Jannes Münchmeyer

1.0k total citations · 1 hit paper
29 papers, 632 citations indexed

About

Jannes Münchmeyer is a scholar working on Geophysics, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Jannes Münchmeyer has authored 29 papers receiving a total of 632 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Geophysics, 20 papers in Artificial Intelligence and 3 papers in Molecular Biology. Recurrent topics in Jannes Münchmeyer's work include Seismology and Earthquake Studies (18 papers), earthquake and tectonic studies (14 papers) and Seismic Waves and Analysis (9 papers). Jannes Münchmeyer is often cited by papers focused on Seismology and Earthquake Studies (18 papers), earthquake and tectonic studies (14 papers) and Seismic Waves and Analysis (9 papers). Jannes Münchmeyer collaborates with scholars based in Germany, France and United States. Jannes Münchmeyer's co-authors include Ulf Leser, Frederik Tilmann, Dino Bindi, Leon Weber, Dietrich Lange, Andreas Rietbrock, Jack Woollam, Joachim Saul, Tim Rocktäschel and Carlo Giunchi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Geophysical Research Letters.

In The Last Decade

Jannes Münchmeyer

26 papers receiving 622 citations

Hit Papers

align trajectories

What are hit papers?

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.

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

2022 121 citations Jannes Münchmeyer, Jack Woollam et al. Journal of Geophysical Research Solid Earth profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jannes Münchmeyer Germany	14	469	426	54	24	17	29	632
Rui Yan China	16	101 0.2×	488 1.1×	74 1.4×	22 0.9×		71	645
Masaki Murakami Japan	11	84 0.2×	297 0.7×	16 0.3×	6 0.3×	39 2.3×	51	416
Chris Johnson United Kingdom	7	134 0.3×	296 0.7×	25 0.5×	7 0.3×	5 0.3×	21	431
Marcelo B. Pádua Brazil	15	154 0.3×	492 1.2×	62 1.1×	54 2.3×	9 0.5×	48	586
Shufan Zhao China	15	142 0.3×	555 1.3×	45 0.8×	36 1.5×	1 0.1×	58	655
A. Peratzakis Greece	14	263 0.6×	470 1.1×	57 1.1×	35 1.5×		17	619
G. Giordano Italy	9	87 0.2×	121 0.3×	28 0.5×	134 5.6×		45	237
Mamoru Hyodo Japan	14	74 0.2×	365 0.9×	6 0.1×	11 0.5×		36	433
Zhi Yin China	11	167 0.4×	19 0.0×	21 0.4×	9 0.4×		39	358

Countries citing papers authored by Jannes Münchmeyer

Since Specialization

Citations

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

Fields of papers citing papers by Jannes Münchmeyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jannes Münchmeyer

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

All Works

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20 of 20 papers shown

Denolle, Marine, Jannes Münchmeyer, Carlos Suárez, et al.. (2025). A review of cloud computing and storage in seismology. Geophysical Journal International. 243(1).

Isken, Marius Paul, et al.. (2025). Qseek: A data-driven Framework for Automated Earthquake Detection, Localization and Characterization. SPIRE - Sciences Po Institutional REpository. 4(1). 5 indexed citations

Denolle, Marine, et al.. (2025). A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale. arXiv (Cornell University). 4(2).

Sippl, Christian, et al.. (2025). Benchmarking seismic phase associators: Insights from synthetic scenarios. SPIRE - Sciences Po Institutional REpository. 4(2).

Münchmeyer, Jannes, David Marsan, Mickaël Langlais, et al.. (2025). Characterizing the Atacama Segment of the Chile Subduction Margin (24°S–31°S) With >165,000 Earthquakes. Journal of Geophysical Research Solid Earth. 130(7). 1 indexed citations

Münchmeyer, Jannes. (2024). PyOcto: A high-throughput seismic phase associator. SHILAP Revista de lepidopterología. 3(1). 17 indexed citations

Shi, Peidong, Men‐Andrin Meier, Linus Villiger, et al.. (2024). From Labquakes to Megathrusts: Scaling Deep Learning Based Pickers Over 15 Orders of Magnitude. SHILAP Revista de lepidopterología. 1(4). 4 indexed citations

Moreno, Marcos, Christian Sippl, Juan Carlos Báez, et al.. (2023). Relation Between Oceanic Plate Structure, Patterns of Interplate Locking and Microseismicity in the 1922 Atacama Seismic Gap. Geophysical Research Letters. 50(15). 14 indexed citations

Lange, Dietrich, Jannes Münchmeyer, Jack Woollam, et al.. (2023). PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning. Earth and Space Science. 11(1). 17 indexed citations

10.

Foster, William J., Bethany J. Allen, Jannes Münchmeyer, et al.. (2023). How predictable are mass extinction events?. Royal Society Open Science. 10(3). 18 indexed citations

11.

Woollam, Jack, Jannes Münchmeyer, Frederik Tilmann, et al.. (2022). SeisBench—A Toolbox for Machine Learning in Seismology. Seismological Research Letters. 93(3). 1695–1709. 96 indexed citations

12.

Münchmeyer, Jannes, Jack Woollam, Andreas Rietbrock, et al.. (2022). Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers. Journal of Geophysical Research Solid Earth. 127(1). 121 indexed citations breakdown →

13.

Münchmeyer, Jannes, Ulf Leser, & Frederik Tilmann. (2022). A probabilistic view on rupture predictability: all earthquakes evolve similarly. arXiv (Cornell University). 7 indexed citations

14.

Foster, William J., Georgy Ayzel, Jannes Münchmeyer, et al.. (2022). Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology. 48(3). 357–371. 16 indexed citations

15.

Münchmeyer, Jannes, et al.. (2022). Graph Neural Networks for Learning Molecular Excitation Spectra. Journal of Chemical Theory and Computation. 18(7). 4408–4417. 22 indexed citations

16.

Münchmeyer, Jannes, Dino Bindi, Ulf Leser, & Frederik Tilmann. (2020). The Transformer Earthquake Alerting Model: Improving Earthquake Early Warning with Deep Learning. AGU Fall Meeting Abstracts. 2020. 1 indexed citations

17.

Münchmeyer, Jannes, Dino Bindi, Ulf Leser, & Frederik Tilmann. (2020). The transformer earthquake alerting model: a new versatile approach to earthquake early warning. Geophysical Journal International. 225(1). 646–656. 75 indexed citations

18.

Weber, Leon, Pasquale Minervini, Jannes Münchmeyer, Ulf Leser, & Tim Rocktäschel. (2019). NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language. 6151–6161. 47 indexed citations

19.

Münchmeyer, Jannes, Dino Bindi, Christian Sippl, Ulf Leser, & Frederik Tilmann. (2019). Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile. Geophysical Journal International. 220(1). 142–159. 18 indexed citations

20.

Münchmeyer, Jannes, et al.. (2017). Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization. BMC Systems Biology. 11(1). 41–41. 12 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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