3-D Earthquake Prediction Models Useful in Earthquake Disaster Planning

Michael Manford McGreer
The earthquake that hit Southern California on Tuesday was one of 378 quakes to hit the region since July 23rd, 2008 and should remind people of the need for continued disaster planning.

The magnitude 5.4 quake shook an area from Los Angeles to San Diego but only caused limited damage and a few injuries. It was felt as far east as Las Vegas.

Dozens of aftershocks followed; the largest a magnitude of 3.8. The 5.4 magnitude shake was the strongest earthquake to strike a populated area of Southern California-Nevada since the 1994 Northridge quake and significantly larger than the many quakes since the 23rd which averaged 1.55 in magnitude (Illustration 1) with an average depth of 7.3 K meters (Illustration 2).Living in an earthquake prone area requires certain precautions in order to mitigate loss of life and property. And planning precautions requires quality information. Fortunately, the U.S. Geological Survey (USGS) is working on 3D computer models that will enable researchers to accurately predict the shaking levels of past and future earthquakes.

Scientists at the USGS and others have found that viewing a 3D structure of the Earth improves the ability of planners to determine the strength of earthquakes at different locations. These models also help in determining the duration of the shaking since seismic waves propagate through different rock types with differing speeds. And rock properties deflect waves and alter velocity contrasts. These effects need to be understood in order to predict the shaking that will be experienced at particular sites.

The ability to produce these maps is based upon a century of geological surface mapping combined with decades of seismic research on rock properties and information collected from boreholes and variations in the Earth's gravity and magnetic fields.

Further researches have determined that understanding the locations of earthquakes relative to faults and rock bodies will yield clues as to the locations of locked patches on the major faults which are the portions of faults most likely to break in future large quakes.

In addition, seismic velocities of rocks and sediments at shallow depths determine how strongly an area will shake thus by assigning velocities to rock types in the 3D model, geologists can better understand areas of low shear velocity which are most likely to experience localized strong shaking and future earthquake damage.

Further, seismic wave velocities at shallow depths (12.5 meters) suggest the susceptibility of the shallow rock units to enhanced shaking and ground failure. In addition low velocity areas are most prone to enhanced shaking. In addition, some areas along the California-Nevada area have shallow water tables and,are also subject to liquefaction.

A typical example of how 3D modeling help can be seen from the collapse of the Cypress Structure, the freeway approach to the Bay Bridge from Oakland, during the 1989 Loma Prieta earthquake. This quake killed 42 people. According to U.S.G.S. Scientists the structure was built on loose soils and therefore shook much more strongly than surrounding regions on stronger ground. In addition, they say, variations in the thickness of the Earth's crust, between the epicenter of the Loma Prieta earthquake in the Santa Cruz Mountains and Oakland, focused energy toward Oakland and downtown San Francisco.

These models have important applications for disaster planners including: a). forecasting strong ground motions that may damage buildings and essential infrastructure or destroy levees or damns, b). predicting where destructive liquefaction of the ground may occur, c). locating earthquakes more accurately, and d). defining the extent of groundwater aquifers and modeling of subsurface transport of contaminants.

Presently the USGS only offers the 3D model of the upper 20 miles of the Earth's crust in the greater San Francisco Bay Area, however, disaster planners in other earthquake zones should consider working with the USGS to build models to their areas of interest.

Sources

U.S. Geological Survey "Earthquake List for Map California-Nevada," at: http://earthquake.usgs.gov/eqcenter/recenteqsus/Maps/special/California_Nevada_eqs.php

Nakata, John K, Meyer, Charles E., Wilshire, Howeard, G., Tinsley, John C., Updegrove, D.M., Peterson, Stephen D., Ellen, Stephen D., Haugerud, Ralph A., McLaughlin, Robert J., Fisher, Reid G.m and Diggles, Michel F., "Loma Prieta, California, Earthquake," at: http://pubs.usgs.gov/dds/dds-29/

U.S. Geological Survey, "3D Geologic and Seismic Velocity Model of the San Francisco Bay Region," at: http://earthquake.usgs.gov/regional/nca/3Dgeologic/index.php

Dr. McGreer served as a Computer Scientist with the U.S. Department of Interior and U.S. Geological Survey from 1997 to 2006.

Published by Michael Manford McGreer

Michael M. McGreer, Ph.D, writes and lectures on issues of historical or contemporary interest to political decision makers and people wishing to survive the consequences of public policy.  View profile

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378 Earthquakes hit California-Nevada since Jy7ly 23rd, 2008

3 Comments

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  • geo6/23/2009

    (one more try )
    There has been a lot more success in the past few years using many sensors to identify earth characteristics occurring before an earthquake. Many of those sensors are displayed at http://www.electricquakes.org

    www.electricquakes.org

    www.electricquakes.org

    Some of those may eventually be useful for meaningful earthquake prediction.

  • geo6/23/2009

    Guess that link didn't take for some reason.

    Here's one more try to the earthquake data precursor page

    www.electricquakes.org

  • Geo6/23/2009

    There has been a lot more success in the past few years using many sensors to identify earth characteristics occurring before an earthquake.


    Many of those sensors are displayed at

    http://www.electricquakes.org

    "http://www.electricquakes.org/">www.electricquakes.org

    Some of those may eventually be useful for meaningful earthquake prediction.

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