49 citations as recorded by crossref.

1. Discussion of “Case Study: Oso, Washington, Landslide of March 22, 2014—Material Properties and Failure Mechanism” by Timothy D. Stark, Ahmed K. Baghdady, Oldrich Hungr, and Jordan Aaron R. Iverson https://doi.org/10.1061/(ASCE)GT.1943-5606.0001917
2. The 28 November 2020 Landslide, Tsunami, and Outburst Flood – A Hazard Cascade Associated With Rapid Deglaciation at Elliot Creek, British Columbia, Canada M. Geertsema et al. https://doi.org/10.1029/2021GL096716
3. Discussion of “Case Study: Oso, Washington, Landslide of March 22, 2014—Material Properties and Failure Mechanism” by Timothy D. Stark, Ahmed K. Baghdady, Oldrich Hungr, and Jordan Aaron J. Keaton et al. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001919
4. AI-powered automatic detection of dynamic triggering of earthquake based on microseismic monitoring F. Jiang et al. https://doi.org/10.1016/j.soildyn.2022.107723
5. A framework for temporal and spatial rockfall early warning using micro-seismic monitoring L. Feng et al. https://doi.org/10.1007/s10346-020-01534-z
6. Analysis of the 2017 June Maoxian landslide processes with force histories from seismological inversion and terrain features J. Zhao et al. https://doi.org/10.1093/gji/ggaa269
7. Identifying landslides from continuous seismic surface waves: a case study of multiple small-scale landslides triggered by Typhoon Talas, 2011 R. Okuwaki et al. https://doi.org/10.1093/gji/ggab129
8. Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate C. Hibert et al. https://doi.org/10.5194/esurf-12-641-2024
9. The 22 December 2018 tsunami from flank collapse of Anak Krakatau volcano during eruption L. Ye et al. https://doi.org/10.1126/sciadv.aaz1377
10. Laboratory Landquakes: Insights From Experiments Into the High‐Frequency Seismic Signal Generated by Geophysical Granular Flows M. Arran et al. https://doi.org/10.1029/2021JF006172
11. The catastrophic 2025 Junlian landslide in Sichuan, China: insights revealed by seismic signals F. Pu et al. https://doi.org/10.1007/s10346-026-02717-w
12. Reconstruction of the 2018 tsunamigenic flank collapse and eruptive activity at Anak Krakatau based on eyewitness reports, seismo-acoustic and satellite observations A. Perttu et al. https://doi.org/10.1016/j.epsl.2020.116268
13. Direct observations of a three million cubic meter rock-slope collapse with almost immediate initiation of ensuing debris flows F. Walter et al. https://doi.org/10.1016/j.geomorph.2019.106933
14. Broad-band seismic analysis and modeling of the 2015 Taan Fjord, Alaska landslide using Instaseis L. Gualtieri & G. Ekström https://doi.org/10.1093/gji/ggy086
15. Shear surface undulations modulate clayey gouge strength and contribute to divergent landslide acceleration W. Schulz et al. https://doi.org/10.1016/j.enggeo.2025.108353
16. Insights on Multistage Rock Avalanche Behavior From Runout Modeling Constrained by Seismic Inversions A. Mitchell et al. https://doi.org/10.1029/2021JB023444
17. Seismic monitoring system for landslide hazard assessment and risk management at the drainage plant of the Peschiera Springs (Central Italy) R. Iannucci et al. https://doi.org/10.1016/j.enggeo.2020.105787
18. Seismic signature of the deadly snow avalanche of January 18, 2017, at Rigopiano (Italy) T. Braun et al. https://doi.org/10.1038/s41598-020-75368-z
19. Seismic Energy Analysis as Generated by Impact and Fragmentation of Single‐Block Experimental Rockfalls L. Saló et al. https://doi.org/10.1029/2017JF004374
20. The relationship between bulk‐mass momentum and short‐period seismic radiation in catastrophic landslides C. Hibert et al. https://doi.org/10.1002/2016JF004027
21. Dynamics of the Wulong landslide revealed by broadband seismic records Z. Li et al. https://doi.org/10.1186/s40623-017-0610-x
22. Evolution of a giant debris flow in the transitional mountainous region between the Tibetan Plateau and the Qinling Mountain range, Western China: Constraints from broadband seismic records X. Huang et al. https://doi.org/10.1016/j.jseaes.2017.08.031
23. Single-block rockfall dynamics inferred from seismic signal analysis C. Hibert et al. https://doi.org/10.5194/esurf-5-283-2017
