69 citations as recorded by crossref.

1. Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. 10.1073/pnas.2219573120
2. Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings A. Szenicer et al. 10.1002/rse2.242
3. RCNN: Recurrent Convolutional Neural Network for Solving 3-D Wave Equation C. Li et al. 10.1109/LGRS.2023.3328421
4. High-frequency wavefield extrapolation using the Fourier neural operator C. Song & Y. Wang 10.1093/jge/gxac016
5. Executing realistic earthquake simulations in unreal engine with material calibration Y. Sun et al. 10.1016/j.cag.2024.104091
6. Physics-informed neural networks for transcranial ultrasound wave propagation L. Wang et al. 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. 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. 10.1121/10.0019704
9. Ground Motion Simulation Via Generative Adversarial Network K. Chen et al. 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. 10.1016/j.micpro.2020.103613
11. Deep neural network reducing numerical dispersion for post-processing of seismic modeling results K. Gadylshina et al. 10.18303/2619-1563-2022-1-99
12. Deep attributes: innovative LSTM-based seismic attributes G. Roncoroni et al. 10.1093/gji/ggae053
13. Scientific Machine Learning for Guided Wave and Surface Acoustic Wave (SAW) Propagation: PgNN, PeNN, PINN, and Neural Operator N. Mehtaj & S. Banerjee 10.3390/s25051401
14. Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks C. Song et al. 10.1093/gji/ggab010
15. AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis Z. Chen et al. 10.1016/j.tws.2023.111423
16. Integration of PSPU-Net gravity inversion neural network with gravity data for enhanced 3D basement relief estimation X. Liu et al. 10.1190/geo2024-0150.1
17. Small-data-driven fast seismic simulations for complex media using physics-informed Fourier neural operators W. Wei & L. Fu 10.1190/geo2021-0573.1
18. Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau Y. Chen et al. 10.1029/2023JB027378
19. On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling G. Ma et al. 10.1190/geo2023-0368.1
20. 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 10.1016/j.advwatres.2023.104463
21. DD-Net: Dual Decoder Network With Curriculum Learning for Full Waveform Inversion X. Zhang et al. 10.1109/TGRS.2024.3358492
22. Data-driven model discovery for plasma turbulence modelling I. Abramovic et al. 10.1017/S0022377822001192
23. Deep-neural-networks-based approaches for Biot–squirt model in rock physics F. Xiong et al. 10.1007/s11600-022-00740-8
24. Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN) C. Lu & C. Zhang 10.3390/app132413312
25. Efficient low-fidelity aeroacoustic permanence calculation of propellers F. Yunus et al. 10.1016/j.ast.2022.107438
26. Modeling the Asymptotic Behavior of Higher Order Aftershocks with Deep Learning L. Mizrahi & D. Jozinović 10.1785/0220240028
27. SeismicTransformer: An attention-based deep learning method for the simulation of seismic wavefields Y. Xiang et al. 10.1016/j.cageo.2024.105629
28. Deep Learning for 3-D Inversion of Gravity Data L. Zhang et al. 10.1109/TGRS.2021.3110606
29. Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations B. Moseley et al. 10.1007/s10444-023-10065-9
30. Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT M. Vu & A. Jardani 10.1093/gji/ggab024
31. High-Precision Intelligence Denoising of Potential Field Data Based on RevU-Net Z. Zhou et al. 10.1109/LGRS.2023.3241295
32. Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks Z. Wang et al. 10.1029/2022WR033895
33. 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. 10.1109/TGRS.2023.3330988
34. Reduced-order modelling for complex three-dimensional seismic wave propagation J. Rekoske et al. 10.1093/gji/ggaf049
35. Generalizable data driven full waveform inversion for complex structures and severe topographies M. Saadat et al. 10.1016/j.petsci.2024.05.002
36. Seismic Wave Propagation and Inversion with Neural Operators Y. Yang et al. 10.1785/0320210026
