American Psychological Association 6th edition

Sabbaghi, H., Tabatabaei, S. H., & Fathianpour, N. (2025). Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering of frequency domain information. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-27332-y

ISO-690 (author-date, English)

SABBAGHI, Hamid, TABATABAEI, Seyed Hassan and FATHIANPOUR, Nader, 2025. Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering of frequency domain information. Scientific Reports. 1 December 2025. Vol. 15, no. 1, . DOI 10.1038/s41598-025-27332-y.

Modern Language Association 9th edition

Sabbaghi, H., S. H. Tabatabaei, and N. Fathianpour. “Multi-Element Geochemical Anomaly Recognition Applying Geologically-Constrained Convolutional Deep Learning Algorithm With Butterworth Filtering of Frequency Domain Information”. Scientific Reports, vol. 15, no. 1, Dec. 2025, https://doi.org/10.1038/s41598-025-27332-y.

Mohr Siebeck - Recht (Deutsch - Österreich)

Sabbaghi, Hamid/Tabatabaei, Seyed Hassan/Fathianpour, Nader: Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering of frequency domain information, Scientific Reports 2025,

Emerald - Harvard

Sabbaghi, H., Tabatabaei, S.H. and Fathianpour, N. (2025), “Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering of frequency domain information”, Scientific Reports, Vol. 15 No. 1, available at:https://doi.org/10.1038/s41598-025-27332-y.

Warning: These citations may not always be 100% accurate.