2024 Deep learning joint inversion

Deep learning joint inversion

Author: pmin

August undefined, 2024

WebAbstract: Join inversion has been playing an important role in integrating multiphysics data to reduce inversion uncertainties and improve resolution. In this paper, we propose a deep learning enhanced (DLE) joint inversion framework which enforces structural similarity by a deep neural network (DNN) and considers nonconforming discretizations of different … WebDec 1, 2024 · PhyDLI. In a physics-deep learning inversion scheme for one or multiple parameters the composite objective function resembles the form of a geophysical joint …

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WebSep 1, 2024 · Download Citation On Sep 1, 2024, Abhinav Pratap Singh and others published Deep learning for joint geophysical inversion of seismic and MT data sets Find, read and cite all the research you ... WebApr 8, 2024 · Transfer Learning for SAR Image Classification via Deep Joint Distribution Adaptation Networks High-Resolution SAR Image Classification Using Context-Aware … tourist on bbc

Deep learning for joint geophysical inversion of seismic

WebABSTRACT. Deep-learning (DL) methods have shown promising performance in predicting acoustic impedance from seismic data that is typically considered as an ill-posed problem for traditional inversion schemes. Most of DL methods are based on a 1D neural network that is straightforward to implement, but they often yield unreasonable lateral ... WebABSTRACT We develop a novel physics-adaptive machine-learning (ML) inversion scheme showing optimal generalization capabilities for field data applications. We apply the physics-driven deep-learning inversion to a massive helicopter-borne transient electromagnetic (TEM) field data set. The objective is the accurate modeling of the near … WebJan 9, 2024 · A deep learning enhanced framework is proposed to jointly invert the crosswell DC resistivity and seismic travel time data. With the strong capability to extract the implicit patterns of the input data, our deep neural network is trained to fuse and extract the connections between separately inverted resistivity and velocity models by the … tourist-online.de

A deep learning-enhanced framework for multiphysics …

IEEE Transactions on Geoscience and Remote Sensing(IEEE TGRS) …

WebJan 23, 2024 · Deep learning Inversion of Seismic Data. In this paper, we propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way to address this ill-posed seismic ... WebApr 8, 2024 · Transfer Learning for SAR Image Classification via Deep Joint Distribution Adaptation Networks High-Resolution SAR Image Classification Using Context-Aware Encoder Network and Hybrid Conditional Random Field Model ... Deep Learning Inversion of Electrical Resistivity Data by One-Sided Mapping. potvin socks.comWebAbstract. Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale datasets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in … potvin sucks chant

"WebUnlike conventional end-to-end networks that map directly from the data domain to the model domain, this DLE framework is designed to improve the joint inversion results … " - Deep learning joint inversion

Deep learning joint inversion

Deep learning for joint geophysical inversion of seismic

WebDec 30, 2024 · The second category is the direct-deep-learning inversion method, in which TgNN with geostatistical constraint, named TgNN-geo, is proposed as the deep-learning framework for inverse modeling. In TgNN-geo, two neural networks are introduced to approximate the random model parameters and solutions, respectively. In order to honor … WebFeb 14, 2024 · Our deep learning-based workflow is generic and can be readily used for reservoir characterization and reservoir model updates involving the use of 4-D seismic data.} ... The primary focus is to verify the feasibility of using deep learning methods to solve the joint inversion problem using multiphysics data. Joint inversion is to infer ...

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WebSeismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by known geological laws and drilling and logging data. The principle of seismic inversion based on deep learning is to learn the mapping between seismic data and rock properties by … WebDeep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity-velocity relationships according to prior knowledge. During the inversion, the unknown resistivity …

WebDec 27, 2024 · The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. WebApr 10, 2024 · With the development of deep learning research in geophysics, deep learning methods are used to first break picking [9,10], seismic data reconstruction [11,12], inversion [13,14,15], noise attenuation [16,17,18,19,20,21,22], etc. The clever and automatic noise attenuation technique based on the deep neural network was studied as …

WebFig. 2. Demonstration of the joint inversion results. (a) and (d) are the true models. (b) and (e) are the separately inverted models, (c) and (f) are the jointly inverted models. IV. C ONCLUSION In this work, we proposed a deep learning enhanced frame-work for joint inversion of crosswell DC resistivity and seismic data. WebJan 9, 2024 · A deep learning enhanced framework is proposed to jointly invert the crosswell DC resistivity and seismic travel time data. With the strong capability to …

WebIncreasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep …

WebOct 11, 2024 · Depth imaging projects dedicated to hydrocarbon exploration or field development rely heavily on velocity model building. When salt bodies are present, their accurate delineation is crucial to ensure the quality of seismic images, especially for sub-salt targets. We investigate a supervised deep learning (DL) approach which predicts salt … potvin \\u0026 bouchard incWebDec 14, 2024 · The Contrast Source Inversion, Deep Convolution, and Joint-Driven methods are compared to analyze the stability of model-driven deep learning networks in the iterative process. ... a model-driven deep learning Super-Resolution inversion algorithm is proposed to solve the problem of high noise and poor imaging in … tourist on tvWebApr 22, 2024 · The inverse problem of magnetotelluric data is extremely difficult due to its nonlinear and ill-posed nature. The existing gradient-descent approaches for this task surface from the problems of falling into local minima and relying on reliable initial models, while statistical-based methods are computationally expensive. Inspired by the excellent … touristopia travelWebMar 29, 2024 · However, the multiparameter joint inversion method based on deep learning, as used in. this article, can be better applied in the case of carbonate reservoirs under salt, which have. tourist on stanWebJun 3, 2024 · 4.2.4 Multimodal Deep Learning. To improve the resolution of inversion, the joint inversion of data from different sources has been a popular topic in recent years (Garofalo et al., 2015). One of the … tourist on hboWebOct 13, 2024 · Deep Learning Joint Inversion of Electrical Data for Ahead-Prospecting in Tunneling 1. Introduction. Continued growth in the world’s population and economy has … tourist on east coastWebMar 30, 2024 · Wellbore-scale joint petrophysical inversion of EM and sonic, with nuclear, dielectric, NMR, etc. ... “The main purpose of deep … potvin sucks song