Can “Did You Feel It?” Intensity Data Be Used in Earthquake Ground Motion Modeling?
College
College of Engineering & Technology (Batten)
Department
Civil & Environmental Engineering
Graduate Level
Doctoral
Presentation Type
No Preference
Abstract
“Did You Feel It?” (DYFI) is an online program that allows citizens to report their feelings and responses to a list of questions related to earthquake shaking and damage. Modified Mercalli Intensity (MMI) is calculated from the reports as a quantitative metric (scaled from 0 to 10) representing earthquake shaking and damage level. Compared to ground motion records collected by ground motion stations, MMI data have a much denser and larger spatial coverage, especially in urban and low seismicity areas. However, because they are derived from subjective responses, MMI data have not been used in ground motion modeling, although they can fill the spatial distribution gaps of ground motion stations. For example, while the short-distance ground motions are most critical (as they potentially produce more damage than longer distances), very few ground motion records at short distances (less than 50 km) are available in the Central and Eastern United States (CEUS). If MMI data were proved to provide consistent decay trends of ground motion shaking along wave propagation distance (defined as path attenuation) as ground motion records, the earthquake ground motion prediction model for the CEUS would be better constrained at short distances by incorporating MMI-inferred ground motions. Given the benefits, this study investigates the consistency between recorded ground motions and inferred ground motions by MMI data, aiming to answer whether MMI data can be used in ground motion modeling. We first collected co-located (within 1 km apart) ground motion records and MMI data from the extended California ground motion database and United States Geological Survey (USGS) DYFI repositories. We then found a strong association between MMI and Peak Ground Acceleration (PGA) using the resultant 3,257 pairs of data. Secondly, we identified 15 PGA-to-DYFI conversion models in the literature and applied the models to infer PGA from MMI. However, none of the models demonstrated good fits to the co-located PGA and MMI data, leading to the proposal of a new model for converting MMI to PGA . The new MMI-to-PGA conversion model significantly reduces model bias and mode uncertainty. The new conversion model was then applied to convert MMI to PGA for the 2011 Virginia earthquake, the largest earthquake with a magnitude of 5.8 in Virginia in the past 30 years. Our analysis showed that MMI exhibited statistically significant consistent path attenuation with ground motion records. By incorporating MMI data, the short-distance ground motions (less than 50 km) were increased intensively, from only four ground motion records to more than 150 inferred ground motions. The promising results suggest a strong potential for using DYFI data to improve ground motion modeling.
Keywords
Did You Feel It?, Modified Mercalli Intensity, Central and Eastern United States, Ground Motion Prediction Model, United States Geological Survey, Peak Ground Acceleration, Path Attenuation.
Can “Did You Feel It?” Intensity Data Be Used in Earthquake Ground Motion Modeling?
“Did You Feel It?” (DYFI) is an online program that allows citizens to report their feelings and responses to a list of questions related to earthquake shaking and damage. Modified Mercalli Intensity (MMI) is calculated from the reports as a quantitative metric (scaled from 0 to 10) representing earthquake shaking and damage level. Compared to ground motion records collected by ground motion stations, MMI data have a much denser and larger spatial coverage, especially in urban and low seismicity areas. However, because they are derived from subjective responses, MMI data have not been used in ground motion modeling, although they can fill the spatial distribution gaps of ground motion stations. For example, while the short-distance ground motions are most critical (as they potentially produce more damage than longer distances), very few ground motion records at short distances (less than 50 km) are available in the Central and Eastern United States (CEUS). If MMI data were proved to provide consistent decay trends of ground motion shaking along wave propagation distance (defined as path attenuation) as ground motion records, the earthquake ground motion prediction model for the CEUS would be better constrained at short distances by incorporating MMI-inferred ground motions. Given the benefits, this study investigates the consistency between recorded ground motions and inferred ground motions by MMI data, aiming to answer whether MMI data can be used in ground motion modeling. We first collected co-located (within 1 km apart) ground motion records and MMI data from the extended California ground motion database and United States Geological Survey (USGS) DYFI repositories. We then found a strong association between MMI and Peak Ground Acceleration (PGA) using the resultant 3,257 pairs of data. Secondly, we identified 15 PGA-to-DYFI conversion models in the literature and applied the models to infer PGA from MMI. However, none of the models demonstrated good fits to the co-located PGA and MMI data, leading to the proposal of a new model for converting MMI to PGA . The new MMI-to-PGA conversion model significantly reduces model bias and mode uncertainty. The new conversion model was then applied to convert MMI to PGA for the 2011 Virginia earthquake, the largest earthquake with a magnitude of 5.8 in Virginia in the past 30 years. Our analysis showed that MMI exhibited statistically significant consistent path attenuation with ground motion records. By incorporating MMI data, the short-distance ground motions (less than 50 km) were increased intensively, from only four ground motion records to more than 150 inferred ground motions. The promising results suggest a strong potential for using DYFI data to improve ground motion modeling.