| Earthquake scenario in West Bengal with emphasis on seismic hazard microzonation of the city of Kolkata, India SK Nath, MD Adhikari, SK Maiti, N Devaraj, N Srivastava, LD Mohapatra Natural Hazards and Earth System Sciences 14 (9), 2549-2575, 2014 | 75 | 2014 |
| Earthquake induced liquefaction hazard, probability and risk assessment in the city of Kolkata, India: its historical perspective and deterministic scenario SK Nath, N Srivastava, C Ghatak, MD Adhikari, A Ghosh, SP Sinha Ray Journal of Seismology 22, 35-68, 2018 | 30 | 2018 |
| Probabilistic seismic hazard model of West Bengal, India SK Maiti, SK Nath, MD Adhikari, N Srivastava, P Sengupta, AK Gupta Journal of Earthquake Engineering 21 (7), 1113-1157, 2017 | 24 | 2017 |
| CREIME: A Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation M Chakraborty, D Fenner, W Li, J Faber, K Zhou, G Ruempker, H Stoecker, ... https://arxiv.org/abs/2204.02924, 2022 | 14 | 2022 |
| EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking W Li, M Chakraborty, D Fenner, J Faber, K Zhou, G Rümpker, H Stöcker, ... Frontiers in Earth Science 10, 953007, 2022 | 12 | 2022 |
| A study on the effect of input data length on a deep-learning-based magnitude classifier M Chakraborty, W Li, J Faber, G Rümpker, H Stoecker, N Srivastava Solid Earth 13 (11), 1721-1729, 2022 | 9 | 2022 |
| EPick: Multi-Class Attention-based U-shaped Neural Network for Earthquake Detection and Seismic Phase Picking W Li, M Chakraborty, D Fenner, J Faber, K Zhou, G Ruempker, H Stoecker, ... arXiv preprint arXiv:2109.02567, 2021 | 9 | 2021 |
| A study on small magnitude seismic phase identification using 1D deep residual neural network W Li, M Chakraborty, Y Sha, K Zhou, J Faber, G Rümpker, H Stöcker, ... Artificial Intelligence in Geosciences 3, 115-122, 2022 | 7 | 2022 |
| PolarCAP–A deep learning approach for first motion polarity classification of earthquake waveforms M Chakraborty, CQ Cartaya, W Li, J Faber, G Rümpker, H Stoecker, ... Artificial Intelligence in Geosciences 3, 46-52, 2022 | 7 | 2022 |
| Automated seismo-volcanic event detection applied to stromboli (Italy) D Fenner, G Rümpker, W Li, M Chakraborty, J Faber, J Köhler, H Stöcker, ... Frontiers in Earth Science 10, 2022 | 6 | 2022 |
| Deep Learning-based Small Magnitude Earthquake Detection and Seismic Phase Classification W Li, Y Sha, K Zhou, J Faber, G Ruempker, H Stoecker, N Srivastava https://arxiv.org/abs/2204.02870, 2022 | 5 | 2022 |
| Real time magnitude classification of earthquake waveforms using deep learning M Chakraborty, G Rümpker, H Stöcker, W Li, J Faber, D Fenner, K Zhou, ... EGU general assembly conference abstracts, EGU21-15941, 2021 | 5 | 2021 |
| Volcano-seismic event classification using wavelet scattering transforms P Laumann, N Srivastava, W Li, G Ruempker EGU General Assembly Conference Abstracts, EGU-17117, 2023 | 3 | 2023 |
| Real-time Earthquake Monitoring using Deep Learning: a case study on Turkey Earthquake Aftershock Sequence W Li, J Koehler, M Chakraborty, C Quinteros-Cartaya, G Ruempker, ... arXiv preprint arXiv:2211.09539, 2022 | 3* | 2022 |
| Exploring a CNN Model for Earthquake Magnitude Estimation using HR-GNSS data CQ Cartaya, J Koehler, W Li, J Faber, N Srivastava arXiv preprint arXiv:2304.09912, 2023 | 2 | 2023 |
| Sunda-arc seismicity: continuing increase of high-magnitude earthquakes since 2004 N Srivastava, J Köhler, FA Nava, O El Sayed, M Chakraborty, ... https://arxiv.org/abs/2108.06557, 2021 | 2 | 2021 |
| Mca-unet: Multi-class attention-aware u-net for seismic phase picking W Li, G Rümpker, H Stöcker, M Chakraborty, D Fenner, J Faber, K Zhou, ... EGU general assembly conference abstracts, EGU21-15841, 2021 | 2 | 2021 |
| Amplitude and inter-event time statistics for the island volcanoes Stromboli, Mount Etna, Yasur, and Whakaari D Fenner, G Rümpker, P Laumann, N Srivastava Frontiers in Earth Science 11, 1228103, 2023 | 1 | 2023 |
| AWESAM: A Python Module for Automated Volcanic Event Detection Applied to Stromboli D Fenner, G Ruempker, W Li, M Chakraborty, J Faber, J Koehler, ... https://arxiv.org/abs/2111.01513, 2021 | 1 | 2021 |
| Feasibility of Deep Learning in Shear Wave Splitting analysis using Synthetic-Data Training and Waveform Deconvolution M Chakraborty, G Rümpker, W Li, J Faber, N Srivastava, F Link Seismica 3 (1), 2024 | | 2024 |
Georg RümpkerProfessor of Geophysics, Goethe University FrankfurtVerified email at geophysik.uni-frankfurt.de
