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On Algorithmically Determined Versus Traditional Macroseismic Intensity Assignments

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Abstract The utility of macroseismic data, defined as the effects of earthquakes on humans and the built environment, has been increasingly recognized following the advent of online systems that now produce unprecedented volumes of macroseismic intensity information. Contributed reports from the U.S. Geological Survey “Did You Feel It?” (DYFI) system (Wald et al., 1999) are used to generate intensity values with an algorithm based on seminal work by Dengler and Dewey (1998). The algorithm was developed initially to reproduce intensity values assigned by expert opinion using questionnaire results collected by telephone survey. In this article, I discuss reasons why intensity values from (self-selected) DYFI responses can differ from values that would be assigned by expert opinion given more complete data from randomly selected participants. For example, with the data used by Dengler and Dewey (1998), intensities near 4 could be determined from the percentage of people who felt shaking in each town. With less spatially rich data from self-selected participants, this percentage often cannot be determined reliably. Audible noises are key additional diagnostic criteria for modified Mercalli intensity (MMI) 4, but, although the DYFI system includes a question about noise, following Dengler and Dewey (1998), the DYFI algorithm does not include a noise indicator. At the upper end of the scale, as defined the DYFI algorithm yields a maximum intensity value of 9.05, nominally corresponding to peak ground acceleration of 75%g. These and other factors can result in DYFI values that are low compared to traditional MMI values assigned using expert opinion, even absent factors that can bias traditional MMI assignments. Modern ground-motion intensity conversion equations determined using DYFI intensities are expected to be appropriate for DYFI intensities, but the results of this study suggest that biases may be introduced if DYFI and traditional intensities are assumed to be interchangeable.
Seismological Society of America (SSA)
Title: On Algorithmically Determined Versus Traditional Macroseismic Intensity Assignments
Description:
Abstract The utility of macroseismic data, defined as the effects of earthquakes on humans and the built environment, has been increasingly recognized following the advent of online systems that now produce unprecedented volumes of macroseismic intensity information.
Contributed reports from the U.
S.
Geological Survey “Did You Feel It?” (DYFI) system (Wald et al.
, 1999) are used to generate intensity values with an algorithm based on seminal work by Dengler and Dewey (1998).
The algorithm was developed initially to reproduce intensity values assigned by expert opinion using questionnaire results collected by telephone survey.
In this article, I discuss reasons why intensity values from (self-selected) DYFI responses can differ from values that would be assigned by expert opinion given more complete data from randomly selected participants.
For example, with the data used by Dengler and Dewey (1998), intensities near 4 could be determined from the percentage of people who felt shaking in each town.
With less spatially rich data from self-selected participants, this percentage often cannot be determined reliably.
Audible noises are key additional diagnostic criteria for modified Mercalli intensity (MMI) 4, but, although the DYFI system includes a question about noise, following Dengler and Dewey (1998), the DYFI algorithm does not include a noise indicator.
At the upper end of the scale, as defined the DYFI algorithm yields a maximum intensity value of 9.
05, nominally corresponding to peak ground acceleration of 75%g.
These and other factors can result in DYFI values that are low compared to traditional MMI values assigned using expert opinion, even absent factors that can bias traditional MMI assignments.
Modern ground-motion intensity conversion equations determined using DYFI intensities are expected to be appropriate for DYFI intensities, but the results of this study suggest that biases may be introduced if DYFI and traditional intensities are assumed to be interchangeable.

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