Seismic imaging profile noise suppression based on self supervised deep learning: a case study in the Yellow Sea

Qiang Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

..., and G. Hu, 2022, Ground truth-free 3-D seismic random noise attenuation via deep tensor convolutional neural networks in the time-frequency domain...

Multichannel seismic deconvolution via 2D K-SVD and convolutional sparse coding

Guiqian Zhang, Xiayu Gao, Bangli Zou, Yaojun Wang, Yingzhu Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

... to the deconvolution objective function in the form of regularization. Frequency Decomposition of Seismic Profile According to the convolutional model...

Abstract: Reflectivity Color Correction in Gabor Deconvolution; #90211 (2015)

Carlos Montana and Gary Margrave

Search and Discovery.com

.... In contrast with the stationary convolutional model, which can be formulated in a simple way either in the time or the frequency domain...

A strategy for acoustic impedance direct inversion in depth domain

Ruiqian Cai, Chengyu Sun, Shizhong Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the depth-domain seismic data, the traditional convolutional model cannot be used to calculate the synthetic seismogram in depth domain. Therefore...

RNN-based seismic velocity model building: Improving generalization using hybrid training data

Hani Alzahrani, Jeffrey Shragge

International Meeting for Applied Geoscience and Energy (IMAGE)

... the underlying velocity model and produces improved results compared to other frequency-domain network architectures, it still suffers from the common...

Seismic absolute acoustic impedance inversion using domain adversarial based transfer learning

Anjali Dixit, Animesh Mandal

International Meeting for Applied Geoscience and Energy (IMAGE)

..., saturation, to name a few. However, to obtain absolute AI, incorporation of low-frequency impedance model is essential. This work presents a unified...

Enhancing seismic data quality: A machine learning approach to denoising and signal damage reduction

Mark Roberts, Olga Brusova, Leandro Gabioli, Alejandro Valenciano

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the common shot domain and a self-supervised ML signal-add back model in the common channel domain. The supervised ML-based denoise (Brusova et al., 2021...

Bayesian variational auto-encoder for seismic wavelet extraction

Ammar Ghanim, Ricard Durall, Norman Ettrich

International Meeting for Applied Geoscience and Energy (IMAGE)

...-shift. b) using a model trained with time- and frequency-domain loss. c) using the same model as above, but with noise superimposed on the input. a) b...

Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks

Brett Bernstein, Yaoguo Li, Richard Hammack

International Meeting for Applied Geoscience and Energy (IMAGE)

...Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks Brett Bernstein, Yaoguo Li...

ABSTRACT: Shaping the Wavelet, by Wang, Yuchun E.; Huo, Shoudong; #90141 (2012)

Search and Discovery.com

Semi-automated prestack seismic inversion workflow using temporal convolutional networks

Hussain Alfayez, Robert Smith, Ayman Suleiman, Nasher AlBinHasan

International Meeting for Applied Geoscience and Energy (IMAGE)

... problems, the model often needs access to a long history of the input sequence. Regular convolutional neural networks (CNN) struggle to capture long-term...

DL-fused elastic FWI: Application to marine streamer data

Pavel Plotnitskii, Oleg Ovcharenko, Vladimir Kazei, Daniel Peter, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

.... METHOD OVERVIEW The idea of this method is to directly map high-wavenumber model updates from time-domain FWI into low-wavenumber model updates (Figure...

Seismic diffractions separation and imaging based on convolutional neural network

Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

...Seismic diffractions separation and imaging based on convolutional neural network Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu...

Deep learning based wavefield separation method for VSP data

Gang Feng, Qin Su, Zhe Yang, Wei Yang, Jian-Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... parameters are shown in Table 1. Table 1 forward parameters Type parameters Wavelet Domain frequency Maximum record time Time sample rate Ricker...

Deep water OBN multiple prediction from local reflectivity in the Stolt domain

Cesar Ricardez

International Meeting for Applied Geoscience and Energy (IMAGE)

...)] (2) This multiple model is then directly subtracted in the (x,y,t) domain or could be adapted using traditional L2 energy minimization techniques...

Seismic simulations of experimental strata

Lincoln Pratson, Wences Gouveia

AAPG Bulletin

.... These reflection coefficients are then converted from the depth to the time domain using the model velocities. Note that the convolutional model is one...

Time-lapse seismic joint inversion based on Zoeppritz equation in depth domain

Wenjin Li, Jingye Li, Lei Han, Zhen Liao

International Meeting for Applied Geoscience and Energy (IMAGE)

... with velocity (depth) in the depth domain leads to seismic records exhibiting non-stationary. Time-frequency analysis techniques for time-window wavelet...

Separation of simultaneous source wavefields using convolutional neural network

Zhehao Li, Hua-Wei Zhou, Kang Fu

International Meeting for Applied Geoscience and Energy (IMAGE)

... not exhibit image Gaussian noise features, we modified the network to train the model in common receiver gathers domain to directly predict...

Seismic interpolation based on quadratic denoising neural network

Yuhan Sui, Xiaojing Wang, Jianwei Ma

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Recently, a deep learning-based method is developed to achieve seismic interpolation by integrating the well-trained denoising convolutional neural...

Retrieving high-resolution acoustic impedance using full-waveform inversion in presalt reservoir setting, offshore Brazil

Akshat Abhishek, Alok K. Soni, Rene-Edouard Plessix, Martijn Blaauw, Jaydip Guha, Gautam Kumar

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the frequency domain, Part 1: Theory and verification in a physical scale model: Geophysics, 64, 659–992, doi: https://doi.org/10.1190/1.1444597. Rutherford...

ABSTRACT: Selected Topics in Seismic Dispersion

Christopher L. Liner

Houston Geological Society Bulletin

..., reflection and transmission coefficients, head waves, etc. The convolutional reflection models we use to model thick and thin bed thin response...

Automated velocity model building using Fourier neural operators

Guanghui Huang, Sean Crawley, Ramzi Djebbi, Jaime Ramos-Martinez, Nizar Chemingui

International Meeting for Applied Geoscience and Energy (IMAGE)

... efficiently computed in the Fourier domain. We show the advantages of using global FNOs over conventional convolutional neural networks (CNN), to achieve...

High-efficient reflection retrieval from massive ambient noise using a deep-learning workflow

Yinghe Wu, Shulin Pan, Dawei Liu, Yaojie Chen, Qinghui Cui

International Meeting for Applied Geoscience and Energy (IMAGE)

... data training. Besides, frequency-domain data are also feed to the network to increase the diversity of the training volume. These steps encourage CAC...

3D real-time imaging for electromagnetic fracturing monitoring based on deep learning

Zhigang Wang, Yao Lu, Ying Hu, Yinchu Li, Ke Wang, Dikun Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

.../10.1093/gji/ggz204. Wang, Z., G. Yu, L. Zhang, C. Wang, J. Zhang, and Z. Liu, 2017, The use of time-frequency domain electromagnetic technique to monitor hydraulic...

An application of FWI with progressive transfer learning

Shirui Wang, Jinjun Liu, Yuchen Jin, Xuqing Wu, Jiefu Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

..., University of Houston Summary The lack of low-frequency data components has been a major obstacle in FWI applications for velocity model building...