HR: 15:35h
AN: S34A-01 INVITED     [Abstracts]
TI: Graph Theory for Seismic Analyses
AU: * Eisner, L
EM: leisner@cambridge.oilfield.slb.com
AF: Schlumberger Cambridge Research, High Cross, Madingley Road, Cambridge, CB42HJ United Kingdom
AU: Arrowsmith, S
EM: sarrowsm@popmail.ucsd.edu
AF: Scripps Institution of Oceanography, 9500 Gilman Drive, La Jolla, CA 92093-0225 United States
AU: Le Calvez, J
EM: JCalvez2@college-station.oilfield.slb.com
AF: Schlumberger, College Station, College Station, TX 77845 United States
AU: Rutledge, J
EM: jrutledge@lanl.gov
AF: Los Alamos NL, Geophysics Group, MS D443, Los Alamos, NM 87545 United States
AB: The interpretation of seismic events is often as challenging as their acquisition. To visualize and effectively analyze the seismic datasets, we propose to represent the data on a graph in which the individual events are represented as nodes and the physical relations between the events are represented as edges. For example, one physical relationship could be a measure of the waveform similarity; i.e., doublets. This representation allows us to use the numerous graph theory algorithms already existing to quickly quantify complex relationships. To illustrate, doublets are grouped into multiplets using a simple search for the connected parts of a graph. Depending on which physical relationship will be analyzed, the weighting function can be generalized to represent other attributes, such as distance between hypocenters or relative source parameters. The graphs may also be oriented (e.g., a delay between origin times may determine the orientation of the edges). As an example of an attribute quantifying the relationship between microseismic events, we use the similarity of the waveforms representing doublets. The graphic representation facilitates the interpretation because the datasets often include large numbers of similar events originating on the same fault and producing similar waveforms. The grouping of doublets into multiplets illustrates the mutual interactions among different fault planes and simplifies the complexity of the analysis. For example, if separate multiplets with a high correlation threshold become part of a single multiplet with a lower correlation threshold, we can conclude that these multiplets are part of one large fault system.
DE: 0902 Computational methods, seismic
DE: 0915 Downhole methods
DE: 0935 Seismic methods (3025)
DE: 7230 Seismicity and seismotectonics
DE: 7260 Theory and modeling
SC: Seismology [S]
MN: 2005 Joint Assembly