Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3)--The Time-Independent Model
2014458 citationsEdward H. Field, R. Arrowsmith et al.Bulletin of the Seismological Society of Americaprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Ray J. Weldon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ray J. Weldon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ray J. Weldon more than expected).
This network shows the impact of papers produced by Ray J. Weldon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ray J. Weldon. The network helps show where Ray J. Weldon may publish in the future.
Co-authorship network of co-authors of Ray J. Weldon
This figure shows the co-authorship network connecting the top 25 collaborators of Ray J. Weldon.
A scholar is included among the top collaborators of Ray J. Weldon based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ray J. Weldon. Ray J. Weldon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Field, Edward H., R. Arrowsmith, G. P. Biasi, et al.. (2013). Overview of the Uniform California Earthquake Rupture Forecast Version 3 (UCERF3) Time-Independent Model. AGUFM. 2013.1 indexed citations
Weldon, Ray J.. (2011). To what extent does earthquake variability affect slip rate estimates; a test using San Andreas fault paleoseismology.. AGUFM. 2011.1 indexed citations
12.
Arrowsmith, R., et al.. (2011). LiDAR-derived measurements of slip in the most recent ground-rupturing earthquakes along elements of the San Andreas fault system. AGUFM. 2011.1 indexed citations
13.
Wald, David J., Kristin D. Marano, Trevor I. Allen, et al.. (2010). Best practices for using macroseismic intensity and ground motion to intensity conversion equations for hazard and loss models. Seismological Research Letters.5 indexed citations
14.
Page, M. T., K. R. Felzer, Ray J. Weldon, & G. P. Biasi. (2008). The Magnitude-Frequency Distribution on the Southern San Andreas Fault Follows the Gutenberg-Richter Distribution. AGU Fall Meeting Abstracts. 2008.4 indexed citations
15.
Schmidt, D. A., Reed J. Burgette, & Ray J. Weldon. (2007). The Distribution of Interseismic Locking on the Central Cascadia Subduction Zone Inferred From Coastal Uplift Rates in Oregon. AGUFM. 2007.2 indexed citations
16.
Scharer, Katherine M., Ray J. Weldon, T. E. Fumal, & G. P. Biasi. (2003). Paleoseismic Data Used to Evaluate Long Term Earthquake Behavior. AGU Fall Meeting Abstracts. 2003.1 indexed citations
Weldon, Ray J., M. B. Boslough, & Thomas J. Ahrens. (1980). Shock-Induced Color Changes in Nontronite: a Possible Martian Surface Process. Lunar and Planetary Science Conference. 1234–1235.2 indexed citations
19.
Boslough, M. B., Ray J. Weldon, & Thomas J. Ahrens. (1980). Impact-induced water loss from serpentine, nontronite and kernite. Lunar and Planetary Science Conference Proceedings. 3. 2145–2158.32 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.