20.7.3.11.2 Seismic Inversion, Seismic Data Processing

Chapter Contents (Back)
Seismic Processing. Seismic Inversion. Seismology. Application, Seismic.

Fornaro, G., Atzori, S., Calo, F., Reale, D., Salvi, S.,
Inversion of Wrapped Differential Interferometric SAR Data for Fault Dislocation Modeling,
GeoRS(50), No. 6, June 2012, pp. 2175-2184.
IEEE DOI 1205
BibRef

Yang, S., Wang, J., Zhou, J., Zhu, T., Wang, H.,
An Efficient Algorithm of Both Frechet Derivative and Inversion of MCIL Data in a Deviated Well in a Horizontally Layered TI Formation Based on TLM Modeling,
GeoRS(52), No. 11, November 2014, pp. 6911-6923.
IEEE DOI 1407
Computational modeling BibRef

Shin, Y.H.[Young Hong], Shum, C.K., Braitenberg, C.[Carla],
Estimating the 3D fold structure of the crust-mantle boundary,
SPIE(Newsroom), November 23, 2015
DOI Link 1602
Deep-seated lithospheric folding can be revealed using a method that combines gravity inversion calculations and isostatic analyses. BibRef

Gao, Z., Pan, Z., Gao, J.,
Multimutation Differential Evolution Algorithm and Its Application to Seismic Inversion,
GeoRS(54), No. 6, June 2016, pp. 3626-3636.
IEEE DOI 1606
evolutionary computation BibRef

Gao, Z., Pan, Z., Gao, J., Wu, R.,
Frequency Controllable Envelope Operator and Its Application in Multiscale Full-Waveform Inversion,
GeoRS(57), No. 2, February 2019, pp. 683-699.
IEEE DOI 1901
Frequency control, Computational modeling, Data models, Optimization methods, Estimation, Inverse problems, Cycle skipping, multiscale scheme BibRef

Yu, Z., Zhou, J., Fang, Y., Hu, Y., Liu, Q.H.,
Through-Casing Hydraulic Fracture Evaluation by Induction Logging II: The Inversion Algorithm and Experimental Validations,
GeoRS(55), No. 2, February 2017, pp. 1189-1198.
IEEE DOI 1702
fast Fourier transforms BibRef

Al-Battal, A.F., Mousa, W.A.,
The Design of 2-D Explicit Depth Extrapolators Using the Cauchy Norm,
GeoRS(55), No. 5, May 2017, pp. 3029-3036.
IEEE DOI 1705
hydrocarbon reservoirs, 2-D explicit depth extrapolators, Cauchy norm, Marmousi model data set, SEG/EAGE salt model, adaptive damping, coefficients yielding, depth migration, filter coefficients, paper, extrapolators, poststack depth migrations, recursive migration process, wavefield extrapolations, Design methodology, Extrapolation, Finite impulse response filters, Frequency-domain analysis, Imaging, Inverse problems, Passband, Cauchy norm, finite impulse response (FIR) filters, regularized least square (RLS), seismic imaging, wavefield, extrapolation BibRef

Bordignon, F.L., de Figueiredo, L.P., Azevedo, L., Soares, A., Roisenberg, M., Neto, G.S.,
Hybrid Global Stochastic and Bayesian Linearized Acoustic Seismic Inversion Methodology,
GeoRS(55), No. 8, August 2017, pp. 4457-4464.
IEEE DOI 1708
Bayes methods, Computational modeling, Correlation, Covariance matrices, Data models, Stochastic processes, Uncertainty, Bayesian inversion, geostatistics, linearized inversion, seismic inversion, stochastic inversion, uncertainty, modeling BibRef

Lan, T., Liu, H., Liu, N., Li, J., Han, F., Liu, Q.H.,
Joint Inversion of Electromagnetic and Seismic Data Based on Structural Constraints Using Variational Born Iteration Method,
GeoRS(56), No. 1, January 2018, pp. 436-445.
IEEE DOI 1801
Green's function methods, fast Fourier transforms, finite difference methods, geophysical signal processing, variational Born iteration method (VBIM) BibRef

Guo, Q., Zhang, H., Han, F., Shang, Z.,
Prestack Seismic Inversion Based on Anisotropic Markov Random Field,
GeoRS(56), No. 2, February 2018, pp. 1069-1079.
IEEE DOI 1802
Anisotropic magnetoresistance, Bayes methods, Data models, Geology, Linear programming, Markov processes, Standards, Anisotropic, seismic inverse problems BibRef

Guo, Q., Zhang, H., Cao, H., Xiao, W., Han, F.,
Hybrid Seismic Inversion Based on Multi-Order Anisotropic Markov Random Field,
GeoRS(58), No. 1, January 2020, pp. 407-420.
IEEE DOI 2001
Geology, Data models, Impedance, Reliability, Mathematical model, Acoustics, Markov processes, multi-order neighborhoods BibRef

Dagnino, D.[Daniel], Sallarès, V.[Valentí], Ranero, C.R.[César R.],
Waveform-Preserving Processing Flow of Multichannel Seismic Reflection Data for Adjoint-State Full-Waveform Inversion of Ocean Thermohaline Structure,
GeoRS(56), No. 3, March 2018, pp. 1615-1625.
IEEE DOI 1804
Butterworth filters, geophysical signal processing, oceanography, seafloor phenomena, seismic waves, seismology, BF processing, underwater acoustic propagation BibRef

Zong, Z., Wang, Y., Li, K., Yin, X.,
Broadband Seismic Inversion for Low-Frequency Component of the Model Parameter,
GeoRS(56), No. 9, September 2018, pp. 5177-5184.
IEEE DOI 1809
Frequency-domain analysis, Broadband communication, Data models, Frequency estimation, Damping, Estimation, Bayes methods, seismic inversion BibRef

Li, H., Wang, L.,
Fast Modeling and Practical Inversion of Laterolog-Type Downhole Resistivity Measurements,
GeoRS(57), No. 1, January 2019, pp. 120-127.
IEEE DOI 1901
Electrodes, Conductivity, Focusing, Tools, Velocity measurement, Current measurement, Electric potential, levenberg-marquardt (LM) method BibRef

Lan, T., Liu, N., Han, F., Liu, Q.H.,
Joint Petrophysical and Structural Inversion of Electromagnetic and Seismic Data Based on Volume Integral Equation Method,
GeoRS(57), No. 4, April 2019, pp. 2075-2086.
IEEE DOI 1904
electric field integral equations, electromagnetic wave scattering, fast Fourier transforms, variational Born iteration method (VBIM) BibRef

Rodriguez, I.A.V.[I. A. Vera],
A Heuristic-Learning Optimizer for Elastodynamic Waveform Inversion in Passive Seismics,
GeoRS(57), No. 4, April 2019, pp. 2234-2248.
IEEE DOI 1904
geophysical signal processing, geophysical techniques, inverse problems, iterative methods, particle swarm optimisation, waveform inversion BibRef

Qiu, C., Liang, B., Han, F., Liu, H., Zhu, C., Liu, N., Liu, F., Fang, G., Liu, Q.H.,
Multifrequency 3-D Inversion of GREATEM Data by BCGS-FFT-BIM,
GeoRS(57), No. 4, April 2019, pp. 2439-2448.
IEEE DOI 1904
conjugate gradient methods, fast Fourier transforms, geology, geophysical techniques, integral equations, inverse problems, volume integral equation (VIE) BibRef

