... neural networks (PINNs). Similarly with linear optimization algorithms, the inputs of PINNs include real seismograms, a wavelet, and a low frequency model. The loss function of PINNs is designed to minimize the difference between real seismograms and synthetic seismic seismograms that are computed from...

... seismogram media applied geoscience & energy 10 american association reservoir characterization equation point-to-line source seismogram marmousi model transformation hybrid method green exploration geophysicist viscoelastic anisotropic media geology amplitude function tensor true model...

... at a work area in west China indicates that the precision of velocity model that fuses near surface model is higher, seismic wave filed information from elastic wave modeling is richer. velocity model geological model seismogram geologist model-building technique applied geoscience & energy...

... Vertical seismic profiling (VSP) datasets often suffer from significant incoherent noise caused by poor coupling between receivers and boreholes. This problem is observed in both the standard VSP, where seismograms are recorded using receivers within the well, and the distributed acoustic...

... intelligence generator seismogram applied geoscience american association inversion architecture gan international ricker wavelet Broadband reconstruction of seismic signal with generative recurrent adversarial network Zhijun Zhang* , TianJin Branch of CNOOC Ltd ;Wei Song, Institute of Geophysics...

... into the Technical Program but was not presented at IMAGE 2022 in Houston, Texas. artificial intelligence seismogram optimization problem amplitude preservation optimization correction upstream oil & gas amplitude preservation seismic amplitude preservation optimization reservoir characterization...

... Due to the strong non-stationarity of the depth-domain seismic data, the traditional convolutional model cannot be used to calculate the synthetic seismogram in depth domain. Therefore, the time-domain inversion methods, which are based on convolutional model, fail to be directly applied...

... considering a realistic 3D layered earth model. At first, we simulate the triggering of dynamic fault slip at the center of a fault plane. Next, we invert the seismograms recorded by receivers located on or near the surface to obtain the full moment-tensor point-source representation and the location...

... & gas rectilinearity function workflow reservoir characterization novel automatic technique seismic event seismogram artificial intelligence first-break picking threshold correlation coefficient applied geoscience correlation matrix exploration geophysicist 10 correlation polarization...

... exploration geophysicist 10 dataset colorado school testing dataset seismogram eagle ford f1-score eagle ford depth range application guided wave energy convolutional neural network geophysics cnn Convolutional Neural Network for Guided Wave Energy Identi cation in Microseismic DAS Data Youfang...

... resolution requires a full description of the source signal to properly separate the reflectivity information from the recorded seismograms. We introduce a physics-guided convolutional autoencoder concept as a novel alternative to perform automatic deconvolution. This unsupervised deep learning structure...

... An amplitude versus angle (AVA) method is presented for estimating density and velocities of a stratified elastic medium. The seismograms are multiple-free, intercept time-horizontal slowness domain Green’s functions of time length T assumed to obey the first order WKBJ model for elastic...

... is that the neural network processes the full waveform seismogram taking into account simultaneously the kinematic and the amplitude part of the wavefield. For the network training, a synthetic dataset was generated using the spectral element method. The results of the work demonstrate the feasibility of the method...

... reflectivity and the virtual data on the datum are estimated in sequence or simultaneously. The synthetic examples show that this LSR method can improve the accuracy and resolution of the redatumed seismograms. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 9:20 AM Presentation Time: 9:20...

... surface waves are then used through an adaptive subtraction process to remove the surface waves from the recorded seismograms. The proposed method does not impose any 1-D assumption as conventional dispersionbased modeling approach does, and hence can model complicated wave phenomena, including back...

... characterization cambridge university press artificial intelligence microcrack machine learning upstream oil & gas tensor rake angle observed seismogram seg international exposition rupture exploration geophysicist 10 hydraulic fracturing information velocity model inversion seismogram green...

... the equivalence of the two methods for a known earth model. Wavefield snapshots and seismograms for both methods are shown including the cases of the following: (1) total vector reflectivity, (2) the vertical and horizontal components of reflectivity separately and (3) variable density. A second example compares...

... Presentation Time: 1:50 PM Location: Poster Station 8 Presentation Type: Poster reservoir characterization seg international exposition atomic norm optimization harbin institute thin layer seismogram frequency upstream oil & gas resolution weak signal seismic deconvolution reflectivity...

... on synthetic data. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Start Time: 4:20 PM Location: 214D Presentation Type: Oral singular spectrum analysis seismic data vector upstream oil & gas reservoir characterization separation seismogram...

... hub in-situ laboratory carbon dioxide experiment seismogram monitoring tertyshnikov small quantity pevzner controlled-release experiment international conference upstream oil & gas south west hub reservoir characterization in-situ laboratory leakage injection seg international...