14 citations as recorded by crossref.

1. Earthquake Forecasting Using Big Data and Artificial Intelligence: A 30-Week Real-Time Case Study in China O. Saad et al. https://doi.org/10.1785/0120230031
2. Changes in animal activity prior to a major (M= 7) earthquake in the Peruvian Andes R. Grant et al. https://doi.org/10.1016/j.pce.2015.02.012
3. Changes in the Specific Electrical Resistivity of Rocks at Different Depths of the Geoelectric Section and Their Comparison with Seismicity A. Bobachev et al. https://doi.org/10.1134/S0001433822130114
4. Cross-correlation earthquake precursors in the hydrogeochemical and geoacoustic signals for the Kamchatka peninsula G. Ryabinin et al. https://doi.org/10.2478/s11600-012-0031-y
5. GPS measurements on pre-, co- and post-seismic surface deformation at first multi-parametric geophysical observatory, Ghuttu in Garhwal Himalaya, India P. Gautam et al. https://doi.org/10.1515/jogs-2020-0114
6. STOCHASTIC VARIABILITY IN X-RAY EMISSION FROM THE BLACK HOLE BINARY GRS 1915+105 Y. Polyakov et al. https://doi.org/10.1088/0004-6256/143/6/148
7. Precursor-Based Earthquake Prediction Research: Proposal for a Paradigm-Shifting Strategy A. Szakács https://doi.org/10.3389/feart.2020.548398
8. Earthquake forecasting: a possible solution considering the GPS ionospheric delay M. De Agostino & M. Piras https://doi.org/10.5194/nhess-11-3263-2011
9. Modulation of geoacoustic emission intensity by time-varying electric field V. Gavrilov & A. Naumov https://doi.org/10.2205/2017ES000591
10. Modulating impact of electromagnetic radiation on geoacoustic emission of rocks V. Gavrilov et al. https://doi.org/10.2205/2013ES000527
11. Models (form) of long-, medium- and short-term earthquake precursors A. Nasirbekovich & Y. Rustamovich https://doi.org/10.1016/j.geog.2022.07.002
12. Is It Possible to Predict Strong Earthquakes? Y. Polyakov et al. https://doi.org/10.1007/s00024-014-0895-7
13. The physical basis of the effects caused by electromagnetic forcing in the intensity of geoacoustic processes V. Gavrilov et al. https://doi.org/10.1134/S1069351314010042
14. Evaluation of the Quality of Periodic Variation Extraction in Model Time Series Under Various Levels of Flicker Noise 1/fα . Glukhov https://doi.org/10.26117/2079-6641-2026-54-2-142-165