USGS ScienceSearch

USGS · 70168456

Potential improvements in horizontal very broadband seismic data in the IRIS/USGS component of the Global Seismic Network

Abstract

The Streckeisen STS‐1 has been the primary vault‐type seismometer used in the over‐150‐station Global Seismographic Network (GSN). This sensor has long been known for its outstanding vertical, very long‐period (e.g., >100  s period), and low‐noise performance, although the horizontal long‐period noise performance is less well known. The STS‐1 is a limited, important resource, because it is no longer made or supported by the original manufacturer. We investigate the incoherent noise of horizontal‐component sensors, where coherent signals among sensors have been removed, giving an upper bound on the self‐noise of both the STS‐1 and STS‐2 horizontal components. Our findings suggest that a well‐installed STS‐2 could potentially produce data with similar or better incoherent noise levels to that of a horizontal‐component STS‐1. Along with our experimental investigation, we compare background noise levels for a calendar year at Incorporated Research Institutions for Seismology/U.S. Geological Survey network stations, which comprise approximately two‐thirds of the GSN, with collocated STS‐1 and STS‐2 seismometers. The use of an STS‐2‐class of sensor (flat to velocity to 120 s period) to acquire low‐frequency data in surface‐vault installations would allow network operators to focus more attention on improving vertical data. In order to deal with the difference in instrument response shapes between the two instruments, we detail two different time‐domain filters that would allow users to convert broadband STS‐2 data into very broadband data with a response similar to that of an STS‐1 (flat to velocity to 360 s period). We conclude that the complexity of the current primary horizontal vault sensors in the GSN may not be necessary until we are better able to isolate surface horizontal sensors from various noise sources.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Adam T. Ringler, J.M. Steim, T Zandt, Charles R. Hutt, David C. Wilson, Tyler Storm. 2015-12-16. Potential improvements in horizontal very broadband seismic data in the IRIS/USGS component of the Global Seismic Network. https://doi.org/10.1785/0220150181

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