Unit 4: The phenomenology of earthquakes from InSAR data
These materials have been reviewed for their alignment with the Next Generation Science Standards as detailed below. Visit InTeGrate and the NGSS to learn more.
OverviewStudents use models to simulate interactions between surface displacement and landscapes. They quantify fault geometry using InSAR data, relating different degrees of variability in source parameters to different results.
Science and Engineering Practices
Planning and Carrying Out Investigations: Plan an investigation or test a design individually and collaboratively to produce data to serve as the basis for evidence as part of building and revising models, supporting explanations for phenomena, or testing solutions to problems. Consider possible confounding variables or effects and evaluate the investigation’s design to ensure variables are controlled. HS-P3.1:
Planning and Carrying Out Investigations: Manipulate variables and collect data about a complex model of a proposed process or system to identify failure points or improve performance relative to criteria for success or other variables. HS-P3.6:
Planning and Carrying Out Investigations: Make directional hypotheses that specify what happens to a dependent variable when an independent variable is manipulated. HS-P3.5:
Developing and Using Models: Evaluate merits and limitations of two different models of the same proposed tool, process, mechanism or system in order to select or revise a model that best fits the evidence or design criteria. HS-P2.1:
Developing and Using Models: Develop, revise, and/or use a model based on evidence to illustrate and/or predict the relationships between systems or between components of a system HS-P2.3:
Developing and Using Models: Develop and/or use a model (including mathematical and computational) to generate data to support explanations, predict phenomena, analyze systems, and/or solve problems. HS-P2.6:
Developing and Using Models: Develop a complex model that allows for manipulation and testing of a proposed process or system. HS-P2.5:
Developing and Using Models: Design a test of a model to ascertain its reliability. HS-P2.2:
Constructing Explanations and Designing Solutions: Make a quantitative and/or qualitative claim regarding the relationship between dependent and independent variables. HS-P6.1:
Constructing Explanations and Designing Solutions: Construct and revise an explanation based on valid and reliable evidence obtained from a variety of sources (including students’ own investigations, models, theories, simulations, peer review) and the assumption that theories and laws that describe the natural world operate today as they did in the past and will continue to do so in the future. HS-P6.2:
Constructing Explanations and Designing Solutions: Apply scientific reasoning, theory, and/or models to link evidence to the claims to assess the extent to which the reasoning and data support the explanation or conclusion. HS-P6.4:
Asking Questions and Defining Problems: ask questions that arise from examining models or a theory, to clarify and/or seek additional information and relationships. HS-P1.2:
Analyzing and Interpreting Data: Consider limitations of data analysis (e.g., measurement error, sample selection) when analyzing and interpreting data HS-P4.3:
Analyzing and Interpreting Data: Compare and contrast various types of data sets (e.g., self-generated, archival) to examine consistency of measurements and observations. HS-P4.4:
Analyzing and Interpreting Data: Analyze data using tools, technologies, and/or models (e.g., computational, mathematical) in order to make valid and reliable scientific claims or determine an optimal design solution. HS-P4.1:
Cross Cutting Concepts
Systems and System Models: Models can be used to predict the behavior of a system, but these predictions have limited precision and reliability due to the assumptions and approximations inherent in models. HS-C4.4:
Systems and System Models: Models (e.g., physical, mathematical, computer models) can be used to simulate systems and interactions—including energy, matter, and information flows—within and between systems at different scales. HS-C4.3:
Stability and Change: Change and rates of change can be quantified and modeled over very short or very long periods of time. Some system changes are irreversible. HS-C7.2:
Patterns: Empirical evidence is needed to identify patterns. HS-C1.5:
Patterns: Different patterns may be observed at each of the scales at which a system is studied and can provide evidence for causality in explanations of phenomena HS-C1.1:
Disciplinary Core Ideas
Information Technologies and Instrumentation: Multiple technologies based on the understanding of waves and their interactions with matter are part of everyday experiences in the modern world (e.g., medical imaging, communications, scanners) and in scientific research. They are essential tools for producing, transmitting, and capturing signals and for storing and interpreting the information contained in them HS-PS4.C1:
Waves and their Applications in Technologies for Information Transfer: Communicate technical information about how some technological devices use the principles of wave behavior and wave interactions with matter to transmit and capture information and energy HS-PS4-5:
Earth's Systems: Analyze geoscience data to make the claim that one change to Earth's surface can create feedbacks that cause changes to other Earth systems. HS-ESS2-2:
This material was developed and reviewed through the GETSI curricular materials development process. This rigorous, structured process includes:
- team-based development to ensure materials are appropriate across multiple educational settings.
- multiple iterative reviews and feedback cycles through the course of material development with input to the authoring team from both project editors and an external assessment team.
- real in-class or field camp/course testing of materials in multiple courses with external review of student assessment data.
- multiple reviews to ensure the materials meet the GETSI materials rubric which codifies best practices in curricular development, student assessment and pedagogic techniques.
- created or reviewed by content experts for accuracy of the science content.