Gao, Z., Pan, Z., Zuo, C., Gao, J., Xu, Z.,
An Optimized Deep Network Representation of Multimutation Differential Evolution and its Application in Seismic Inversion,
GeoRS(57), No. 7, July 2019, pp. 4720-4734.
IEEE DOI 1907
Optimization methods, Data models, Mathematical model, Training, Convergence, Deep learning, Deep learning, seismic inversion BibRef

Hu, Y.[Yong], Wu, R.S.[Ru-Shan], Han, L.G.[Li-Guo], Zhang, P.[Pan],
Joint Multiscale Direct Envelope Inversion of Phase and Amplitude in the Time-Frequency Domain,
GeoRS(57), No. 7, July 2019, pp. 5108-5120.
IEEE DOI 1907
Separate hase and amplitude. Data models, Scattering, Imaging, Frequency-domain analysis, Transforms, Optimization, Numerical models, waveform-phase BibRef

Li, G., Cai, H., Li, C.,
Alternating Joint Inversion of Controlled-Source Electromagnetic and Seismic Data Using the Joint Total Variation Constraint,
GeoRS(57), No. 8, August 2019, pp. 5914-5922.
IEEE DOI 1908
geophysical prospecting, geophysical techniques, hydrocarbon reservoirs, seismic waves, seismology, structural constraint BibRef

Huang, G.T.[Guang-Tan], Chen, X.H.[Xiao-Hong], Luo, C.[Cong], Li, X.,
Prestack Waveform Inversion by Using an Optimized Linear Inversion Scheme,
GeoRS(57), No. 8, August 2019, pp. 5716-5728.
IEEE DOI 1908
geophysical techniques, seismic waves, seismology, adaptively determined regularization weight, prestack waveform inversion (PWI) BibRef

Huang, G.T.[Guang-Tan], Chen, X.H.[Xiao-Hong], Luo, C.[Cong], Chen, Y.K.[Yang-Kang],
Mesoscopic Wave-Induced Fluid Flow Effect Extraction by Using Frequency-Dependent Prestack Waveform Inversion,
GeoRS(59), No. 8, August 2021, pp. 6510-6524.
IEEE DOI 2108
Dispersion, Rocks, Time-frequency analysis, Attenuation, Reservoirs, Oils, Inversion spectral decomposition, white model BibRef

Wang, L., Li, H., Fan, Y.,
Bayesian Inversion of Logging-While-Drilling Extra-Deep Directional Resistivity Measurements Using Parallel Tempering Markov Chain Monte Carlo Sampling,
GeoRS(57), No. 10, October 2019, pp. 8026-8036.
IEEE DOI 1910
approximation theory, Bayes methods, drilling (geotechnical), inverse problems, Markov processes, Monte Carlo methods, parallel tempering (PT) BibRef

Huang, W., Liu, J.,
Robust Seismic Image Interpolation With Mathematical Morphological Constraint,
IP(29), No. 1, 2020, pp. 819-829.
IEEE DOI 1910
Interpolation, Mathematical model, Transforms, Morphological operations, Morphology, Spatial databases, Shape, inversion problems BibRef

Li, S., Liu, B., Ren, Y., Chen, Y., Yang, S., Wang, Y., Jiang, P.,
Deep-Learning Inversion of Seismic Data,
GeoRS(58), No. 3, March 2020, pp. 2135-2149.
IEEE DOI 2003
Deep neural networks (DNNs), seismic inversion BibRef

Liu, Q.C.[Qian-Cheng], Lu, Y.M.[Yong-Ming], Zhang, H.[Hao],
Fast Single-Step Least-Squares Reverse-Time Imaging via Adaptive Matching Filters in Beams,
GeoRS(58), No. 3, March 2020, pp. 1913-1919.
IEEE DOI 2003
LSRTM. Adaptive matching filter, fast inversion BibRef

Zhang, Q.C.[Qing-Chen], Mao, W.J.[Wei-Jian], Fang, J.W.[Jin-Wei],
Elastic Full Waveform Inversion With Source-Independent Crosstalk-Free Source-Encoding Algorithm,
GeoRS(58), No. 4, April 2020, pp. 2915-2927.
IEEE DOI 2004
Crosstalk, elastic full waveform inversion (FWI), source-encoding, source-independent BibRef

Valerio, E.[Emanuela], Manzo, M.[Mariarosaria], Casu, F.[Francesco], Convertito, V.[Vincenzo], de Luca, C.[Claudio], Manunta, M.[Michele], Monterroso, F.[Fernando], Lanari, R.[Riccardo], de Novellis, V.[Vincenzo],
Seismogenic Source Model of the 2019, Mw 5.9, East-Azerbaijan Earthquake (NW Iran) through the Inversion of Sentinel-1 DInSAR Measurements,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Sun, B., Alkhalifah, T.A.,
Joint Minimization of the Mean and Information Entropy of the Matching Filter Distribution for a Robust Misfit Function in Full-Waveform Inversion,
GeoRS(58), No. 7, July 2020, pp. 4704-4720.
IEEE DOI 2006
Computational modeling, Focusing, Data models, Mathematical model, Optimization, Predictive models, Current measurement, nonlinear inversion BibRef

Fediuk, A.[Annika], Wilken, D.[Dennis], Thorwart, M.[Martin], Wunderlich, T.[Tina], Erkul, E.[Ercan], Rabbel, W.[Wolfgang],
The Applicability of an Inverse Schlumberger Array for Near-Surface Targets in Shallow Water Environments,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Wang, Y., Ge, Q., Lu, W., Yan, X.,
Well-Logging Constrained Seismic Inversion Based on Closed-Loop Convolutional Neural Network,
GeoRS(58), No. 8, August 2020, pp. 5564-5574.
IEEE DOI 2007
Impedance, Convolution, Data models, Encoding, Kernel, Convolutional neural networks, Deep learning, seismic inversion BibRef

Song, C.[Chao], Alkhalifah, T.A.[Tariq A.],
Efficient Wavefield Inversion With Outer Iterations and Total Variation Constraint,
GeoRS(58), No. 8, August 2020, pp. 5836-5846.
IEEE DOI 2007
Perturbation methods, Mathematical model, TV, Linear programming, Computational modeling, Propagation, Data models, Cycle skipping, wavefield reconstruction BibRef

Zong, J., Wo, Y., Zhou, H., Dyaur, N.,
Inversion for Salt Flank Geometry Using Transmitted P- and S-Wave Travel Times,
GeoRS(58), No. 9, September 2020, pp. 6504-6511.
IEEE DOI 2008
Geometry, Tomography, Data models, Mathematical model, Receivers, Numerical models, Geology, Joint inversion, seismic tomography, vertical seismic profiling (VSP) BibRef

Chen, G., Yang, W., Chen, S., Liu, Y., Gu, Z.,
Application of Envelope in Salt Structure Velocity Building: From Objective Function Construction to the Full-Band Seismic Data Reconstruction,
GeoRS(58), No. 9, September 2020, pp. 6594-6608.
IEEE DOI 2008
Linear programming, Data models, Image reconstruction, Buildings, Reconstruction algorithms, Inverse problems, Scattering, velocity building BibRef

Sun, M.[Minao], Jin, S.G.[Shuang-Gen],
Multiparameter Elastic Full Waveform Inversion of Ocean Bottom Seismic Four-Component Data Based on A Modified Acoustic-Elastic Coupled Equation,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Sun, M.[Minao], Jin, S.G.[Shuang-Gen], Yu, P.F.[Peng-Fei],
Elastic Least-Squares Reverse-Time Migration Based on a Modified Acoustic-Elastic Coupled Equation for OBS Four-Component Data,
GeoRS(59), No. 11, November 2021, pp. 9772-9782.
IEEE DOI 2111
Mathematical model, Perturbation methods, Imaging, Propagation, Impedance, Data models, Sun, Least-squares migration (LSM), reverse-time migration BibRef

