84 citations as recorded by crossref.

1. Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. https://doi.org/10.1073/pnas.2219573120
2. Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings A. Szenicer et al. https://doi.org/10.1002/rse2.242
3. RCNN: Recurrent Convolutional Neural Network for Solving 3-D Wave Equation C. Li et al. https://doi.org/10.1109/LGRS.2023.3328421
4. High-frequency wavefield extrapolation using the Fourier neural operator C. Song & Y. Wang https://doi.org/10.1093/jge/gxac016
5. Executing realistic earthquake simulations in unreal engine with material calibration Y. Sun et al. https://doi.org/10.1016/j.cag.2024.104091
6. Physics-informed neural networks for transcranial ultrasound wave propagation L. Wang et al. https://doi.org/10.1016/j.ultras.2023.107026
7. Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science F. Yang et al. https://doi.org/10.1016/j.earscirev.2024.104941
8. Approximation of modal wavenumbers and group speeds in an oceanic waveguide using a neural network A. Varon et al. https://doi.org/10.1121/10.0019704
9. Ground Motion Simulation Via Generative Adversarial Network K. Chen et al. https://doi.org/10.1007/s11770-025-1154-7
10. Deep learning, machine learning and internet of things in geophysical engineering applications: An overview K. Dimililer et al. https://doi.org/10.1016/j.micpro.2020.103613
11. Stable Model Reduction for Time‐Domain Room Acoustics: A Structure‐Preserving Formulation for Complex Boundaries S. Bonthu et al. https://doi.org/10.1002/nme.70295
12. Deep neural network reducing numerical dispersion for post-processing of seismic modeling results K. Gadylshina et al. https://doi.org/10.18303/2619-1563-2022-1-99
13. Deep attributes: innovative LSTM-based seismic attributes G. Roncoroni et al. https://doi.org/10.1093/gji/ggae053
14. Scientific Machine Learning for Guided Wave and Surface Acoustic Wave (SAW) Propagation: PgNN, PeNN, PINN, and Neural Operator N. Mehtaj & S. Banerjee https://doi.org/10.3390/s25051401
15. Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks C. Song et al. https://doi.org/10.1093/gji/ggab010
16. AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis Z. Chen et al. https://doi.org/10.1016/j.tws.2023.111423
17. Integration of PSPU-Net gravity inversion neural network with gravity data for enhanced 3D basement relief estimation X. Liu et al. https://doi.org/10.1190/geo2024-0150.1
18. Small-data-driven fast seismic simulations for complex media using physics-informed Fourier neural operators W. Wei & L. Fu https://doi.org/10.1190/geo2021-0573.1
19. Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau Y. Chen et al. https://doi.org/10.1029/2023JB027378
20. On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling G. Ma et al. https://doi.org/10.1190/geo2023-0368.1
21. Multitasking neural network to jointly map discrete fracture structures and matrix transmissivity by inverting hydraulic data acquired in 2D fractured aquifers. XNET-fracture M. Vu & A. Jardani https://doi.org/10.1016/j.advwatres.2023.104463
22. DD-Net: Dual Decoder Network With Curriculum Learning for Full Waveform Inversion X. Zhang et al. https://doi.org/10.1109/TGRS.2024.3358492
23. Data-driven model discovery for plasma turbulence modelling I. Abramovic et al. https://doi.org/10.1017/S0022377822001192
24. Deep-neural-networks-based approaches for Biot–squirt model in rock physics F. Xiong et al. https://doi.org/10.1007/s11600-022-00740-8
25. Fast Forward Modeling of 3-D Gravity Data for Curved Hexahedral Grid Based on Neural Network H. He et al. https://doi.org/10.1109/LGRS.2025.3557187
26. Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN) C. Lu & C. Zhang https://doi.org/10.3390/app132413312
27. Efficient low-fidelity aeroacoustic permanence calculation of propellers F. Yunus et al. https://doi.org/10.1016/j.ast.2022.107438
28. Modeling the Asymptotic Behavior of Higher Order Aftershocks with Deep Learning L. Mizrahi & D. Jozinović https://doi.org/10.1785/0220240028
29. PGVnet: a machine learning framework for the generation of rapid, physics-consistent PGV maps F. Ramadan et al. https://doi.org/10.1093/gji/ggag008
