Ground Failure Triggered by the 7 January 2020 M 6.4 Puerto Rico Earthquake

Kate E. Allstadt,Eric M. Thompson, Desiree Bayouth Garcia, Edwin Irizarry Brugman,K. Stephen Hughes,Robert G. Schmitt

SEISMOLOGICAL RESEARCH LETTERS(2022)

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摘要
The 7 January 2020 M 6.4 Puerto Rico earthquake, the mainshock of an extended earthquake sequence, triggered significant ground failure. In this study, we detail the ground failure that occurred based largely on a postearthquake field reconnaissance campaign that we conducted. We documented more than 300 landslides, mainly rock falls that were concentrated in areas where peak ground acceleration (PGA) exceeded 30%g; sparse smaller landslides occurred in highly susceptible areas more than 50 km from the epicenter and at PGA values <10%g. Though some of the largest mass movements were in natural slopes, rock falls in road cuts had more impact because they caused widespread transportation disruption. Some structures also were damaged by landslides, but no landslide-related fatalities were reported. Liquefaction and related lateral spreading were severe in some areas, causing damage to residential and commercial structures and a power plant. Most of the liquefaction occurred in coastal areas where shaking exceeded 50%g, though some of the most damaging instances were in Ponce, where shaking estimates were as low as 20%g. In this article, we summarize the most notable ground failures and detail the overall patterns that we observed. The observations and compiled inventory datasets presented here are valuable because no island-wide hazard maps for earthquake-triggered landslides or liquefaction have been developed for Puerto Rico, and postevent inventories are vital for such an effort. In general, the datasets presented here contribute to a global understanding of coseismic ground-failure hazard by presenting detailed observations for a relatively moderate ground-failure event with well-constrained shaking estimates; current models and expectations are biased by the tendency to collect detailed datasets mainly for exceptional events.
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