Wu, G.L.[Guo-Li], Dong, H.F.[He-Feng], Ke, G.P.[Gan-Pan], Song, J.Q.A.[Jun-Qi-Ang],
Shear-Wave Tomography Using Ocean Ambient Noise with Interference,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Zhang, Z., Lin, Y.,
Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization,
GeoRS(58), No. 10, October 2020, pp. 6900-6913.
IEEE DOI 2009
Inverse problems, Computational modeling, Mathematical model, Generative adversarial networks, Neural networks, transfer learning BibRef

He, B., Liu, Y., Lu, H., Zhang, Z.,
Correlative Full-Intensity Waveform Inversion,
GeoRS(58), No. 10, October 2020, pp. 6983-6994.
IEEE DOI 2009
Data models, Linear programming, Frequency-domain analysis, Bandwidth, Scattering, Geology, Geophysics, Cycle-skipping, source-independent BibRef

Long, Z., Cai, H., Hu, X., Li, G., Shao, O.,
Parallelized 3-D CSEM Inversion With Secondary Field Formulation and Hexahedral Mesh,
GeoRS(58), No. 10, October 2020, pp. 6812-6822.
IEEE DOI 2009
Computational modeling, Finite element analysis, Mathematical model, Numerical models, Solid modeling, Data models, secondary field formulation BibRef

Jia, Z.A.[Zhu-Ang], Lu, W.K.[Wen-Kai],
Blind Separation of Ground-Roll Using Interband Morphological Similarity and Pattern Coding,
GeoRS(58), No. 10, October 2020, pp. 7166-7177.
IEEE DOI 2009
Task analysis, Frequency-domain analysis, Encoding, Wavelet transforms, Dictionaries, Signal to noise ratio, self-similarity BibRef

Roger, M.[Marine], Li, Z.H.[Zhen-Hong], Clarke, P.[Peter], Song, C.[Chuang], Hu, J.C.[Jyr-Ching], Feng, W.P.[Wan-Peng], Yi, L.[Lei],
Joint Inversion of Geodetic Observations and Relative Weighting: The 1999 Mw 7.6 Chi-Chi Earthquake Revisited,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Feng, D., Wang, X., Wang, X.,
New Dynamic Stochastic Source Encoding Combined With a Minmax-Concave Total Variation Regularization Strategy for Full Waveform Inversion,
GeoRS(58), No. 11, November 2020, pp. 7753-7771.
IEEE DOI 2011
Encoding, Mathematical model, TV, Stochastic processes, Data models, Linear programming, Crosstalk, variable-density acoustic equation BibRef

Zhang, Y., Zhao, Z., Nie, Z., Liu, Q.H.,
Approach on Joint Inversion of Electromagnetic and Acoustic Data Based on Structural Constraints,
GeoRS(58), No. 11, November 2020, pp. 7672-7681.
IEEE DOI 2011
Acoustics, Scattering, Mathematical model, Image reconstruction, Inverse problems, Electromagnetics, Permittivity, Joint inversion, subspace-based optimization method (SOM) BibRef

Liu, B., Li, H., Mohandes, M., Al-Shaikhi, A., Zhao, L.,
A Robust Scheme for Sparse Reflectivity Recovering From Uniformly Quantized Seismic Data,
GeoRS(58), No. 12, December 2020, pp. 8665-8673.
IEEE DOI 2012
Quantization (signal), Matching pursuit algorithms, Uncertainty, Estimation, Deconvolution, Robustness, Petroleum, seismic data quantization BibRef

Zhang, X.T.[Xiao-Tian], Jia, Z.[Zhe], Ross, Z.E.[Zachary E.], Clayton, R.W.[Robert W.],
Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning,
GeoRS(58), No. 12, December 2020, pp. 8932-8939.
IEEE DOI 2012
Dispersion, Correlation, Training, Surface waves, Machine learning, Data models, Surface treatment, Convolutional networks, surface waves BibRef

Jiang, B., Lu, W.,
Adaptive Multiple Subtraction Based on an Accelerating Iterative Curvelet Thresholding Method,
IP(30), 2021, pp. 806-821.
IEEE DOI 2012
Curvelet transform, geophysical signal processing, sparsity, iterative thresholding, signal separation BibRef

Luo, J., Wang, B., Wu, R.S., Gao, J.,
Elastic Full Waveform Inversion With Angle Decomposition and Wavefield Decoupling,
GeoRS(59), No. 1, January 2021, pp. 871-883.
IEEE DOI 2012
Scattering, Frequency-domain analysis, Perturbation methods, Acoustics, Time-domain analysis, Analytical models, wave mode decoupling BibRef

Jiang, B., Lu, W.,
Primal-Dual Optimization Strategy With Total Variation Regularization for Prestack Seismic Image Deblurring,
GeoRS(59), No. 1, January 2021, pp. 884-893.
IEEE DOI 2012
TV, Mathematical model, Imaging, Convolution, Lighting, Deconvolution, Data models, Deconvolution, nonstationary, total variation (TV) BibRef

Qian, F., Zhang, C., Feng, L., Lu, C., Zhang, G., Hu, G.,
Tubal-Sampling: Bridging Tensor and Matrix Completion in 3-D Seismic Data Reconstruction,
GeoRS(59), No. 1, January 2021, pp. 854-870.
IEEE DOI 2012
Tensors, Matrix decomposition, Periodic structures, Mathematical model, Bridges, Signal to noise ratio, Convolution, tubal sampling BibRef

Grathwohl, C.[Christine], Kunstmann, P.C.[Peer Christian], Quinto, E.T.[Eric Todd], Rieder, A.[Andreas],
Imaging with the Elliptic Radon Transform in Three Dimensions from an Analytical and Numerical Perspective,
SIIMS(13), No. 4, 2020, pp. 2250-2280.
DOI Link 2012
A linear model in seismic imaging. BibRef

Wang, D., Gao, J., Liu, N., Jiang, X.,
Structure-Oriented DTGV Regularization for Random Noise Attenuation in Seismic Data,
GeoRS(59), No. 2, February 2021, pp. 1757-1771.
IEEE DOI 2101
TV, Attenuation, Transforms, Oils, Geology, Data models, Signal to noise ratio, Gradient structure tensor (GST), variational regularization BibRef

Yuan, Y., Li, Y., Zhou, S.,
Multichannel Statistical Broadband Wavelet Deconvolution for Improving Resolution of Seismic Signals,
GeoRS(59), No. 2, February 2021, pp. 1772-1783.
IEEE DOI 2101
Deconvolution, Correlation, Receivers, Surface treatment, Signal resolution, Convolution, Surface waves, Broadband wavelet, resolution BibRef

Liu, X., Chen, X., Li, J., Chen, Y.,
Nonlocal Weighted Robust Principal Component Analysis for Seismic Noise Attenuation,
GeoRS(59), No. 2, February 2021, pp. 1745-1756.
IEEE DOI 2101
Noise reduction, Principal component analysis, Attenuation, Image reconstruction, Matrix decomposition, Linear programming, seismic data BibRef

