Danijel Schorlemmer

6.4k total citations · 1 hit paper
103 papers, 4.4k citations indexed

About

Danijel Schorlemmer is a scholar working on Geophysics, Artificial Intelligence and Civil and Structural Engineering. According to data from OpenAlex, Danijel Schorlemmer has authored 103 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Geophysics, 56 papers in Artificial Intelligence and 10 papers in Civil and Structural Engineering. Recurrent topics in Danijel Schorlemmer's work include earthquake and tectonic studies (69 papers), Earthquake Detection and Analysis (42 papers) and Seismology and Earthquake Studies (38 papers). Danijel Schorlemmer is often cited by papers focused on earthquake and tectonic studies (69 papers), Earthquake Detection and Analysis (42 papers) and Seismology and Earthquake Studies (38 papers). Danijel Schorlemmer collaborates with scholars based in Germany, United States and Switzerland. Danijel Schorlemmer's co-authors include Stefan Wiemer, Max Wyss, J. Woessner, Matthew C. Gerstenberger, David A. Rhoades, T. W. Becker, Thomas Goebel, C. G. Sammis, Georg Dresen and D. D. Jackson and has published in prestigious journals such as Nature, SHILAP Revista de lepidopterología and Journal of Geophysical Research Atmospheres.

In The Last Decade

Danijel Schorlemmer

100 papers receiving 4.3k citations

Hit Papers

Variations in earthquake-... 2005 2026 2012 2019 2005 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Danijel Schorlemmer Germany 33 4.0k 1.8k 398 296 142 103 4.4k
Sebastian Hainzl Germany 40 4.5k 1.1× 1.2k 0.7× 290 0.7× 212 0.7× 178 1.3× 153 4.8k
Lucile M. Jones United States 34 4.4k 1.1× 1.3k 0.8× 406 1.0× 164 0.6× 117 0.8× 71 4.8k
Max Wyss United States 51 8.6k 2.2× 2.1k 1.2× 612 1.5× 436 1.5× 232 1.6× 203 9.3k
Paul A. Reasenberg United States 28 6.7k 1.7× 1.7k 0.9× 512 1.3× 226 0.8× 167 1.2× 44 7.0k
Andrew J. Michael United States 40 6.2k 1.6× 1.6k 0.9× 583 1.5× 204 0.7× 64 0.5× 99 6.8k
V. I. Keilis‐Borok Russia 30 2.7k 0.7× 1.3k 0.7× 134 0.3× 140 0.5× 462 3.3× 93 3.4k
R. Shcherbakov Canada 22 1.7k 0.4× 616 0.4× 152 0.4× 239 0.8× 204 1.4× 61 2.1k
Egill Hauksson United States 54 9.9k 2.5× 3.7k 2.1× 610 1.5× 246 0.8× 58 0.4× 174 10.4k
J. Woessner Switzerland 23 2.9k 0.7× 877 0.5× 492 1.2× 99 0.3× 63 0.4× 38 3.2k
Jiancang Zhuang Japan 29 2.7k 0.7× 1.5k 0.8× 202 0.5× 54 0.2× 310 2.2× 136 3.4k

Countries citing papers authored by Danijel Schorlemmer

Since Specialization
Citations

This map shows the geographic impact of Danijel Schorlemmer'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 Danijel Schorlemmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danijel Schorlemmer more than expected).

Fields of papers citing papers by Danijel Schorlemmer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danijel Schorlemmer. 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 Danijel Schorlemmer. The network helps show where Danijel Schorlemmer may publish in the future.

