# MathGeo
A toolbox for seismic data processing from Center of Geopyhsics, Harbin Institute of Technology, China
## 2020 updates
# Deep learning related works
* python_segy
A Denoised CNN is use for attenuation of random noise.
Reference:
S. Yu, J. Ma, W. Wang, Deep learning for denoising, Geophysics, 2019, 84 (6), V333-V350
* FCNVMB
A U-Net is used for predicting velocity model from raw prestack seismic records.
Reference:
F. Yang, J. Ma, Deep-learning inversion: a next generation seismic velocity model building method, Geophysics, 2019, 84 (4), R583-R599.
* CNN-POCS
A combination of CNN denoiser and POCS is used for seismic interpolation.
Reference:
H. Zhang, X. Yang, J. Ma, Can learning from natural image denoising be used for seismic data interpolation, Geophysics, 2020, 85 (4)
## 2018 updates
* GMD
Geometric mode decomposition (GMD) is designed to decompose seismic signal with linear or hpyerbolic events, with applications to denoising and interpolation.
Reference:
S. Yu, J. Ma, S. Osher, Geometric mode decomposition, Inverse Problem and Imaging, 2018, 12 (4), 831-852.
* GVRO
Gradient-vector matrices are formed by collecting gradient vectors in a local seismic patch as columns. For single-dip signals, the gradient vectors will group along same lines. So, gradient-vector matrices should be approximately rank-one matrices. For multi-dip signals, the local seismic data are decomposed into single-dip components, with each components’ gradient-vector matrices regularized to be rank-one matrices. The proposed gradient-vector rank-one regularization (GVRO) model is solved in the frame work of block coordinate descending algorithm, and can be used for random noise attenuation and coherent signals separation according to the dip differences.
Reference:
K. Cai, J. Ma, MDCA: multidirectional component analysis for robust estimation of multiple local dips, IEEE Transactions on Geoscience and Remote Sensing, 2019, 57 (5), 2798-2810.
* SR1
We presented a generalization of the low-rank approximation, which allows to individually shift the column of rank-1 matrices (SR1). This model was designed to represent objects that move through the data. This holds in applications such as seismic or ultrasonic image processing as well as video processing.
Reference:
F. Bossmann, J. Ma, Enhanced image approximation using shifted rank-1 reconstruction, Inverse Problems and Imaging, 2020, 14 (2), 267-290.
* TSDL
We proposed a tree structure dictionary learning (TSDL) method. Our approach is based on two components: a sparse data representation in a learned dictionary and a similarity measure for image patches that is evaluated using the Laplacian matrix of a graph.
Reference:
L. Liu, J. Ma, G. Plonka, Sparse graph-regularized dictionary learning for random seismic noise, Geophysics, 2018, 83 (3), V213-V231.
## 2017
* AGCM:
We have used the asymmetric Gaussian chirplet model (AGCM) and established a dictionary-free variant of the orthogonal matching pursuit, a greedy algorithm for sparse approximation of seismic traces.
Reference:
F. Bossmann, J. Ma, Asymmetric chirplet transform for sparse representation of seismic data, Geophysics, 2015, 80(6):WD89-WD100.
F. Bossmann, J. Ma, Asymmetric chirplet transform Part 2: Phase, frequency, and chirp rate, Geophysiscs, 2016, 81(6):V425-V439.
* Decurtain:
An infimal convolution model is applied to split the corrupted 3D image into the clean image and two types of corruptions, namely a striped part and a laminar one.
Reference:
J. Fitschen, J. Ma, S. Schuff, Removel of curtaining effects by a variational model with directional first and second order differences, Computer Vision and Image Understanding, 2017, 155, 24-32.
* EMPCR:
We propose a simple yet efficient interpolation algorithm, which is based on the Hankel matrix, for randomly missing traces.
Reference:
Y. Jia, S. Yu, L. Liu, J. Ma, A fast rank-reduction algorithm for three-dimensional seismic data interpolation. Journal of Applied Geophysics, 2016, 132:137-145.
* RegistrationMultiComponent:
We propose a new curvelet-based registration method to improve the precision of registration, especially for the data with heavy random noises.
Reference:
H. Wang, Y. Cheng, J. Ma, Curvelet-based registration of multi-component seismic waves. Journal of Applied Geophysics, 2014, 104(5):90-96.
* DL_toolbox
We propose a simultaneous dictionary learning and denoising method for seismic data.
Reference:
S. Beckouche, J. Ma, Simultaneously dictionary learning and denoising for seismic data, Geophysics, 2014, 79 (3), A27-A31.
* DDTF3D:
We study an application of the data-driven tight frame (DDTF) method to noise suppression and interpolation of high-dimensional seis- mic data.
Reference:
S. Yu, J. Ma, X. Zhang, M. Sacchi, Interpolation and denoising of high-dimensional seismic data by learning a tight frame, Geophysics, 2015, 80 (5), V119-V132.