Gao, Z., Li, C., Liu, N., Pan, Z., Gao, J., Xu, Z.,
Large-Dimensional Seismic Inversion Using Global Optimization With Autoencoder-Based Model Dimensionality Reduction,
GeoRS(59), No. 2, February 2021, pp. 1718-1732.
IEEE DOI 2101
Optimization methods, Dimensionality reduction, Decoding, Data models, Impedance, Numerical models, Autoencoder, seismic inversion BibRef

Xu, S., Su, W., Yang, D., Li, Z.,
A Application of adaptive generalized S transform in formation Q value extraction,
CVIDL20(688-692)
IEEE DOI 2102
geophysical signal processing, geophysical techniques, optimisation, seismic waves, time-frequency analysis, Teager-Kaiser energy BibRef

Adler, A., Araya-Polo, M., Poggio, T.,
Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows,
SPMag(38), No. 2, March 2021, pp. 89-119.
IEEE DOI 2103
Seismic measurements, Inverse problems, Earthquakes, Deep learning, Hydrocarbons, Hazards, Earth, Analytical models, Geophysical measurements BibRef

Yang, C.S.[Cheng-Sheng], Wang, T.[Ting], Zhu, S.[Sainan], Han, B.Q.[Bing-Quan], Dong, J.H.[Ji-Hong], Zhao, C.Y.[Chao-Ying],
Co-Seismic Inversion and Post-Seismic Deformation Mechanism Analysis of 2019 California Earthquake,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Wu, B.[Bangyu], Meng, D.[Delin], Zhao, H.X.[Hai-Xia],
Semi-Supervised Learning for Seismic Impedance Inversion Using Generative Adversarial Networks,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Zhong, T., Cheng, M., Dong, X., Li, Y.,
Seismic Random Noise Suppression by Using Adaptive Fractal Conservation Law Method Based on Stationarity Testing,
GeoRS(59), No. 4, April 2021, pp. 3588-3600.
IEEE DOI 2104
Noise reduction, Attenuation, Signal processing algorithms, Testing, Transforms, Signal to noise ratio, Data processing, stationarity testing BibRef

Wang, H., Huang, G., Chen, W., Chen, Y.,
Q-Compensated Denoising of Seismic Data,
GeoRS(59), No. 4, April 2021, pp. 3580-3587.
IEEE DOI 2104
Noise reduction, Attenuation, Convolution, Data models, Mathematical model, Noise measurement, Estimation, Denoising, Q-compensation BibRef

Liu, Y.T.[Yang-Ting], Zhong, Y.[Yu],
Machine Learning-Based Seafloor Seismic Prestack Inversion,
GeoRS(59), No. 5, May 2021, pp. 4471-4480.
IEEE DOI 2104
Training, Data models, Neurons, Mathematical model, Computational modeling, Biological neural networks, seafloor properties BibRef

Ferreira, R.S.[Rodrigo S.], Oliveira, D.A.B.[Dário A. B.], Semin, D.G.[Daniil G.], Zaytsev, S.[Semen],
Automatic Velocity Analysis Using a Hybrid Regression Approach With Convolutional Neural Networks,
GeoRS(59), No. 5, May 2021, pp. 4464-4470.
IEEE DOI 2104
Training, MOS devices, Convolution, Splines (mathematics), Stacking, Geophysics, regression analysis BibRef

Wang, S.N.[Sheng-Nan], Li, Y.[Yue], Wu, N.[Ning], Zhao, Y.X.[Yu-Xing], Yao, H.Y.[Hai-Yang],
Attribute-Based Double Constraint Denoising Network for Seismic Data,
GeoRS(59), No. 6, June 2021, pp. 5304-5316.
IEEE DOI 2106
Noise reduction, Training, Noise measurement, Task analysis, Data mining, Generative adversarial networks, seismic data BibRef

Cyz, M.[Marta], Azevedo, L.[Leonardo],
Direct Geostatistical Seismic Amplitude Versus Angle Inversion for Shale Rock Properties,
GeoRS(59), No. 6, June 2021, pp. 5335-5344.
IEEE DOI 2106
Rocks, Data models, Computational modeling, Physics, Predictive models, Stochastic processes, Reservoirs, Geostatistics, stochastic inversion BibRef

Huang, G.T.[Guang-Tan], Chen, X.H.[Xiao-Hong], Chen, Y.K.[Yang-Kang],
P-P and Dynamic Time Warped P-SV Wave AVA Joint-Inversion With l1-2 Regularization,
GeoRS(59), No. 7, July 2021, pp. 5535-5548.
IEEE DOI 2106
Reservoirs, Heuristic algorithms, Correlation, Reliability, Data mining, Data models, Rocks, l1-2-norm penalty, prestack joint inversion BibRef

Liu, C.Y.[Chun-Yy], Yang, H.F.[Hong-Feng], Wang, B.S.[Bao-Shan], Yang, J.[Jun],
Impacts of Reservoir Water Level Fluctuation on Measuring Seasonal Seismic Travel Time Changes in the Binchuan Basin, Yunnan, China,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Cheng, J.W.[Jing-Wang], Chen, W.[Wei], Zhou, L.[Li], Yang, L.Q.[Liu-Qing], Liu, Q.M.[Qi-Min], Zhang, X.[Xiang],
Deblending of Simultaneous-Source Seismic Data Using Bregman Iterative Shaping,
GeoRS(59), No. 7, July 2021, pp. 6208-6217.
IEEE DOI 2106
Transforms, Crosstalk, Iterative algorithms, Imaging, Mathematical model, Data models, Bregman iterative shaping (BIS), simultaneous-source BibRef

Ha, W.[Wansoo], Shin, C.S.[Chang-Soo],
Handling Negative Values for the Logarithmic Objective Function in Acoustic Laplace-Domain Full-Waveform Inversion Using Real Variables,
GeoRS(59), No. 7, July 2021, pp. 6218-6224.
IEEE DOI 2106
Linear programming, Damping, Data models, Numerical models, Laplace equations, Optimization methods, logarithmic objective function BibRef

Liu, H.[Hong], Yang, K.[Kunde], Yang, Q.L.[Qiu-Long],
Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
Geoacoustic analysis in shallow water. BibRef

Shi, H.Y.[Hui-Yan], Li, T.L.[Tong-Lin], Sun, R.[Rui], Zhang, G.B.[Gong-Bo], Zhang, R.Z.[Rong-Zhe], Kang, X.Z.[Xin-Ze],
Insights from the P Wave Travel Time Tomography in the Upper Mantle Beneath the Central Philippines,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Fokker, E.[Eldert], Ruigrok, E.[Elmer], Hawkins, R.[Rhys], Trampert, J.[Jeannot],
Physics-Based Relationship for Pore Pressure and Vertical Stress Monitoring Using Seismic Velocity Variations,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Ayala-Garcia, D.[Daniella], Curtis, A.[Andrew], Branicki, M.[Michal],
Seismic Interferometry from Correlated Noise Sources,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Wang, H.Z.[Hong-Zhou], Li, Y.[Yue], Dong, X.T.[Xin-Tong],
Generative Adversarial Network for Desert Seismic Data Denoising,
GeoRS(59), No. 8, August 2021, pp. 7062-7075.
IEEE DOI 2108
Noise reduction, Generators, Generative adversarial networks, Convolution, Signal to noise ratio, Training, low signal-to-noise ratios (SNRs) BibRef

Dong, X.T.[Xin-Tong], Li, Y.[Yue],
Denoising the Optical Fiber Seismic Data by Using Convolutional Adversarial Network Based on Loss Balance,
GeoRS(59), No. 12, December 2021, pp. 10544-10554.
IEEE DOI 2112
Noise reduction, Signal to noise ratio, Noise measurement, Generative adversarial networks, Optical scattering, low signal-to-noise ratio (SNR) BibRef