This activity was selected for the On the Cutting Edge Reviewed Teaching Collection
This activity has received positive reviews in a peer review process involving five review categories. The five categories included in the process are
- Scientific Accuracy
- Alignment of Learning Goals, Activities, and Assessments
- Pedagogic Effectiveness
- Robustness (usability and dependability of all components)
- Completeness of the ActivitySheet web page
For more information about the peer review process itself, please see http://serc.carleton.edu/NAGTWorkshops/review.html.
This page first made public: Dec 14, 2015
How are different types of earthquakes represented in InSAR data? How can we obtain detailed information on the earthquake source from InSAR data? How well can we resolve those details? In this unit, students investigate how simple elastic dislocation models can be matched to interferograms of earthquakes, and the various geometrical and surficial factors that can affect that process.
Note: Nov 1, 2019 - it was just brought to our attention that the Visible Earthquake InSAR modeling tool that is used in this unit has gone offline. We are working with the partner company (which was apparently bought by another company) in order to try to rectify this situation. Sorry for any inconvenience.
Unit 4 Learning Outcomes
- Students will depict the relationship between earthquake source parameters and fault geometry.
- Students will differentiate between coseismic deformation from a single earthquake and the long-term signature of multiple earthquake cycles that is recorded in the landscape.
- Students will model coseismic deformation of an earthquake captured by InSAR data, in order to obtain earthquake source parameters.
- Students will relate different coseismic deformation patterns to different faulting styles and orientations.
- Students will relate different degrees of variability in source parameters from different contributed results to the intrinsic uncertainties in those parameters and the non-uniqueness of the results.
Unit 4 Teaching Objectives
- Cognitive: Promote student ability to understand the relationship between fault geometry/earthquake source parameters and surface displacement, as measured with InSAR. Enable exploration of uncertainty in model results and its possible causes.
- Behavioral: Facilitate development of skills in data-fitting and pattern matching.
Context for Use
Description and Teaching Materials
This unit is a practical exercise that requires access to computers. After some reinforcement of the terminology of earthquake source parameters and some exploration of concepts of the earthquake cycle and elastic rebound, students use the Visible Earthquakes tool to model InSAR data of at least two earthquakes. The whole class will model the same event (a normal faulting earthquake from Turkey), and then a variety of other events of other faulting styles and orientations can be modeled by subsets of the class as a jigsaw exercise. This will facilitate student exploration and/or class discussion of two topics: 1) uncertainty in earthquake source parameters, and the potential causes of it; and 2) how faulting style and orientation affect the deformation pattern that InSAR records.
IF you are planning to use either of the two suggested case study earthquakes of El Major Cucapah and South Napa for the Unit 5: How do earthquakes affect society? summative assessment, than it is recommended that you steer students away from choosing those for their second earthquake in Unit 4.
Visible Earthquakes - this interactive web tool forms the basis of most of the activities in the unit.Unit 4 First Motion Background Presentation (PowerPoint 2007 (.pptx) 6.4MB Dec11 15)
Teaching Notes and Tips
You may choose to use this file just for your own reference or to present portions of it to the students after they have already worked through the exercise.
1) Take a little time experimenting with and becoming familiar with the Visible Earthquakes tool before starting to teach Unit 4. Visible Earthquake has a good Getting Started page that overviews the online tool function as well as the basics of faulting and InSAR, thus serving as a useful reference for students.
2) When assessing a student's attempt at modeling a given earthquake interferogram, consider the following:
- Are the largest positive and negative deformation signals approximately matched in terms of their amplitudes?
- In the residual view, the amplitude of any remaining signal should be small.
- In the wrapped interferogram view, the numbers of fringes should be similar on both sides of the fault.
- Does the modeled deformation pattern have a similar spatial extent to that seen in the data?
- Use the ruler tool to measure lengths and widths, if necessary.
- Is the modeled deformation pattern in a similar location to that seen in the data?
- Again, the ruler tool can be helpful to make comparisons between positions.
- Gaps or holes in the data can also be used to assess position.
- Very large localized residuals can sometimes indicate a mislocated fault (mapping positive deformation on top of negative, for instance, would cause a large residual in the area of overlap).
3) For the jigsaw portion of the exercise, pick a few different earthquakes from the selection available with different mechanisms and orientations, and divide them among the students.
- It will help to have students working on reverse faulting earthquakes and strike-slip faults with different strikes (N-S vs E-W).
- Reverse faulting earthquakes are very similar to model to normal faults (except for the reversed sense of motion and rake).
- All dip-slip earthquakes have reasonably simple deformation patterns in general, regardless of fault orientation, as they mostly cause vertical displacements of the surface, which InSAR is very sensitive to.
- N-S and E-W strike-slip faults are very different in their deformation patterns, as satellite-based InSAR is very insensitive to N-S displacements, but moderately sensitive to E-W displacements.
- These similarities and differences can be highlighted during the report out following the jigsaw exercise.
