Matches in SemOpenAlex for { <https://semopenalex.org/work/W3046496543> ?p ?o ?g. }
- W3046496543 endingPage "3587" @default.
- W3046496543 startingPage "3580" @default.
- W3046496543 abstract "It is widely known that strong noise can decrease the quality of seismic data. However, the anelastic attenuation could be more important to account for the weak amplitude and low quality of seismic data. Here, we develop an inversion framework to simultaneously compensate for the attenuation of seismic data and remove noise, thereby enhancing the quality of seismic data. Instead of directly applying a compensation operator to the input seismic data, we formulate an inverse problem that connects the sparse reflectivity model and the raw seismic data via the convolution and attenuation functions. The random noise is assumed to be the unpredicted part of the forward modeling process. We use the L2-norm regularization for the data misfit and impose a sparsity constraint onto the reflectivity series, e.g., using the L1-norm constraint. We use an iterative preconditioned conjugate gradient method to solve the L1-norm constrained least-squares optimization problem and obtain the reflectivity series. The denoised and compensated data are obtained by applying the convolution operator to the reflectivity. We use several synthetic and field seismic data to illustrate the effectiveness of the presented method." @default.
- W3046496543 created "2020-08-07" @default.
- W3046496543 creator A5019972279 @default.
- W3046496543 creator A5042175903 @default.
- W3046496543 creator A5067576135 @default.
- W3046496543 creator A5089366118 @default.
- W3046496543 date "2021-04-01" @default.
- W3046496543 modified "2023-10-13" @default.
- W3046496543 title "Q-Compensated Denoising of Seismic Data" @default.
- W3046496543 cites W1512208174 @default.
- W3046496543 cites W1865538694 @default.
- W3046496543 cites W1966320522 @default.
- W3046496543 cites W1973540739 @default.
- W3046496543 cites W1976162422 @default.
- W3046496543 cites W1980811494 @default.
- W3046496543 cites W1996098514 @default.
- W3046496543 cites W2020205028 @default.
- W3046496543 cites W2021734622 @default.
- W3046496543 cites W2030400978 @default.
- W3046496543 cites W2054198069 @default.
- W3046496543 cites W2076211048 @default.
- W3046496543 cites W2076393573 @default.
- W3046496543 cites W2100245965 @default.
- W3046496543 cites W2118592408 @default.
- W3046496543 cites W2130940196 @default.
- W3046496543 cites W2134909076 @default.
- W3046496543 cites W2141953966 @default.
- W3046496543 cites W2145167443 @default.
- W3046496543 cites W2158677781 @default.
- W3046496543 cites W2171584283 @default.
- W3046496543 cites W2189494574 @default.
- W3046496543 cites W2312973324 @default.
- W3046496543 cites W2403089413 @default.
- W3046496543 cites W2412205031 @default.
- W3046496543 cites W2416739439 @default.
- W3046496543 cites W2474759111 @default.
- W3046496543 cites W2602807606 @default.
- W3046496543 cites W2744992965 @default.
- W3046496543 cites W2747542692 @default.
- W3046496543 cites W2753932103 @default.
- W3046496543 cites W2755572092 @default.
- W3046496543 cites W2765388976 @default.
- W3046496543 cites W2766109704 @default.
- W3046496543 cites W2766287589 @default.
- W3046496543 cites W2776535170 @default.
- W3046496543 cites W2794182965 @default.
- W3046496543 cites W2805829540 @default.
- W3046496543 cites W2886072820 @default.
- W3046496543 cites W2887874867 @default.
- W3046496543 cites W2891795837 @default.
- W3046496543 cites W2922480875 @default.
- W3046496543 cites W2944813907 @default.
- W3046496543 cites W2952315560 @default.
- W3046496543 cites W2963481168 @default.
- W3046496543 cites W2980661370 @default.
- W3046496543 cites W2980937689 @default.
- W3046496543 cites W4244753991 @default.
- W3046496543 doi "https://doi.org/10.1109/tgrs.2020.3010813" @default.
- W3046496543 hasPublicationYear "2021" @default.
- W3046496543 type Work @default.
- W3046496543 sameAs 3046496543 @default.
- W3046496543 citedByCount "4" @default.
- W3046496543 countsByYear W30464965432021 @default.
- W3046496543 countsByYear W30464965432022 @default.
- W3046496543 countsByYear W30464965432023 @default.
- W3046496543 crossrefType "journal-article" @default.
- W3046496543 hasAuthorship W3046496543A5019972279 @default.
- W3046496543 hasAuthorship W3046496543A5042175903 @default.
- W3046496543 hasAuthorship W3046496543A5067576135 @default.
- W3046496543 hasAuthorship W3046496543A5089366118 @default.
- W3046496543 hasConcept C104317684 @default.
- W3046496543 hasConcept C11413529 @default.
- W3046496543 hasConcept C120665830 @default.
- W3046496543 hasConcept C121332964 @default.
- W3046496543 hasConcept C126255220 @default.
- W3046496543 hasConcept C127313418 @default.
- W3046496543 hasConcept C134306372 @default.
- W3046496543 hasConcept C135252773 @default.
- W3046496543 hasConcept C137219930 @default.
- W3046496543 hasConcept C154945302 @default.
- W3046496543 hasConcept C158448853 @default.
- W3046496543 hasConcept C159737794 @default.
- W3046496543 hasConcept C160920958 @default.
- W3046496543 hasConcept C165205528 @default.
- W3046496543 hasConcept C17020691 @default.
- W3046496543 hasConcept C184652730 @default.
- W3046496543 hasConcept C185592680 @default.
- W3046496543 hasConcept C1893757 @default.
- W3046496543 hasConcept C2776135515 @default.
- W3046496543 hasConcept C33923547 @default.
- W3046496543 hasConcept C39267094 @default.
- W3046496543 hasConcept C41008148 @default.
- W3046496543 hasConcept C55493867 @default.
- W3046496543 hasConcept C77928131 @default.
- W3046496543 hasConcept C81184566 @default.
- W3046496543 hasConcept C86339819 @default.
- W3046496543 hasConceptScore W3046496543C104317684 @default.
- W3046496543 hasConceptScore W3046496543C11413529 @default.