Zhu, H.Y.[Hui-Yu], Sun, M.Y.[Meng-Yao], Fu, H.H.[Hao-Huan], Du, N.[Nianmao], Zhang, J.[Jie],
Training a Seismogram Discriminator Based on ResNet,
GeoRS(59), No. 8, August 2021, pp. 7076-7085.
IEEE DOI 2108
Earthquakes, Task analysis, Neural networks, Geophysics, Training, Image recognition, Machine learning, seismological observations BibRef

Wang, H.[Hang], Chen, W.[Wei], Zhang, Q.[Quan], Liu, X.Y.[Xing-Ye], Zu, S.[Shaohuan], Chen, Y.K.[Yang-Kang],
Fast Dictionary Learning for High-Dimensional Seismic Reconstruction,
GeoRS(59), No. 8, August 2021, pp. 7098-7108.
IEEE DOI 2108
Dictionaries, Sparse matrices, Encoding, Mathematical model, Transforms, Singular value decomposition, Image reconstruction, signal processing BibRef

Qu, Y.M.[Ying-Ming], Huang, C.P.[Chong-Peng], Liu, C.[Chang], Li, Z.C.[Zhen-Chun],
Full-Path Compensated Least-Squares Reverse Time Migration of Joint Primaries and Different-Order Multiples for Deep-Marine Environment,
GeoRS(59), No. 8, August 2021, pp. 7109-7121.
IEEE DOI 2108
Attenuation, Imaging, Mathematical model, Linear programming, Seismic waves, Media, Acoustics, Deep-marine environment, viscoacoustic BibRef

Guo, Q.[Qiang], Ba, J.[Jing], Luo, C.[Cong],
Prestack Seismic Inversion With Data-Driven MRF-Based Regularization,
GeoRS(59), No. 8, August 2021, pp. 7122-7136.
IEEE DOI 2108
Maximum likelihood estimation, Uncertainty, Inverse problems, Geology, Simulated annealing, Linear programming, prestack seismic inversion BibRef

Chen, H.M.[Han-Ming], Zhou, H.[Hui], Rao, Y.[Ying],
Source Wavefield Reconstruction in Fractional Laplacian Viscoacoustic Wave Equation-Based Full Waveform Inversion,
GeoRS(59), No. 8, August 2021, pp. 6496-6509.
IEEE DOI 2108
Propagation, Laplace equations, Time-domain analysis, Receivers, Mathematical model, Correlation, Graphics processing units, wavefield reconstruction BibRef

Oliveira, D.A.B.[Dário A. B.], Semin, D.G.[Daniil G.], Zaytsev, S.[Semen],
Self-Supervised Ground-Roll Noise Attenuation Using Self-Labeling and Paired Data Synthesis,
GeoRS(59), No. 8, August 2021, pp. 7147-7159.
IEEE DOI 2108
Training, Attenuation, Noise reduction, Pipelines, Noise measurement, Data models, Geology, Deep learning, geophysical image processing, self-supervised learning BibRef

Ao, Y.[Yile], Lu, W.K.[Wen-Kai], Jiang, B.[Bowu], Monkam, P.[Patrice],
Seismic Structural Curvature Volume Extraction With Convolutional Neural Networks,
GeoRS(59), No. 9, September 2021, pp. 7370-7384.
IEEE DOI 2109
Feature extraction, Estimation, Deformable models, Surface topography, Surface impedance, Strain, structural curvature BibRef

Yang, L.Q.[Liu-Qing], Chen, W.[Wei], Wang, H.[Hang], Chen, Y.K.[Yang-Kang],
Deep Learning Seismic Random Noise Attenuation via Improved Residual Convolutional Neural Network,
GeoRS(59), No. 9, September 2021, pp. 7968-7981.
IEEE DOI 2109
Attenuation, Transforms, Training, Task analysis, Noise reduction, Signal to noise ratio, Convolution, transfer learning BibRef

Guo, R.[Rui], Yao, H.M.[He Ming], Li, M.[Maokun], Ng, M.K.P.[Michael Kwok Po], Jiang, L.J.[Li-Jun], Abubakar, A.[Aria],
Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint,
GeoRS(59), No. 9, September 2021, pp. 7982-7995.
IEEE DOI 2109
Training, Deep learning, Knowledge engineering, Conductivity, Data models, Numerical models, Space exploration, velocity BibRef

Zhou, Y.X.[Yan-Xin], Wang, R.[Runqiu], Huang, W.[Weilin],
Surface Diffraction Noise Attenuation for Marine Seismic Data Processing With Mathematical Morphological Filtering,
GeoRS(59), No. 9, September 2021, pp. 8007-8021.
IEEE DOI 2109
Surface morphology, Trajectory, Diffraction, Data processing, Petroleum, Surface treatment, Attenuation, surface diffraction noise (SDN) BibRef

Li, Y.S.[Yin-Shuo], Song, J.Y.[Jian-Yong], Lu, W.K.[Wen-Kai], Monkam, P.[Patrice], Ao, Y.[Yile],
Multitask Learning for Super-Resolution of Seismic Velocity Model,
GeoRS(59), No. 9, September 2021, pp. 8022-8033.
IEEE DOI 2109
Task analysis, Image edge detection, Computational modeling, Deep learning, Convolution, Correlation, Deep learning, edge image, super-resolution (SR) BibRef

Fang, Z.L.[Zhi-Long], Demanet, L.[Laurent],
Lift and Relax for PDE-Constrained Inverse Problems in Seismic Imaging,
GeoRS(59), No. 9, September 2021, pp. 8034-8039.
IEEE DOI 2109
Optimization, Linear programming, Mathematical model, Numerical models, Data models, Atmospheric modeling, Receivers, surface and subsurface properties BibRef

Meyers, P.M.[Patrick M.], Prestegard, T.[Tanner], Mandic, V.[Vuk], Tsai, V.C.[Victor C.], Bowden, D.C.[Daniel C.], Matas, A.[Andrew], Pavlis, G.[Gary], Caton, R.[Ross],
A Linear Inversion Approach to Measuring the Composition and Directionality of the Seismic Noise Field,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, W.[Wei], Gao, J.H.[Jing-Huai], Gao, Z.Q.[Zhao-Qi], Chen, H.L.[Hong-Ling],
Adjoint-Driven Deep-Learning Seismic Full-Waveform Inversion,
GeoRS(59), No. 10, October 2021, pp. 8913-8932.
IEEE DOI 2109
Inverse problems, Linear programming, Image reconstruction, Data models, Mathematical model, Training, Reliability, inverse problem BibRef

Zhang, W.[Wei], Gao, J.H.[Jing-Huai],
Deep-Learning Full-Waveform Inversion Using Seismic Migration Images,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI 2112
Image reconstruction, Data models, Neural networks, Iterative methods, Inverse problems, Tools, Electronics packaging, reverse time migration BibRef

Wang, B.F.[Ben-Feng], Li, J.K.[Jia-Kuo], Luo, J.R.[Jing-Rui], Wang, Y.Y.[Ying-Ying], Geng, J.H.[Jian-Hua],
Intelligent Deblending of Seismic Data Based on U-Net and Transfer Learning,
GeoRS(59), No. 10, October 2021, pp. 8885-8894.
IEEE DOI 2109
Feature extraction, Transforms, Training data, Training, Data mining, Deep learning, Volume measurement, Deblending, deep learning, U-net BibRef

