Physics-guided data-driven seismic inversion

Author: gdko

August undefined, 2024

Webb2 maj 2024 · The model-driven inversion method and data-driven prediction method are effective to obtain velocity and density from seismic data. The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data. Webb1 jan. 2024 · Physics-Guided Data-Driven Seismic Inversion: Recent progress and future opportunities in full-waveform inversion January 2024 DOI: 10.1109/MSP.2024.3217658 …

Physics-guided convolutional neural network (PhyCNN) …

Webb9 aug. 2024 · Seismic inversion is the inverse problem: given actual surface measurements, infer what subsurface configuration would give rise to those … WebbDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning … branches in the government

Physics-guided deep learning for seismic inversion with

Webb7 feb. 2024 · The process of obtaining subsurface data from surface measurements is called seismic inversion. Subsurface geophysical properties influence the transmission … Webb8 apr. 2024 · Physics-Constrained Deep Learning of Geomechanical Logs. 地震数据点云上采样. Deep Learning for Irregularly and Regularly Missing 3-D Data Reconstruction. 地震检测. Intelligent Real-Time Earthquake Detection by Recurrent Neural Networks. 地震数据反演. Well-Logging Constrained Seismic Inversion Based on Closed-Loop ... haggle and co

Data Assimilation Networks - Boudier - 2024 - Journal of Advances …

Model-data-driven AVO inversion method based on multiple …

WebbExperienced Seismic Petrophysicist / Rock physicist in Schlumberger SRC Seismic Reservoir Characterization group since November 2011. معرفة المزيد حول تجربة عمل Omar Aly وتعليمه وزملائه والمزيد من خلال زيارة ملفه الشخصي على LinkedIn WebbB. Data-driven Acoustic- and Elastic-waveform Inversion Particularly in seismic waveform inversion, there have some recent development of data-driven waveform inversion techniques, which can be categorized into two groups: an end-to-end learning [3, 35, 46, 47] and low-wave number learning [32, 38]. The end-to-end strategy directly learns a hagglebids auctionWebb1 juli 2024 · Figure 1 Flow chart showing the discovery of dynamics from physical modeling to data-driven modeling Despite great progress in seeking accurate numerical approximator to nonlinear structural... haggle at carmax

"Webb7 sep. 2024 · Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging … " - Physics-guided data-driven seismic inversion

Physics-guided data-driven seismic inversion

Physics-guided convolutional neural network (PhyCNN) for data-driven …

Webb22 nov. 2024 · Abstract: The low-frequency seismic data provide crucial information for guiding the full-waveform inversion (FWI), especially when strong reflectors exist in the velocity model. However, hardware limitations make it difficult to acquire low-frequency data. To overcome the nonlinearity and ill-posedness caused by the absence of the low … Webb1 sep. 2024 · In this paper, we develop new physics-informed data augmentation techniques based on convolutional neural networks. Specifically, our methods leverage …

Did you know?

WebbSeismic inversion is the inverse problem: given actual surface measurements, infer what subsurface configuration would give rise to those measurements. Like most inverse … WebbAbstract: The goal of seismic inversion is to obtain subsurface properties from surface measurements. Seismic images have proven valuable, even crucial, for a variety of …

Webb22 juni 2024 · Lin, “Data-driven seismic waveform in version: A study on the robustness and generalization,” IEEE T ransactions on Geoscience and Remote sensing , vol. 58, no. 10, pp. 6900–6913, 2024. Webb23 mars 2024 · Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization Abstract: Full-waveform inversion is an important and widely used …

Webb14 dec. 2024 · Abstract: Geostatistical seismic rock physics amplitude-versus-angle (AVA) inversion allows the joint prediction of rock and fluid properties from seismic reflection … Webb13 apr. 2024 · In this paper, we propose a fully data driven deep learning framework generalizing recurrent Elman networks and data assimilation algorithms. Our approach approximates a sequence of prior and posterior densities conditioned on noisy observations using a log-likelihood cost function .

Webb6 jan. 2024 · Seismic Inversion Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis DOI: 10.1190/geo2024-0312.1 Authors: Jian Sun …

WebbPhysics-Guided Data-Driven Seismic Inversion: Recent progress and future opportunities in full-waveform inversion Lin, Youzuo; Theiler, James; Wohlberg, Brendt; Abstract. … branches in visual studioWebbResults indicate that the predictions of the trained network are susceptible to facies proportions, the rock-physics model, and source-wavelet parameters used in the training data set. Finally, we apply CNN inversion on the Volve field data set from offshore Norway. haggle co outdoor furnitureWebb2 jan. 2024 · Abstract: The goal of seismic inversion is to obtain subsurface properties from surface measurements. Seismic images have proven valuable, even crucial, for a variety of applications, including subsurface energy exploration, earthquake early warning, carbon capture and sequestration, estimating pathways of subsurface contaminant … branches lifestyleWebb16 okt. 2024 · CycleFCN: A Physics-Informed Data-driven Seismic Waveform Inversion Method Full Record Related Research Authors: Lin, Youzuo [1]; Feng, Shihang [1]; Jin, Peng [1]; Wohlberg, Brendt Egon [1]; Moulton, John David [1] + Show Author Affiliations Publication Date: Fri Oct 16 00:00:00 EDT 2024 Research Org.: haggle co mount barkerWebbPhysics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling Ruiyang Zhanga, Yang Liub, Hao Suna,c, aDepartment of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA bDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA hagglebids auction houseWebbDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning methods that use seismic data as the only input lead to difficult training and unstable inversion results (i.e., transverse discontinuity or geologic unreliability). haggle baby woodvilleWebb12 juli 2024 · Physics-guided deep learning for seismic inversion: hybrid training and uncertainty analysis Authors: Jian Sun Ocean University of China Kristopher Innanen … branches lakeview