAC learning).

Graphic User Interface to visualize substructures and their

instances.