Co-authorship network of co-authors of Danijel Schorlemmer

This figure shows the co-authorship network connecting the top 25 collaborators of Danijel Schorlemmer. A scholar is included among the top collaborators of Danijel Schorlemmer 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 Danijel Schorlemmer. Danijel Schorlemmer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sairam, Nivedita, Jan Visscher, Danijel Schorlemmer, et al.. (2024). Effective Adaptation Options to Alleviate Nuisance Flooding in Coastal Megacities—Learning From Ho Chi Minh City, Vietnam. Earth s Future. 12(11). 4 indexed citations
2.
Savran, William H., Pablo Iturrieta, Matthew C. Gerstenberger, et al.. (2023). Are Regionally Calibrated Seismicity Models More Informative than Global Models? Insights from California, New Zealand, and Italy. SHILAP Revista de lepidopterología. 3(2). 86–95. 6 indexed citations
3.
Melis, Νikolaos S., et al.. (2023). Seismic monitoring in Greece, 1899–2014: catalogue completeness 1966–2014. Geophysical Journal International. 235(2). 1049–1063. 1 indexed citations
4.
Pilz, Marco, et al.. (2022). Calculating earthquake damage building by building: the case of the city of Cologne, Germany. Bulletin of Earthquake Engineering. 20(3). 1519–1565. 11 indexed citations
6.
Savran, William H., Pablo Iturrieta, Khawaja M. Asim, et al.. (2022). pyCSEP: A Python Toolkit for Earthquake Forecast Developers. Seismological Research Letters. 93(5). 2858–2870. 19 indexed citations
7.
Kotha, Sreeram Reddy, et al.. (2021). Testing Nonlinear Amplification Factors of Ground-Motion Models. Bulletin of the Seismological Society of America. 111(4). 2121–2137. 20 indexed citations
8.
Pilz, Marco, et al.. (2020). Seismic risk analysis in Germany: an example from the Lower Rhine Embayment. Final report. GFZpublic. 2 indexed citations
9.
Savran, William H., P. J. Maechling, Maximilian J. Werner, et al.. (2019). The Collaboratory for the Study of Earthquake Predictability Version 2 (CSEP2): Testing Forecasts that Generate Synthetic Earthquake Catalogs. Publication Database GFZ (GFZ German Research Centre for Geosciences). 12445. 1 indexed citations
10.
Schneider, Max, et al.. (2016). Prospectively Evaluating the Collaboratory for the Study of Earthquake Predictability: An Evaluation of the UCERF2 and Updated Five-Year RELM Forecasts. Publication Database GFZ (GFZ German Research Centre for Geosciences). 1 indexed citations
11.
Euchner, F., et al.. (2016). QuakeML 2.0: Recent developments. Publication Database GFZ (GFZ German Research Centre for Geosciences). 1 indexed citations
12.
Schorlemmer, Danijel, et al.. (2012). The Source Inversion Validation (SIV) Initiative: A Collaborative Study on Uncertainty Quantification in Earthquake Source Inversions. EGUGA. 8578. 1 indexed citations
13.
Holschneider, M., C. Narteau, П. Н. Шебалин, Zhigang Peng, & Danijel Schorlemmer. (2012). Bayesian analysis of the modified Omori law. Publication Database GFZ (GFZ German Research Centre for Geosciences). 3536. 4 indexed citations
14.
Page, M. T., et al.. (2010). Source Inversion Validation: Quantifying Uncertainties in Earthquake Source Inversions. AGU Fall Meeting Abstracts. 2010. 1 indexed citations
15.
Schorlemmer, Danijel, D. D. Jackson, J. D. Zechar, & T. H. Jordan. (2009). Collaboratory for the Study of Earthquake Predictability: Design of Prediction Experiments. AGUFM. 2009. 2 indexed citations
16.
Tsuruoka, Hiroshi, et al.. (2008). CSEP Earthquake Forecast Testing Center for Japan. AGUFM. 2008. 2 indexed citations
17.
Bachmann, Charles M., Danijel Schorlemmer, J. Woessner, & Stefan Wiemer. (2005). Probabilistic Estimates of Monitoring Completeness of Seismic Networks. AGU Fall Meeting Abstracts. 2005. 5 indexed citations
18.
Schorlemmer, Danijel, et al.. (2004). QuakeML - An XML Schema for Seismology. AGUFM. 2004. 15 indexed citations
19.
Jackson, D. D., Danijel Schorlemmer, Matthew C. Gerstenberger, et al.. (2004). Prospective Tests of Southern California Earthquake Forecasts. AGU Fall Meeting Abstracts. 2004. 2 indexed citations
20.
Schorlemmer, Danijel, et al.. (2002). b-value: What about focal mechanisms?. AGUFM. 2002. 1 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.

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