USGS ScienceSearch

USGS · 70212701

#EarthquakeAdvisory: Exploring discourse between government officials, news media and social media during the Bombay Beach 2016 Swarm

Abstract

Communicating probabilities of natural hazards to varied audiences is a notoriously difficult task. Many of these challenges were encountered during the 2016 Bombay Beach, California, swarm of ~100 2≤M≤4.3 earthquakes, which began on 26 September 2016 and lasted for several days. The swarm’s proximity to the southern end of the San Andreas fault caused concern that a larger earthquake could be triggered. Within 1–2 days, different forecast models were used to evaluate the likelihood of a larger event with two agencies (the U.S. Geological Survey [USGS] and the California Governor’s Office of Emergency Services) releasing probabilities and forecasts for larger earthquakes. Our research explores communication and news media efforts, as well as how people on a microblogging social media site (Twitter) responded to these forecasts. Our findings suggest that news media used a combination of information sources, basing their articles on what they learned from social media, as well as using information provided by government agencies. As the swarm slowed down, there is evidence of the continued interplay between news media and social media, with the USGS issuing revised probability reports and scientists from the USGS and other institutions participating in media interviews. In reporting on the swarm, news media often used language more generally than the scientists; terms such as probability, likelihood, chance, and possibility were used interchangeably. Knowledge of how news media used scientific information from the 2016 Bombay Beach forecasts can assist local, state, and federal agencies in developing effective communication strategies to respond to future earthquakes.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 33.330528249028085° to 33.37641235124676° latitude; -115.740966796875° to -115.63247680664062° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sara K. McBride, Andrea L. Llenos, Morgan T. Page, Nicholas van der Elst. 2019-11-06. #EarthquakeAdvisory: Exploring discourse between government officials, news media and social media during the Bombay Beach 2016 Swarm. https://doi.org/10.1785/0220190082

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Digitizer Suite: The Albuquerque Seismological Laboratory Digitizer Testing Suite

Laboratory testing of digitizers and seismometers helps ensure that prior to deployment the instrumentation can produce high quality data and is operating within specifications. In this work we detail the software package called: the Albuquerque Seismological Laboratory (ASL) Digitizer Test Suite. This Java software package provides several algorithms to verify various performance parameters of digitizers commonly used for recording analog seismic instruments. The goal of these tests is not to be exhaustive, but to identify common failures that could compromise the integrity of seismic data being recorded on the digitizer. For example, Sandia National Laboratories (e.g., Slad and Merchant, 2018) routinely do comprehensive testing of digitizers for various monitoring missions. While these tests reports are valuable for comprehensively characterizing a recording system, it would be resource intensive to conduct such tests on every seismic recorder used in a network. We focus on tests that include ways to estimate the sensitivity, timing, self-noise, and clip-level of the digitizer, as well as the fidelity of the signal being recorded. The software is publicly available and provides a way for the community to verify the integrity of a digitizer using a minimum amount of outside equipment.

Seismological Research Letters

The digital archivist: Automating legacy macroseismic data processing using large language models

Macroseismic data are a key resource to investigate shaking and damage from preinstrumental and early instrumental eras. However, data are often stored as inconsistently formatted reports describing observed shaking and damage, making manually parsing and interpreting accounts labor‐intensive. We introduce a novel workflow using Google’s Gemini 2.5 Pro large language model (LLM) to automate the extraction and structuring of macroseismic observations from summary reports. We apply this workflow to the 22 March 1957 M 5.3 Daly City, California, earthquake as a case study. We used Gemini to extract addresses, originally assigned modified Mercalli intensity values, and descriptions from each report. To address coordinate precision limits, addresses were geocoded via Google’s Geocoding application programming interface. This workflow yielded over 2300 geocoded intensity reports for the Daly City earthquake. We use the geocoded accounts, with the original report intensity assignments, to develop a shaking intensity map that in some respects rivals modern Did You Feel It? Maps. We also extract and present data for the 9 February 1971 M L 6.7 Sylmar, California, earthquake. Our results demonstrate the potential of LLMs for reliably extracting and analyzing large, unstructured macroseismic datasets. LLMs offer a scalable solution for rapidly digitizing macroseismic archives, enabling their broader use to constrain ground‐motion models in modern seismic hazard analysis and to improve our understanding of site effects in urban areas. The concepts explored here may also be applied to the handling of other legacy seismological and earth science data.

Seismological Research Letters