Geophysical Modeling


There are several generic types of physical property distributions commonly considered when interpreting geophysical data. Models are invariably simpler than the real Earth, and the degree of simplification depends upon the geometry used for data acquisition (single points, lines, random locations, etc), as well as on the methods employed for interpretation. There are some common generic types of geophysical models:

  • Uniform Halfspace  A “uniform halfspace” means the earth beneath the surface has the same physical property value for as far as the measurement system can detect. A “uniform wholespace” refers to a volume with the same physical property above, below and all around, as might be encountered by an instrument in a borehole.
  • Buried Objects  When buried objects are the focus, the earth is usually considered to be uniform all around the object. The object itself may be represented with a more or less complicated set of parameters
  • 1D Layered Models  The physical property is assumed to vary in only one direction (usually depth).
  • 2D Models The physical property is assumed to vary in only two directions (usually depth, and the direction parallel to a survey line), each of whic has a constant value of physical property. There are two variants of the 1D model: (i) layer thicknesses can be fixed and we try to find only the values of the property, or (ii) we fix the number of layers and try to find values for the property and layer thicknesses. Surveys that are designed to yield 1D results are often called soundings.
  • 3D Models  The physical property varies in all three directions.

[SOURCE:  UBC Earth and Ocean Sciences  copyrighted]


Research excerpts from CCS collaborators studying Geophysical Modeling:

  1. Earthquake Cycle Effects in the Eastern California Shear Zone by Gina M. Schmalzle and Timothy H. Dixon
  2. Studying Volcanoes and Earthquakes using Interferometric Synthetic Aperture Radar (InSAR) by Scott Baker and Falk Amelung


1. Earthquake Cycle Effects in the Eastern California Shear Zone by Gina M. Schmalzle and Timothy H. Dixon

We use the 3 dimensional finite element program G-TECTON to study earthquake cycle effects in the Eastern California Shear zone (ECSZ). The model shown below encompasses the major faults in the region: the San Andreas (SAF), Owens Valley (OV), Panamint Valley (PV) and Death Valley faults (DV). Also included in this model are the Deep Springs (DS) and Eureka (EU) faults. The faults separate major tectonic blocks known as the Basin and Range and Sierra Nevada Blocks and the Pacific plate. The model includes an elastic layer that is coupled, or welded, to the underlying viscoelastic layer. We compare GPS data that has been processed at the University of Miami Geodesy Lab to the model, and interpret fault rate, earthquake recurrence, and other fault and material properties. Studies such as this one are important in determining seismic hazard.

Figure 16: Panel A displays the initial conditions of the model. Red arrows represent boundary conditions and point in the fixed model directions. For example, the bottom of the model cannot move vertically, so the red arrows point up. The green arrow represents the direction of overall plate motion. Panel B is an example result of the velocity field for this model. Warmer colors represent faster rates. Surface fault traces for both figures are displayed as black lines.



2. Studying Volcanoes and Earthquakes using Interferometric Synthetic Aperture Radar (InSAR) by Scott Baker and Falk Amelung

We study active volcanoes and earthquakes using a remote-sensing technique called Interferometric Synthetic Aperture Radar (InSAR). In this technique, multiple satellite radar images (from the same area on the Earth) are combined to precisely measure surface displacements, such as due to subsurface magma migration and fault movements. At volcanoes, interpretation of the detected deformation in terms of magma chamber models and magma pathways leads to a better understanding of how, when, and why volcanoes erupt. The measurement accuracy increases with the number of available satellite images. The simultaneous analysis of many radar images (for some volcanoes we have more than 100 images) is, computationally, very challenging. Inflation of Mauna Loa volcano, Hawaii, during 2002-2005, measured with satellite radar interferometry is shown in Figure 17.

Figure 17: Inflation of Mauna Loa volcano, Hawaii, during 2002-2005 measured with satellite radar interferometry. One color cycle corresponds to 3 cm change in distance between the Earth’s surface and the satellite. The image shows that up to 20 cm ground deformation occurred during this time period, caused by the intrusion of new magma into the volcano’s interior. Credit: Scott Baker and Falk Amelung.



MASTHEAD IMAGE SOURCE: Western Australian Geodesy Group, Curtin University. Used with permission from: Hirt, Christian. et al (2013), New ultra-high resolution picture of Earth’s gravity field, Geophysical Research Letters, 40(16), 4279-4283, doi: 10.1002/grl.50838.

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