CHAKRABORTY, Megha, LI, Wei, FABER, Johannes, RÜMPKER, Georg, STÖCKER, Horst und SRIVASTAVA, Nishtha, 2022. A study on the effect of input data length on a deep-learning-based magnitude classifier. Göttingen: Copernicus Publ.
Elsevier - Harvard (with titles)Chakraborty, M., Li, W., Faber, J., Rümpker, G., Stöcker, H., Srivastava, N., 2022. A study on the effect of input data length on a deep-learning-based magnitude classifier, Solid earth. Copernicus Publ., Göttingen. https://doi.org/10.5194/se-13-1721-2022
American Psychological Association 7th editionChakraborty, M., Li, W., Faber, J., Rümpker, G., Stöcker, H., & Srivastava, N. (ca. 2022). A study on the effect of input data length on a deep-learning-based magnitude classifier [Cd]. In Solid earth. Copernicus Publ. https://doi.org/10.5194/se-13-1721-2022
Springer - Basic (author-date)Chakraborty M, Li W, Faber J, Rümpker G, Stöcker H, Srivastava N (2022) A study on the effect of input data length on a deep-learning-based magnitude classifier. Copernicus Publ., Göttingen
Juristische Zitierweise (Stüber) (Deutsch)Chakraborty, Megha/ Li, Wei/ Faber, Johannes/ Rümpker, Georg/ Stöcker, Horst/ Srivastava, Nishtha, A study on the effect of input data length on a deep-learning-based magnitude classifier, Göttingen 2022.