37. Physics-informed differentiable method for piano modeling R. Simionato et al. 10.3389/frsip.2023.1276748
38. Simulating Multicomponent Elastic Seismic Wavefield Using Deep Learning C. Song et al. 10.1109/LGRS.2023.3250522
39. Deep neural Helmholtz operators for 3-D elastic wave propagation and inversion C. Zou et al. 10.1093/gji/ggae342
40. Accelerating Bayesian microseismic event location with deep learning A. Spurio Mancini et al. 10.5194/se-12-1683-2021
41. Rapid Seismic Waveform Modeling and Inversion With Neural Operators Y. Yang et al. 10.1109/TGRS.2023.3264210
42. Multi-task neural network in hydrological tomography to map the transmissivity and storativity simultaneously: HT-XNET M. Vu & A. Jardani 10.1016/j.jhydrol.2022.128167
43. A versatile framework to solve the Helmholtz equation using physics-informed neural networks C. Song et al. 10.1093/gji/ggab434
44. Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography M. Aleardi et al. 10.1002/nsg.12211
45. Bayesian Geophysical Inversion Using Invertible Neural Networks X. Zhang & A. Curtis 10.1029/2021JB022320
46. Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN T. Xu et al. 10.3390/rs15123120
47. Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions M. Rasht‐Behesht et al. 10.1029/2021JB023120
48. Machine Learning in Earthquake Seismology S. Mousavi & G. Beroza 10.1146/annurev-earth-071822-100323
49. Wavefield Reconstruction Inversion via Physics-Informed Neural Networks C. Song & T. Alkhalifah 10.1109/TGRS.2021.3123122
50. A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning S. Wang et al. 10.1007/s11004-022-10027-9
51. One-dimensional magnetotelluric parallel inversion using a ResNet1D-8 residual neural network W. Ling et al. 10.1016/j.cageo.2023.105454
52. Systematic literature review on seismic diffraction imaging G. Zakarewicz et al. 10.1016/j.earscirev.2024.104792
53. Numerical dispersion mitigation neural network for seismic modeling K. Gadylshin et al. 10.1190/geo2021-0242.1
54. Accelerating 2D and 3D frequency-domain seismic wave modeling through interpolating frequency-domain wavefields by deep learning W. Cao et al. 10.1190/geo2021-0435.1
55. A review of approaches for submarine landslide-tsunami hazard identification and assessment J. Roger et al. 10.1016/j.marpetgeo.2024.106729
56. 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 10.1016/j.petrol.2022.110654
57. Predicting transmission loss in underwater acoustics using convolutional recurrent autoencoder network W. Mallik et al. 10.1121/10.0013894
58. Ground-Motion Evaluation of Hybrid Seismic Velocity Models R. Ajala & P. Persaud 10.1785/0320220022
59. Uncertainty Quantification in Intelligent-Based Electrical Resistivity Tomography Image Reconstruction With Monte Carlo Dropout Strategy L. Xixi et al. 10.1109/TGRS.2023.3262835
60. Instantaneous Physics‐Based Ground Motion Maps Using Reduced‐Order Modeling J. Rekoske et al. 10.1029/2023JB026975
61. Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks A. Sandhu et al. 10.3389/feart.2023.1227828
62. DEEP LEARNING-BASED NUMERICAL DISPERSION MITIGIATION IN SEISMIC MODELLING K. Gadylshina et al. 10.33764/2618-981X-2021-2-2-17-25
63. Deep learning for fast simulation of seismic waves in complex media B. Moseley et al. 10.5194/se-11-1527-2020
64. A 3D complexity-adaptive approach to explore sparsity in elastic wave propagation C. Haindl et al. 10.1190/geo2020-0490.1
65. Wave propagation modeling using machine learning-based finite difference scheme D. Ji et al. 10.1016/j.jcp.2025.113870
66. Data‐Driven Design of Wave‐Propagation Models for Shale‐Oil Reservoirs Based on Machine Learning F. Xiong et al. 10.1029/2021JB022665
67. Development and application of neural network technology in solving geodynamic problems O. Maximova 10.21285/2227-2917-2023-3-516-525
68. Seismic Wavefields Modeling With Variable Horizontally Layered Velocity Models via Velocity-Encoded PINN J. Zou et al. 10.1109/TGRS.2024.3411472
69. Integration of PSPU-Net GI Neural Network with Gravity Data for Enhanced 3D Basement Relief Estimation X. Liu et al. 10.1190/geo2024-0150.1