Li, Q.[Qin], Wang, W.[Wei],
AVO Inversion in Orthotropic Media Based on SA-PSO,
GeoRS(59), No. 10, October 2021, pp. 8903-8912.
IEEE DOI 2109
Seismic inversion. amplitude variation with offset. Media, Optimization, Reservoirs, Prediction algorithms, Temperature distribution, Rocks, Particle swarm optimization, orthotropic medium BibRef

Feng, J.[Jie], Zhao, J.H.[Jian-Hu], Zheng, G.[Gen], Li, S.B.[Shao-Bo],
Horizon Picking from SBP Images Using Physicals-Combined Deep Learning,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
Seismic related, marine bottom layers. BibRef

Vargas, D.[David], Vasconcelos, I.[Ivan], Ravasi, M.[Matteo], Luiken, N.[Nick],
Time-Domain Multidimensional Deconvolution: A Physically Reliable and Stable Preconditioned Implementation,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Li, Z.X.[Zhong-Xiao], Sun, N.[Ningna], Gao, H.T.[Hao-Tian], Qin, N.[Ning], Li, Z.C.[Zhen-Chun],
Adaptive Subtraction Based on U-Net for Removing Seismic Multiples,
GeoRS(59), No. 11, November 2021, pp. 9796-9812.
IEEE DOI 2111
Adaptation models, Data models, Training, Mathematical model, Kernel, Computational modeling, Minimization, Adaptive subtraction, U-net BibRef

Yu, H.[Han], Chen, Y.Q.[Yu-Qing], Hanafy, S.M.[Sherif M.], Schuster, G.T.[Gerard T.],
Skeletonized Wave-Equation Refraction Inversion With Autoencoded Waveforms,
GeoRS(59), No. 10, October 2021, pp. 8210-8227.
IEEE DOI 2109
Training, Neural networks, Feature extraction, Decoding, Data models, Telecommunications, Tools, Autoencoder, refractions, skeletonization, waveform inversion BibRef

Chen, G.X.[Guo-Xin], Yang, W.[Wencai], Liu, Y.[Yanan], Luo, J.R.[Jing-Rui], Jing, H.[Hao],
Envelope-Based Sparse-Constrained Deconvolution for Velocity Model Building,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Deconvolution, Data models, Buildings, Seismic waves, Optimization, Numerical models, Linear programming, Envelope, velocity model building BibRef

Ao, Y.[Yile], Lu, W.K.[Wen-Kai], Xu, P.C.[Peng-Cheng], Jiang, B.[Bowu],
Seismic Dip Estimation With a Domain Knowledge Constrained Transfer Learning Approach,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Estimation, Transfer learning, Task analysis, Robustness, Deep learning, Azimuth, Tensors, Convolutional neural network, transfer learning BibRef

Zhang, C.[Chao], van der Baan, M.[Mirko],
Seismic Signal Matching and Complex Noise Suppression by Zernike Moments and Trilateral Weighted Sparse Coding,
GeoRS(60), 2022, pp. 1-10.
IEEE DOI 2112
Noise reduction, Encoding, Sparse matrices, Euclidean distance, Signal to noise ratio, Correlation, Noise measurement, Zernike moments BibRef

Zhao, Y.X.[Yu-Xing], Li, Y.[Yue], Wu, N.[Ning],
Distributed Acoustic Sensing Vertical Seismic Profile Data Denoiser Based on Convolutional Neural Network,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Noise reduction, Training, Optical noise, Convolution, Interference, Optical coupling, Fading channels, Borehole seismic survey, vertical seismic profile (VSP) BibRef

Tian, X.Y.[Xing-Yu], Lu, W.K.[Wen-Kai], Li, Y.[Yanda],
Improved Anomalous Amplitude Attenuation Method Based on Deep Neural Networks,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Deep learning, Convolutional neural networks, Task analysis, Noise reduction, Noise measurement, Attenuation, seismic denoising BibRef

Othman, A.[Abdullah], Iqbal, N.[Naveed], Hanafy, S.M.[Sherif M.], Waheed, U.B.[Umair Bin],
Automated Event Detection and Denoising Method for Passive Seismic Data Using Residual Deep Convolutional Neural Networks,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Noise reduction, Neural networks, Noise measurement, Feature extraction, Event detection, Data mining, machine learning BibRef

Ma, H.T.[Hai-Tao], Wang, Y.Z.[Yu-Zhuo], Li, Y.[Yue], Zhao, Y.X.[Yu-Xing],
Desert Seismic Low-Frequency Noise Attenuation Using Low-Rank Decomposition-Based Denoising Convolutional Neural Network,
GeoRS(60), 2022, pp. 1-9.
IEEE DOI 2112
Convolution, Attenuation, Neural networks, Training, Signal to noise ratio, Noise reduction, Noise measurement, low-rank decomposition BibRef

Lin, Y.[Yi], Zhang, J.H.[Jin-Hai],
A Multispectral Denoising Framework for Seismic Random Noise Attenuation,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI 2112
Tensors, Noise measurement, Noise reduction, Transforms, Attenuation, Time-frequency analysis, Matrix decomposition, 3-D tensor, seismic data processing BibRef

Li, J.T.[Jin-Tao], Wu, X.M.[Xin-Ming], Hu, Z.X.[Zhan-Xuan],
Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Image resolution, Superresolution, Training data, Training, Noise reduction, Convolution, Computational modeling, super-resolution BibRef

Giannakis, I.[Iraklis], Giannopoulos, A.[Antonios], Warren, C.[Craig], Sofroniou, A.[Anastasia],
Fractal-Constrained Crosshole/Borehole-to-Surface Full-Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar,
GeoRS(60), 2022, pp. 1-10.
IEEE DOI 2112
Permittivity, Soil, Fractals, Computational modeling, Transmitters, Time-domain analysis, Finite difference methods, principal component analysis (PCA) BibRef

Liu, N.H.[Nai-Hao], Li, F.Y.[Fang-Yu], Wang, D.H.[De-Hua], Gao, J.H.[Jing-Huai], Xu, Z.B.[Zong-Ben],
Ground-Roll Separation and Attenuation Using Curvelet-Based Multichannel Variational Mode Decomposition,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI 2112
Transforms, Attenuation, Signal resolution, Wavelet transforms, Signal to noise ratio, Robustness, Optimization, variational mode decomposition (VMD) BibRef

Bai, Y.[Yang], Tan, M.[Maojin], Shi, Y.J.[Yu-Jiang], Zhang, H.T.[Hai-Tao], Li, G.[Gaoren],
Regression Committee Machine and Petrophysical Model Jointly Driven Parameters Prediction From Wireline Logs in Tight Sandstone Reservoirs,
GeoRS(60), 2022, pp. 1-9.
IEEE DOI 2112
Reservoirs, Training, Predictive models, Permeability, Mathematical model, Data models, Acoustics, Petrophysical models, wireline logs BibRef

Zhou, Y.T.[Ya-Tong], Yang, J.[Jian], Wang, H.[Hang], Huang, G.T.[Guang-Tan], Chen, Y.K.[Yang-Kang],
Statistics-Guided Dictionary Learning for Automatic Coherent Noise Suppression,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI 2112
Dictionaries, Transforms, Noise reduction, Measurement, Atomic measurements, Training, Signal processing algorithms, statistics BibRef

Huang, G.T.[Guang-Tan], Zhang, D.[Dong], Chen, W.[Wei], Chen, Y.K.[Yang-Kang],
Accelerated Signal-and-Noise Orthogonalization,
GeoRS(60), 2022, pp. 1-9.
IEEE DOI 2112
Noise reduction, Acceleration, Imaging, Inverse problems, Smoothing methods, Oils, Mathematical model, Denoising, seismic data processing BibRef