24. Landslide reconstruction using seismic signal characteristics and numerical simulations: Case study of the 2017 “6.24” Xinmo landslide Y. Yan et al. https://doi.org/10.1016/j.enggeo.2020.105582
25. A Joint Seismic and Space‐Based Investigation of the 2016 Lamplugh Glacier and 2017 Wrangell Mountains (Alaska) Landslides X. Luo et al. https://doi.org/10.1029/2022JF006903
26. Enhanced landslide mobility by basal liquefaction: The 2014 State Route 530 (Oso), Washington, landslide B. Collins & M. Reid https://doi.org/10.1130/B35146.1
27. Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding J. Chang et al. https://doi.org/10.5194/nhess-25-451-2025
28. Broadband-seismic analysis of a massive landslide in southwestern China: Dynamics and fragmentation implications Z. Li et al. https://doi.org/10.1016/j.geomorph.2019.03.024
29. A rockslide-generated tsunami in a Greenland fjord rang Earth for 9 days K. Svennevig et al. https://doi.org/10.1126/science.adm9247
30. Estimation of dynamic friction and movement history of large landslides M. Yamada et al. https://doi.org/10.1007/s10346-018-1002-4
31. Oso, Washington, Landslide of March 22, 2014: Dynamic Analysis J. Aaron et al. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001748
32. Variation patterns of landslide basal friction revealed from long-period seismic waveform inversion D. Yu et al. https://doi.org/10.1007/s11069-019-03813-y
33. 3D analysis of 2014 Oso landslide P. Kargar et al. https://doi.org/10.1016/j.enggeo.2021.106100
34. Seismic Advances in Process Geomorphology K. Cook & M. Dietze https://doi.org/10.1146/annurev-earth-032320-085133
35. Earthquake hazard and risk analysis for natural and induced seismicity: towards objective assessments in the face of uncertainty J. Bommer https://doi.org/10.1007/s10518-022-01357-4
36. A large frozen debris avalanche entraining warming permafrost ground—the June 2021 Assapaat landslide, West Greenland K. Svennevig et al. https://doi.org/10.1007/s10346-022-01922-7
37. 2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges S. McDougall https://doi.org/10.1139/cgj-2016-0104
38. RETRACTED: Reconstructing the Dynamic Processes of the Taimali Landslide in Taiwan Using the Waveform Inversion Method G. Lin & C. Hung https://doi.org/10.3390/app10175872
39. Formation mechanism and dynamic process of open-pit coal mine landslides: a case study of the Xinjing landslide in Inner Mongolia, China Q. Wang et al. https://doi.org/10.1007/s10346-023-02193-6
40. Near-real-time seismic monitoring improves deep-seated landslides early warning, Jiuxianping, China L. Feng et al. https://doi.org/10.1016/j.enggeo.2025.108231
41. Closure to “Case Study: Oso, Washington, Landslide of March 22, 2014—Material Properties and Failure Mechanism” by Timothy D. Stark, Ahmed K. Baghdady, Oldrich Hungr, and Jordan Aaron T. Stark et al. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001920
42. The 22 March 2014 Oso landslide, Washington, USA J. Wartman et al. https://doi.org/10.1016/j.geomorph.2015.10.022
43. Using surface waves recorded by a large mesh of three-element arrays to detect and locate disparate seismic sources W. Fan et al. https://doi.org/10.1093/gji/ggy316
44. Effects of mass entrainment on the estimation of landslide parameters from long-period seismic inversion X. Wang et al. https://doi.org/10.1007/s10950-023-10165-5
45. Particle size effect on the impact processes and geomorphology of granular flow deposition J. Huang et al. https://doi.org/10.1016/j.enggeo.2026.108963
46. Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath A. Schöpa et al. https://doi.org/10.5194/esurf-6-467-2018
47. Impact‐Induced Liquefaction Mechanism of Sandy Silt at Different Saturations H. Li et al. https://doi.org/10.1155/2021/6686339
48. When hazard avoidance is not an option: lessons learned from monitoring the postdisaster Oso landslide, USA M. Reid et al. https://doi.org/10.1007/s10346-021-01686-6
49. Combining seismic signal dynamic inversion and numerical modeling improves landslide process reconstruction Y. Yan et al. https://doi.org/10.5194/esurf-10-1233-2022