* MCDDTF3D:
We have designed a new patch selection method for DDTF seismic data recovery. We suppose that patches with higher variance contain more information related to complex structures, and should be selected into the training set with higher probability.
Reference:
S. Yu, J. Ma, S. Osher, Monte Carlo data-driven tight frame for seismic data recovery, Geophysics, 2016, 81 (4), V327-V340.
* CVMD:
We have extended varitional mode decomposition to complex-valued situation and apply CVMD to f-x spectrum of seismic for denoising.
Reference:
S. Yu, J. Ma, Complex variational model decomposition for slop-preserving denoising, IEEE Transactions on Geoscience and Remote Sensing, 2018, 56 (1), 586 - 597.
* LDMM
We have applied low dimensional manifold method for seismic strong noise attenuation. LDMM uses a low dimensional method to approximate all the patches of seismic data.
Reference:
S. Yu, S. Osher, J. Ma, Z. Shi, Noise attenuation in a low dimensional manifold, Geophysics, 2017, 82 (5), V321-V334.
* test data download link
http://pan.baidu.com/s/1qYwI1IG
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MathGeo2020
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MathGeo2020 (244个子文件)
perform_dictionary_denoising.asv 4KB
WangHR.asv 3KB
test.asv 2KB
im2colsvar.c 5KB
col2imstep.c 4KB
col2imstep.c 4KB
im2colstep.c 3KB
im2colstep.c 3KB
MathGeo中文简介.html 5KB
desktop.ini 67B
LICENSE 18KB
LICENSE 1KB
test4compare_sigmoid.m 9KB
perform_dictionary_learning.m 9KB
sr1.m 9KB
Demo_pocs_cnn.m 8KB
GMD.m 6KB
Demo_cnndenoise.m 6KB
AGCMfreq_bestStep.m 6KB
curtainDropping.m 5KB
HRMD.m 5KB
HRMD1D.m 4KB
Example_interp_MC_3D.m 4KB
VMDC.m 4KB
Example_AGCM_transform.m 4KB
perform_dictionary_denoising.m 4KB
SOR.m 4KB
fx_decon_patch.m 4KB
Example_interp_MC_2D.m 4KB
write_patches_tv_sor_center.m 3KB
sepAGCM.m 3KB
write_patches_tv_sor.m 3KB
SOR_2dip1.m 3KB
Test_tree_dic.m 3KB
wigb.m 3KB
fig14_RMD_para1d_multiple.m 3KB
fig11_RMD_para_inter.m 3KB
EOR1MP.m 3KB
SOR_3dip.m 2KB
Example_multi_match.m 2KB
pradon.m 2KB
test.m 2KB
SOR_single_2.m 2KB
fig10_VMD_2D_test_denoise.m 2KB
fig07_RMD_para.m 2KB
Demo_cnndenoise3D.m 2KB
filter_learning_2D.m 2KB
ipradon.m 2KB
filter_learning_3D.m 2KB
Example_interp.m 2KB
SOR_single_sor.m 2KB
sr1_demo.m 2KB
fig05_VMD_2D_test_seis.m 2KB
example_CVMD_denoising.m 2KB
gen_dic_by_iwt_3d.m 2KB
gen_dic_by_iwt_3d.m 2KB
fig09_RMD_para1d.m 2KB
patch_center.m 2KB
patch_decenter.m 2KB
clip.m 1KB
clip.m 1KB
fig06_VMD_2D_test_convergency_seis.m 1KB
get_Gradientmatrix_sobel.m 1KB
get_Gradientmatrix_backward_3point.m 1KB
get_Gradientmatrix_forward_3point.m 1KB
write_patches_center.m 1KB
OMPerr.m 1KB
DisplayH3d_all.m 1KB
im2colstep.m 1KB
im2colstep.m 1KB
im2colsvar.m 1KB
DisplayH3d_all.m 1KB
Example_EMPCR.m 1KB
leigs.m 1KB
get_Gradientmatrix_forward.m 1KB
seishow3D.m 1KB
seishow3D.m 1KB
get_Gradientmatrix_backward.m 1KB
get_Gradientmatrix_center.m 1KB
Build_Tree.m 1KB
get_patches.m 1KB
readme.m 1KB
proj_mask.m 1KB
proj_mask.m 1KB
write_patches.m 1KB
inter3d_yu.m 1022B
col2imstep.m 1001B
col2imstep.m 1001B
displayH.m 998B
OMP.m 974B
train3d.m 969B
train3d.m 969B
inter2d_yu.m 898B
Example_denoise.m 885B
softShrinkage.m 880B
inter3d.m 836B
get_patches_location.m 835B
get_patches_slides.m 818B
Build_Tree_Second.m 740B
makeMask.m 670B
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