Yang, J.D.[Ji-Dong], Huang, J.P.[Jian-Ping], Li, Z.C.[Zhen-Chun], Zhu, H.[Hejun], McMechan, G.A.[George A.], Luo, X.[Xin],
Approximating the Gauss-Newton Hessian Using a Space-Wavenumber Filter and its Applications in Least-Squares Seismic Imaging,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Mathematical model, Imaging, Computational modeling, Receivers, Data models, Media, Geometry, Gauss-Newton Hessian (GNH), seismic imaging BibRef

Luo, C.[Cong], Huang, G.T.[Guang-Tan], Chen, X.H.[Xiao-Hong], Chen, Y.K.[Yang-Kang],
Registration-Free Multicomponent Joint AVA Inversion Using Optimal Transport,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Earth, Mathematical model, Convex functions, Transforms, Signal to noise ratio, Media, Linear approximation, registration-free BibRef

Liu, B.[Bo], Mohandes, M.[Mohamed], Nuha, H.[Hilal], Deriche, M.[Mohamed], Fekri, F.[Faramarz], McClellan, J.H.[James H.],
A Multitone Model-Based Seismic Data Compression,
SMCS(52), No. 2, February 2022, pp. 1030-1040.
IEEE DOI 2201
Data models, Parameter estimation, Transforms, Encoding, Analytical models, Redundancy, Optimization, Data compression, sinusoidal waves BibRef

Zhao, H.X.[Hai-Xia], Bai, T.T.[Ting-Ting], Wang, Z.Q.[Zhi-Qiang],
A Natural Images Pre-Trained Deep Learning Method for Seismic Random Noise Attenuation,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Han, Z.[Zhi], Yu, S.Q.[Si-Quan], Lin, S.B.[Shao-Bo], Zhou, D.X.[Ding-Xuan],
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization,
PAMI(44), No. 4, April 2022, pp. 1853-1868.
IEEE DOI 2203
Applied to earthquake seismic intensity prediction. Feature extraction, Data mining, Deep learning, Task analysis, Optimization, Machine learning algorithms, Deep nets, learning theory BibRef

Fang, P.[Peng], Zhang, J.H.[Jin-Hai],
Recursive Enhancement of Weak Subsurface Boundaries and Its Application to SHARAD Data,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Han, J.G.[Jian-Guang], Gu, B.L.[Bing-Luo], Zhu, G.H.[Guang-Hui], Liu, Z.W.[Zhi-Wei],
High-Precision Depth Domain Migration Method in Imaging of 3D Seismic Data in Coalfield,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhang, X.[Xin], Qian, Y.[Yinping], Shen, X.[Xuzhang], Huang, H.[He], Chai, H.B.[Hai-Bin],
Shallow Crustal Structure of S-Wave Velocities in the Coastal Area of South China Constrained by Receiver Function Amplitudes,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Li, F.D.[Fang-Da], Guo, Z.W.[Zhen-Wei], Pan, X.P.[Xin-Peng], Liu, J.X.[Jian-Xin], Wang, Y.Y.[Yan-Yi], Gao, D.W.[Da-Wei],
Deep Learning with Adaptive Attention for Seismic Velocity Inversion,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Wang, N.[Ning], Shi, Y.[Ying], Zhou, H.[Hui],
Accurately Stable Q-Compensated Reverse-Time Migration Scheme for Heterogeneous Viscoelastic Media,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Li, W.[Wenda], Wu, T.Q.[Tian-Qi], Liu, H.[Hong],
Structure-Preserving Random Noise Attenuation Method for Seismic Data Based on a Flexible Attention CNN,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link 2211
BibRef

Guo, Z.Q.[Zhi-Qi], Zhao, D.Y.[Dan-Yu], Liu, C.[Cai],
A New Seismic Inversion Scheme Using Fluid Dispersion Attribute for Direct Gas Identification in Tight Sandstone Reservoirs,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhao, B.H.[Bing-Hui], Han, L.G.[Li-Guo], Zhang, P.[Pan], Yin, Y.C.[Yu-Chen],
Weak Signal Enhancement for Passive Seismic Data Reconstruction Based on Deep Learning,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhu, G.[Guang], Chen, X.H.[Xiao-Hong], Li, J.Y.[Jing-Ye], Guo, K.K.[Kang-Kang],
Data-Driven Seismic Impedance Inversion Based on Multi-Scale Strategy,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Ouyang, Z.Y.[Zhi-Yuan], Zhang, L.Q.[Li-Qi], Wang, H.Z.[Hua-Zhong], Yang, K.[Kai],
High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform-Constrained Tensor CANDECOM/PARAFAC Decomposition,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhang, Z.[Zheng], Yan, Z.[Zhe], Jing, J.K.[Jian-Kun], Gu, H.[Hanming], Li, H.Y.[Hai-Ying],
Generating Paired Seismic Training Data with Cycle-Consistent Adversarial Networks,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Lin, Y.[Youzuo], Theiler, J.[James], Wohlberg, B.[Brendt],
Physics-Guided Data-Driven Seismic Inversion: Recent progress and future opportunities in full-waveform inversion,
SPMag(40), No. 1, January 2023, pp. 115-133.
IEEE DOI 2301
Seismic measurements, Uncertainty, Surface waves, Surface contamination, Measurement uncertainty, Earthquakes, Predictive models BibRef

Yin, Y.C.[Yu-Chen], Han, L.G.[Li-Guo], Zhang, P.[Pan], Lu, Z.W.[Zhan-Wu], Shang, X.[Xujia],
First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Zeng, J.W.[Jing-Wen], Han, L.G.[Li-Guo],
Sparse Inversion for the Iterative Marchenko Scheme of Irregularly Sampled Data,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Su, Y.Z.[Yi-Zhe], Wang, D.L.[De-Li], Hu, B.[Bin], Gong, X.B.[Xiang-Bo], Zhang, J.M.[Jun-Ming],
Supervirtual Refraction Interferometry in the Radon Domain,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
seismic first arrivals. BibRef

Deng, W.[Wubing], Cao, Q.S.[Qing-Song], Morozov, I.B.[Igor B.], Fu, L.Y.[Li-Yun],
Seismic-Q Compensation by Iterative Time-Domain Deconvolution,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Wu, C.L.[Cheng-Liang], Feng, B.[Bo], Song, X.N.[Xiao-Nan], Wang, H.Z.[Hua-Zhong], Xu, R.W.[Rong-Wei], Sheng, S.[Shen],
Automatic Horizon Picking Using Multiple Seismic Attributes and Markov Decision Process,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Zhang, P.[Pan], Wu, R.S.[Ru-Shan], Han, L.G.[Li-Guo], Zhou, Y.X.[Yi-Xiu],
Strong-Scattering Multiparameter Reconstruction Based on Elastic Direct Envelope Inversion and Full-Waveform Inversion with Anisotropic Total Variation Constraint,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Borcea, L.[Liliana], Garnier, J.[Josselin], Mamonov, A.V.[Alexander V.], Zimmerling, J.[Jorn],
Waveform Inversion with a Data Driven Estimate of the Internal Wave,
SIIMS(16), No. 1, 2023, pp. 280-312.
DOI Link 2302
BibRef