30. SeismicTransformer: An attention-based deep learning method for the simulation of seismic wavefields Y. Xiang et al. https://doi.org/10.1016/j.cageo.2024.105629
31. Deep Learning for 3-D Inversion of Gravity Data L. Zhang et al. https://doi.org/10.1109/TGRS.2021.3110606
32. Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations B. Moseley et al. https://doi.org/10.1007/s10444-023-10065-9
33. Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multistage Training Q. Kong et al. https://doi.org/10.1785/0220250085
34. Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT M. Vu & A. Jardani https://doi.org/10.1093/gji/ggab024
35. High-Precision Intelligence Denoising of Potential Field Data Based on RevU-Net Z. Zhou et al. https://doi.org/10.1109/LGRS.2023.3241295
36. Real-time reconstruction of ground motion during small magnitude earthquakes: A pilot study Y. Kim et al. https://doi.org/10.1016/j.soildyn.2025.109707
37. Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks Z. Wang et al. https://doi.org/10.1029/2022WR033895
38. Extended Particle Weak-Form-Based Neural Networks for Seismic Modeling K. Chang et al. https://doi.org/10.1109/LGRS.2025.3550958
39. 3-D Joint Inversion of Gravity and Magnetic Data Based on a Deep Learning Network With Automatic Recognition of Structural Similarity Z. Zhou et al. https://doi.org/10.1109/TGRS.2023.3330988
40. Reduced-order modelling for complex three-dimensional seismic wave propagation J. Rekoske et al. https://doi.org/10.1093/gji/ggaf049
41. Generalizable data driven full waveform inversion for complex structures and severe topographies M. Saadat et al. https://doi.org/10.1016/j.petsci.2024.05.002
42. Simulation and application of two-dimensional seismic wavefields with random sources using Fourier Neural Operator Z. Zhu et al. https://doi.org/10.1016/j.engappai.2025.112773
43. Seismic Wave Propagation and Inversion with Neural Operators Y. Yang et al. https://doi.org/10.1785/0320210026
44. Physics-informed differentiable method for piano modeling R. Simionato et al. https://doi.org/10.3389/frsip.2023.1276748
45. A deep learning approach to broadband modal propagation in various shallow water waveguides A. Varon et al. https://doi.org/10.1121/10.0043177
46. Prediction of seismic topographic effects from DEM data via U-Net deep learning model Y. Li & H. Zhou https://doi.org/10.1140/epjp/s13360-025-07235-7
47. Simulating Multicomponent Elastic Seismic Wavefield Using Deep Learning C. Song et al. https://doi.org/10.1109/LGRS.2023.3250522
48. Deep neural Helmholtz operators for 3-D elastic wave propagation and inversion C. Zou et al. https://doi.org/10.1093/gji/ggae342
49. Accelerating Bayesian microseismic event location with deep learning A. Spurio Mancini et al. https://doi.org/10.5194/se-12-1683-2021
50. Rapid Seismic Waveform Modeling and Inversion With Neural Operators Y. Yang et al. https://doi.org/10.1109/TGRS.2023.3264210
51. Multi-task neural network in hydrological tomography to map the transmissivity and storativity simultaneously: HT-XNET M. Vu & A. Jardani https://doi.org/10.1016/j.jhydrol.2022.128167
52. A versatile framework to solve the Helmholtz equation using physics-informed neural networks C. Song et al. https://doi.org/10.1093/gji/ggab434
53. Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography M. Aleardi et al. https://doi.org/10.1002/nsg.12211
54. Bayesian Geophysical Inversion Using Invertible Neural Networks X. Zhang & A. Curtis https://doi.org/10.1029/2021JB022320
55. Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN T. Xu et al. https://doi.org/10.3390/rs15123120
56. Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions M. Rasht‐Behesht et al. https://doi.org/10.1029/2021JB023120
57. Machine Learning in Earthquake Seismology S. Mousavi & G. Beroza https://doi.org/10.1146/annurev-earth-071822-100323
58. Wavefield Reconstruction Inversion via Physics-Informed Neural Networks C. Song & T. Alkhalifah https://doi.org/10.1109/TGRS.2021.3123122
59. A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning S. Wang et al. https://doi.org/10.1007/s11004-022-10027-9
