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Showing 621 Results. Searched 200,293 documents.
Estimate near-surface velocity with reversals using deep learning and full-waveform inversion
Yong Ma, Xu Ji, Weiguang He, Tong Fei
International Meeting for Applied Geoscience and Energy (IMAGE)
... domain for both training and prediction. In this way, we avoid domain conversions between time and depth in DNN. This timedomain velocity profile can...
2022
Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion
Sagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota, Long Jin, Ilya Tsvankin
International Meeting for Applied Geoscience and Energy (IMAGE)
... are described below. Encoder model: Stage 1 The input is a 1D tensor or vector of a fixed time length (NT), which is a stacked seismic trace. The output...
2022
Development and Application of a Real-Time Drilling State Classification Algorithm with Machine Learning
Yuxing Ben, Chris James, Dingzhou Cao
Unconventional Resources Technology Conference (URTEC)
... machine learning models were over 99%. The CNN model was proven to be the best model, excelling with high accuracy, short computation time, and scalability...
2019
Filter-Bank Strategies for Efficient Computation of Radon Transforms for SNR Enhancement
Mauricio D. Sacchi
Search and Discovery.com
... and parabolic paths (for a frequency domain implementation) and linear and hyperbolic paths (for a time-variant/time domain numerical implementation...
Unknown
Filter-Bank Strategies for Efficient Computation of Radon Transforms for SNR Enhancement
Mauricio D. Sacchi
Search and Discovery.com
... and parabolic paths (for a frequency domain implementation) and linear and hyperbolic paths (for a time-variant/time domain numerical implementation...
Unknown
Automated velocity modeling with domain transformations
Kevin Gullikson, Arnab Dhara, Ram Tuvi, Mrinal K. Sen
International Meeting for Applied Geoscience and Energy (IMAGE)
... the data from the shot gather domain to the tau-p domain, then predicting the time-domain velocity model using a U-Net, and finally converting from...
2024
Physics-based preconditioned multidimensional deconvolution in the time domain
David Vargas, Ivan Vasconcelos, Matteo Ravasi, Nick Luiken
International Meeting for Applied Geoscience and Energy (IMAGE)
... the convolutional kernel in (4) cannot be decoupled on a frequency-by-frequency basis. In the time-domain, the operator P+ is too large to be explicitly...
2022
U-net based primary alignment
Ricard Durall, Ammar Ghanim, Norman Ettrich
International Meeting for Applied Geoscience and Energy (IMAGE)
... in the presence of such events. Furthermore, our model is versatile and can be applied to both offset and angle gathers in both time and depth...
2023
Noise suppression and compressive sensing recovery with seismic-adapted DnCNN within RED
Nasser Kazemi
International Meeting for Applied Geoscience and Energy (IMAGE)
..., i.e., seismic domain. In this paper, we explore the transferability of feedforward denoising convolutional neural networks (DnCNN) learned operator...
2024
Efficient seismic image super-resolution
Adnan Hamida, Motaz Alfarraj, Abdullatif A. Al-Shuhail, Salam A. Zummo
International Meeting for Applied Geoscience and Energy (IMAGE)
... a GAN-based model with four convolutional layers for both the generator and discriminator. Fehler and Keliher (2011) SEAM Phase I synthetic dataset...
2022
Towards flexible demultiple with deep learning
Mario Fernandez, Norman Ettrich, Matthias Delescluse, Alain Rabaute, Janis Keuper
International Meeting for Applied Geoscience and Energy (IMAGE)
... moveout to be considered multiple reflections in Mi+1 than in Mi . We build the training data through the convolutional model for a large number...
2024
Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising
Nanying Lan, Kaiheng Sang, Fanchang Zhang
International Meeting for Applied Geoscience and Energy (IMAGE)
...Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising Nanying Lan, Kaiheng Sang...
2022
Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs
Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib-Asl
Unconventional Resources Technology Conference (URTEC)
... and the last 20% for testing. The developed model predicted the daily oil production rate as a function of production data time series. Their results...