Gu, Z.Y.[Zhi-Yuan], Chai, X.[Xintao], Yang, T.[Taihui],
Deep-Learning-Based Low-Frequency Reconstruction in Full-Waveform Inversion,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Lv, Q.Z.[Qing-Zhou], Liu, W.[Wanzeng], Li, R.[Ran], Yang, H.[Hui], Tao, Y.[Yuan], Wang, M.J.[Meng-Jiao],
Classification of Seismaesthesia Information and Seismic Intensity Assessment by Multi-Model Coupling,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Tao, L.R.[Liu-Rong], Ren, H.R.[Hao-Ran], Gu, Z.W.[Zhi-Wei],
Acoustic Impedance Inversion from Seismic Imaging Profiles Using Self Attention U-Net,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Luo, C.[Cong], Ba, J.[Jing], Guo, Q.[Qiang],
Sequential Seismic Anisotropic Inversion for VTI Media with Simulated Annealing Algorithm Aided by Adaptive Setting of Optimization Parameters,
RS(15), No. 7, 2023, pp. 1891.
DOI Link 2304
BibRef

Yan, D.[Dong], Tian, Y.[You], Li, Z.Q.[Zhi-Qiang], Li, H.L.[Hong-Li],
Upper Mantle Velocity Structure Beneath the Yarlung-Tsangpo Suture Revealed by Teleseismic P-Wave Tomography,
RS(15), No. 11, 2023, pp. 2724.
DOI Link 2306
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Parasyris, A.[Apostolos], Stankovic, L.[Lina], Stankovic, V.[Vladimir],
Synthetic Data Generation for Deep Learning-Based Inversion for Velocity Model Building,
RS(15), No. 11, 2023, pp. 2901.
DOI Link 2306
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Nosov, M.A.[Mikhail A.], Kolesov, S.V.[Sergey V.], Sementsov, K.A.[Kirill A.],
Interpretation of Signals Recorded by Ocean-Bottom Pressure Gauges during the Passage of Atmospheric Lamb Wave on 15 January 2022,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Li, J.[Jun], Yin, C.C.[Chang-Chun], Liu, Y.H.[Yun-He], Wang, L.Y.[Lu-Yuan], Ma, X.P.[Xin-Peng],
Simulation of Seismoelectric Waves Using Time-Domain Finite-Element Method in 2D PSVTM Mode,
RS(15), No. 13, 2023, pp. 3321.
DOI Link 2307
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Deng, F.[Fei], Hu, J.[Jian], Wang, X.[Xuben], Yu, S.[Siling], Zhang, B.[Bohao], Li, S.[Shuai], Li, X.[Xue],
Magnetotelluric Deep Learning Forward Modeling and Its Application in Inversion,
RS(15), No. 14, 2023, pp. 3667.
DOI Link 2307
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Han, J.G.[Jian-Guang], Lü, Q.T.[Qing-Tian], Gu, B.L.[Bing-Luo], Yan, J.Y.[Jia-Yong],
Q-Compensated Gaussian Beam Migration under the Condition of Irregular Surface,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
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Ding, J.Q.[Jia-Qi], Zhao, X.F.[Xiao-Feng], Yang, P.[Pinglv], Fu, Y.[Yapeng],
A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric,
RS(15), No. 19, 2023, pp. 4893.
DOI Link 2310
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Zhang, X.B.[Xue-Bing], Song, Z.C.[Zheng-Chun], Li, B.[Bonan], Feng, X.[Xuan], Zhou, J.G.[Jian-Gang], Yu, Y.P.[Yi-Peng], Hu, X.[Xin],
The LPR Instantaneous Centroid Frequency Attribute Based on the 1D Higher-Order Differential Energy Operator,
RS(15), No. 22, 2023, pp. 5305.
DOI Link 2311
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Liu, L.[Lei], Sun, Y.[Yong], Ji, M.[Min], Wang, H.[Huimeng], Liu, J.T.[Jian-Tao],
Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing,
IJGI(12), No. 12, 2023, pp. 473.
DOI Link 2312
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Galone, L.[Luciano], d'Amico, S.[Sebastiano], Colica, E.[Emanuele], Iregbeyen, P.[Peter], Galea, P.[Pauline], Rivero, L.[Lluís], Villani, F.[Fabio],
Assessing Shallow Soft Deposits through Near-Surface Geophysics and UAV-SfM: Application in Pocket Beaches Environments,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
horizontal-to-vertical spectral ratio, seismic ambient noise, pocket beach, Malta, near-surface geophysics, electrical resistivity tomography, photogrammetry BibRef

Ding, M.[Mu], Zhou, Y.[Yatong], Chi, Y.[Yue],
Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously,
RS(16), No. 2, 2024, pp. 305.
DOI Link 2402
BibRef

Xia, M.M.[Mu-Ming], Zhou, H.[Hui], Jiang, C.[Chuntao], Cui, J.M.[Jin-Ming], Zeng, Y.[Yong], Chen, H.[Hanming],
Comparative Study of 2D Lattice Boltzmann Models for Simulating Seismic Waves,
RS(16), No. 2, 2024, pp. 285.
DOI Link 2402
BibRef


Gaharwar, A.[Anshuman], Kulkarni, P.P.[Parth Parag], Dickey, J.[Joshua], Shah, M.[Mubarak],
Xi-Net: Transformer based Seismic Waveform Reconstructor,
ICIP23(2725-2729)
IEEE DOI 2312
BibRef

Zhang, H.[Hao], Ma, J.W.[Jian-Wei],
Optimal Transport with a New Preprocessing for Deep-Learning Full Waveform Inversion,
ICIP22(1446-1450)
IEEE DOI 2211
Deep learning, Seismic measurements, Recurrent neural networks, Transforms, Geophysics, Probability density function, integration affine transform BibRef

Hernandez-Rojas, A.[Alejandra], Arguello, H.[Henry],
3D Geometry Design via End-To-End Optimization for Land Seismic Acquisition,
ICIP22(4053-4057)
IEEE DOI 2211
Geometry, Seismic measurements, Costs, Neural networks, Jitter, Surfaces, Seismic Acquisition Geometry, End-to-End Optimization, Undersampling Rate BibRef

Picetti, F.[Francesco], Lipari, V.[Vincenzo], Bestagini, P.[Paolo], Tubaro, S.[Stefano],
Anti-Aliasing Add-On for Deep Prior Seismic Data Interpolation,
ICIP21(1979-1983)
IEEE DOI 2201
Interpolation, Laplace equations, Neural networks, Machine learning, Tools, Spatial databases, spatial aliasing, convolutional neural network BibRef

Lin, Y., Theiler, J., Wohlberg, B., Wu, Y., Zhang, Z.,
Data-driven Methods for Solving Large-scale Inverse Problems with Applications to Subsurface Imaging,
SSIAI20(13-13)
IEEE DOI 2009
convolutional neural nets, geophysical image processing, geophysical prospecting, geophysical techniques, Convolutional neural networks BibRef

Abbas, A.[Ahmed], Swoboda, P.[Paul],
Bottleneck Potentials in Markov Random Fields,
ICCV19(3174-3183)
IEEE DOI 2004
combinatorial mathematics, graph theory, inverse problems, Markov processes, minimisation, random processes, Image segmentation BibRef

Angulo Bustos, H.I.[Harold Ivan], dos Santos Silva, M.P.[Marcelino Pereira],
A MAP algorithm for AVO seismic inversion based on the mixed (L2, non-L2) norms to separate primary and multiple signals in slowness space,
WACV09(1-6).
IEEE DOI 0912
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Geological Analysis, Rocks .


Last update:Mar 16, 2024 at 20:36:19