Characterizing differences in seismicity clustering and background rates between Hawaii and California

Clicks: 27
ID: 326370
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #35 of 338 articles by views in geophysical journal international

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 338 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Summary The statistics of earthquake populations are governed by stress changes and event interactions at various scales which promote non-linear event cascades and seismicity clustering. Earthquake clustering in space and time is observed in both tectonic and volcanically dominated regions; however, notable differences exist for instance during eruptive sequences. Here, we characterize seismicity statistics in California and Hawaii and quantify differences in background rates and clustering. We compare three statistical approaches, i.e, i) Reasenberg declustering, ii) Gamma distribution fit, and iii) Nearest Neighbor Clustering which are applied to seismicity in Hawaii, southern California, and ETAS catalogs with known clustering characteristics. Tests with ETAS-catalogs suggest that background rate variations are more consistently resolvable using the Nearest Neighbor and Gamma distribution algorithm without requiring parameter adjustments to the specific study area. The Nearest Neighbor method tends to overestimate background rates, whereas the Gamma distribution algorithm shows systematic underestimation, suggesting that an ensemble model can improve the performance over any single method. Uncertainties in background rate estimates based on small magnitude events (e.g., M≥2.5) are high, in particular for the Reasenberg method, which overpredicts true rates by up to a factor of 4, and extrapolations to larger magnitudes need to be treated cautiously. Our analysis shows that the combination of the three approaches generates new insights into the processes that govern seismicity clustering. The analysis of southern California seismicity produces consistent results between the different methods, and the distribution of background seismicity rates is indistinguishable from Poissonian rates at magnitudes above 3.5. Conversely, the seismicity in Hawaii exhibits notable non-Poissonian background rate changes. Results from the Nearest Neighbor clustering analysis suggest notable differences between tectonic and volcano seismicity, including background rate changes and more pronounced spatiotemporal clustering around active volcanoes. Hawaiian seismicity is characterized by more localized spatial-temporal clustering, in particular during the 2018 eruption. This difference may be a result of magmatic, hydrothermal and eruptive processes, which make volcanic areas susceptible to stress perturbations and earthquake triggering at small scales.
Reference Key
openalex_W7204179122 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sadia Marium Rinty, Thomas H W Goebel
Are you one of these authors? Claim your profile
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag341
URL
https://doi.org/10.1093/gji/ggag341
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.