2022
How Machine Learning is Helping Seismic Structural Interpreters in The Age of Big Data
Çağil Karakaş, James Kiely
GEO ExPro Magazine
... is a very time-consuming task, often leading to a simplified fault model, a geology-driven, machine-learning workflow can significantly improve...
2021
High-resolution seismic data processing method based on deep convolutional dictionary learning
Xiayu Gao, Qingyu Feng, Yaojun Wang, Bangli Zou, Yang Luo
International Meeting for Applied Geoscience and Energy (IMAGE)
... decomposition on the entire image, fully considering the local relevance of seismic data and strictly following the seismic data convolutional model...
2024
Abstract: Recovering Low Frequencies for Impedance Inversion by Frequency Domain Deconvolution; #90224 (2015)
Sina Esmaeili and Gary Frank
Search and Discovery.com
... reflectivity. We start by reintroducing the convolutional model for normal incident seismograms and then show how reflectivity can be estimated...
2015
Seismic sparse time-frequency representation via GAN-based unsupervised learning
Youbo Lei, Yang Yang, Naihao Liu, Shengtao Wei, Jinghuai Gao, Xiudi Jiang
International Meeting for Applied Geoscience and Energy (IMAGE)
... the optimization problem. However, STFR is often based on a mathematical model designed with the domain knowledge. Moreover, it suffers from the expensive...
2022
Unsupervised frequency space domain deep learning framework for reconstructing 5D seismic data
Gui Chen, Yang Liu, Haoran Zhang, Mi Zhang, Yuhang Sun
International Meeting for Applied Geoscience and Energy (IMAGE)
... of the incomplete data itself for recovering missing traces. Almost all existing DL reconstruction methods are performed in the time domain, which...
2024
Refining our understanding of the subsurface geology using deep learning techniques
Salma Alsinan, Philippe Nivlet, Hamad Alghenaim
International Meeting for Applied Geoscience and Energy (IMAGE)
...) regarding the importance of developing an inclusive geological model to train these algorithms. Furthermore, this Figure 3: Time slice at reservoir level...
2022
Deep learning decomposition for null and active space estimation for thin-bed reflectivity inversion
Kristian Torres, Mauricio D. Sacchi
International Meeting for Applied Geoscience and Energy (IMAGE)
... parts of the model and the ”inverted” noise, respectively. METHOD We can decompose the domain of the forward operator into two sub-spaces: the measurement...
2022
Deep learning software accelerators for full-waveform inversion
Sergio Botelho, Souvik Mukherjee, Vinay Rao, Santi Adavani
International Meeting for Applied Geoscience and Energy (IMAGE)
...-difference time domain (FDTD) method (Louboutin et al., 2019; Luporini et al., 2020). For preliminary experiments, we will use a velocity model...
2022
4D Finite Difference Forward Modeling within a Redefined Closed-Loop Seismic Reservoir Monitoring Workflow, #41922 (2016).
David Hill, Dominic Lowden, Sonika, Chris Koeninger
Search and Discovery.com
... modeled base-line shot gather (Figure 9). If the basic shot-domain noise models are summed with both the modeled data for the base-line and time-steps...
2016
First arrival enhancement by statics preserving filtering using surface-consistent constraints
Alejandro Quiaro, Mauricio D. Sacchi
International Meeting for Applied Geoscience and Energy (IMAGE)
... initialize the workflow assuming zero initial static. We explore the advantages of building an initial time shift model by maximizing cross-correlations...
2023
Introducing stochasticity into CNN-based property estimation from angle-stack seismic
Haibin Di, Tao Zhao, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... and perturbing with Gaussian noises ℕ(0,1) per prior rock property model. convolutional layer for reconstructing the fullstack seismic, and (iii) one...
2024
Identifying geologic facies through seismic dataset-to-dataset transfer learning using convolutional neural networks
Joseph Stitt, Adam Shugar, Rachael Wang
International Meeting for Applied Geoscience and Energy (IMAGE)
... for the baseline model we used is a public-domain survey called “Parihaka,” which contains offshore seismic data from the New Zealand government...
2022