60. One-dimensional magnetotelluric parallel inversion using a ResNet1D-8 residual neural network W. Ling et al. https://doi.org/10.1016/j.cageo.2023.105454
61. Three-Dimensional High-Fidelity Surrogate Modeling of Frequency-Domain Airborne Electromagnetics Based on Hybrid DeepONet-Fourier Neural Operator Z. Ke et al. https://doi.org/10.1109/TAP.2025.3623249
62. Prediction of three-dimensional stress in fracture zones under multi-water-level steady-state conditions based on deep learning W. Jiang et al. https://doi.org/10.1007/s10596-026-10418-5
63. Implementation of a physics-informed neural network to predict relative densities for copper LPBF with a green laser system M. Schäfle et al. https://doi.org/10.1108/RPJ-08-2024-0343
64. Characterizing irregularly shaped rockfall dynamics using seismic signals: Geometric source effects combined with deep learning-driven strategy Z. Li et al. https://doi.org/10.1016/j.jrmge.2026.03.005
65. Systematic literature review on seismic diffraction imaging G. Zakarewicz et al. https://doi.org/10.1016/j.earscirev.2024.104792
66. Numerical dispersion mitigation neural network for seismic modeling K. Gadylshin et al. https://doi.org/10.1190/geo2021-0242.1
67. Accelerating 2D and 3D frequency-domain seismic wave modeling through interpolating frequency-domain wavefields by deep learning W. Cao et al. https://doi.org/10.1190/geo2021-0435.1
68. A review of approaches for submarine landslide-tsunami hazard identification and assessment J. Roger et al. https://doi.org/10.1016/j.marpetgeo.2024.106729
69. Nonlinear seismic inversion by physics-informed Caianiello convolutional neural networks for overpressure prediction of source rocks in the offshore Xihu depression, East China Y. Cheng & L. Fu https://doi.org/10.1016/j.petrol.2022.110654
70. Predicting transmission loss in underwater acoustics using convolutional recurrent autoencoder network W. Mallik et al. https://doi.org/10.1121/10.0013894
71. Ground-Motion Evaluation of Hybrid Seismic Velocity Models R. Ajala & P. Persaud https://doi.org/10.1785/0320220022
72. Uncertainty Quantification in Intelligent-Based Electrical Resistivity Tomography Image Reconstruction With Monte Carlo Dropout Strategy L. Xixi et al. https://doi.org/10.1109/TGRS.2023.3262835
73. Instantaneous Physics‐Based Ground Motion Maps Using Reduced‐Order Modeling J. Rekoske et al. https://doi.org/10.1029/2023JB026975
74. Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks A. Sandhu et al. https://doi.org/10.3389/feart.2023.1227828
75. DEEP LEARNING-BASED NUMERICAL DISPERSION MITIGIATION IN SEISMIC MODELLING K. Gadylshina et al. https://doi.org/10.33764/2618-981X-2021-2-2-17-25
76. Deep learning for fast simulation of seismic waves in complex media B. Moseley et al. https://doi.org/10.5194/se-11-1527-2020
77. A 3D complexity-adaptive approach to explore sparsity in elastic wave propagation C. Haindl et al. https://doi.org/10.1190/geo2020-0490.1
78. Wave propagation modeling using machine learning-based finite difference scheme D. Ji et al. https://doi.org/10.1016/j.jcp.2025.113870
79. The evolution of seismic tomography in earth sciences—advancements, limitations, and its AI-enabled future (A critical review) S. Ahmed & H. Güneyli https://doi.org/10.1016/j.eqrea.2025.100425
80. Data‐Driven Design of Wave‐Propagation Models for Shale‐Oil Reservoirs Based on Machine Learning F. Xiong et al. https://doi.org/10.1029/2021JB022665
81. Supervised pre-training and prior-guided fine-tuning for deep learning-based scatter interpolation and geophysical inversion Z. Guo et al. https://doi.org/10.1093/gji/ggaf200
82. An acceleration method for elastic wave forward modeling based on 1D convolution operator B. Chen et al. https://doi.org/10.1016/j.cageo.2025.106100
83. Development and application of neural network technology in solving geodynamic problems O. Maximova https://doi.org/10.21285/2227-2917-2023-3-516-525
84. Seismic Wavefields Modeling With Variable Horizontally Layered Velocity Models via Velocity-Encoded PINN J. Zou et al. https://doi.org/10.1109/TGRS.2024.3411472