4) Comparing the source parameters across the class is a powerful way to bring up uncertainty in scientific findings. The plotting of histograms of the source parameters from students' approved models can work a couple of different ways:
- The values they write on their handouts can be collated as a class and plotted by the students themselves, either on their computers, or by hand. (This may be useful if you want to emphasize histogram plotting as a skill.)
- A quicker way to see the histograms is available within the Visible Earthquakes tool. The models they submit to the Visible Earthquakes database can be viewed within the web tool.
- In this case, it is imperative that all student submit their models under the same group name (otherwise, the results will not appear under the same group name within the tool). The names are case sensitive! A quick overview of the submission process is also given in the Visible Earthquake's Getting Started page.
- Make the group name unique and easy to spell (all one case, maybe one word, consider adding a date), and give it to the students in advance.
- Once an event has been modeled, a "Results" button will appear next to it on the main Visible Earthquakes page. Clicking on this will take you through to the histograms for each fault parameter. By default, all results from all submissions are shown.
- To see just the results from your group, click the "none" link toward the top right of the window, and then click on your group name.
- The buttons below the histogram allow you to choose which parameter (strike, dip, length, etc) is displayed on the histogram.
The rubric can be used to guide grading of the assignment. Unit 4 Grading Rubric (Microsoft Word 2007 (.docx) 107kB Dec1 15)
References and Resources
Primer on Focal Mechanism Solutions for Geologists by Vince Cronin
The following citations provide source parameter information for a selection of the earthquakes available on the Visible Earthquakes InSAR Tool.
- Aiquile Earthquake, Bolivia 1994
- Funning, G., et al., 2005, The 1998 Aiquile, Bolivia earthquake: A seismically active fault revealed with InSAR, EPSL, 232, 39-49.
- Dinar, Turkey 1995
- Eyidoǧan, H. and A. Barka, The 1 October 1995 Dinar earthquake, SW Turkey, Terra Motae, 8, 5, 479–485.
- T.J. Wright, B.E. Parsons, J.A. Jackson, M. Haynes, E.J. Fielding, P.C. England, and P.J. Clarke, 1999, Source parameters of the 1 October 1995 Dinar (Turkey) earthquake from SAR interferometry and seismic bodywave modelling. Earth and Planetary Science Letters, 172, 1-2, 23–37.
- Damxung, China 2008
- Bie, L., Ryder, I., Nippress, S.E.J.k and R. Bürgmann, 2014, Coseismic and post-seismic activity associated with the 2008 Mw 6.3 Damxung earthquake, Tibet, constrained by InSAR. Geophysics Journal International, 196, 2, 788-803.
- Qiao, X., Yang, S., Du, R., Ge, L., and Q. Wang, 2011, Coseismic Slip from the 6 October 2008, M w6.3 Damxung Earthquake, Tibetan Plateau, Constrained by InSAR Observations. Pure and Applied Geophysics, 168, 10, 1749-1758.
- El Mayor Cucapah, Mexico 2010
- Oskin, M.E., Arrowsmith, J.R., Corona, A.H., Elliott, A.J., Fletcher, J.M., Fielding, E.J., Gold, P.O., Garcia, J.J.G., Hudnet, K.W., Liu-Zeng, J., and Teran, O.J., 2012, Near-Field Deformation from the El Mayor–Cucapah Earthquake Revealed by Differential LIDAR. Science, 335, 6069, 702-705.
- Wei, S., Fielding, E., Leprince, S., Sladen, A., Avouac, J.P., Helmberger, D., Hauksson, E., Chu, R., Simons, M., Hudnut, K., Herring, T., and Briggs, R., Superficial simplicity of the 2010 El Mayor–Cucapah earthquake of Baja California in Mexico. Nature Geoscience, 4, 9, 615-619.
- Southern California Earthquake Data Center - El Mayor Cucapah Earthquake
- Haida Gwaii, Canada 2012
- Kao, H., Shan, S., and Frahbod, A.M., 2015, Source Characteristics of the 2012 Haida Gwaii Earthquake Sequence. Bulletin of the Seismological Society of America, 105, 2B, 1206-1218.
- Landers, CA 1992
- Northridge, CA 1994
- Zhang, J., Kuge, K., Lay, T., and Tsuboi, S., 1997, Determination of earthquake source mechanisms using teleseismic 30–140 s waves: The January 17, 1994, Northridge earthquake. Journal of Geophysical Research Solid Earth, 102, B4, 8159-8169.
- Southern California Earthquake Data Center - Northridge Earthquake
- South Napa, CA 2014
- F. Guangcaia, L. Zhiweia, S. Xinjianb, X., Binga, and D. Yanan, 2015, Source parameters of the 2014 Mw 6.1 South Napa earthquake estimated from the Sentinel 1A, COSMO-SkyMed and GPS data. Tectonophysics, 655, 139–146.
- D.S. Dreger, M.H. Huang, A. Rodgers, T. Taira, and K. Wooddell, 2015, Kinematic Finite‐Source Model for the 24 August 2014 South Napa, California, Earthquake from Joint Inversion of Seismic, GPS, and InSAR Data. Seismological research Letters, 86, 2A, 327-334.