Deep-learning inversion of seismic data

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August undefined, 2024

WebApr 3, 2024 · Deep learning technology have been used in seismic exploration to solve seismic inversion problems but require sufficient and diverse training samples and labels. Insufficient training labels is a common problem since labels usually come from well- logging data, which are limited and sparsely distributed. WebFeb 14, 2024 · In this research, we adopt CycleGAN to build a deep learning based time-lapse seismic inversion workflow, which can be used to quickly determine reservoir fluid property changes based on time-lapse seismic data. Seismic inversion, an ill-posed and highly nonlinear problem, is traditionally solved via statistical or gradient based method, …

Deep learning inversion of gravity data for detection of CO2

WebApr 7, 2024 · @article{osti_1865313, title = {Real-time deep-learning inversion of seismic full waveform data for CO2 saturation and uncertainty in geological carbon storage … WebFeb 27, 2024 · Recently, seismic inversion has made extensive use of supervised learning methods. The traditional deep learning inversion network can utilize the temporal correlation in the vertical direction. Still, it does not consider the spatial correlation in the horizontal direction of seismic data. Each seismic trace is inverted independently, … is jed a word

Optimization-Inspired Deep Learning High-Resolution Inversion …

WebDeepSeismic. This repository shows you how to perform seismic imaging and interpretation on Azure. It empowers geophysicists and data scientists to run seismic experiments … WebNeural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, when solving a seismic inversion problem with DL, each author uses, in addition to different DL … WebDec 21, 2024 · This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We … kevin michaelson cpa

Deep-Learning Inversion of Seismic Data - arxiv.org

Seismic Impedance Inversion Based on Residual Attention Network

WebWave-equation-based inversion. Thanks to its unmatched ability to resolve CO 2 plumes, active-source time-lapse seismic is arguably the preferred imaging modality when … Webdirectly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer … kevin michaels realtorWebJan 1, 2024 · The depth domain seismic data, initial model and logging data are input into the inversion module of the network model. Then, the output acoustic impedance data is used to synthesize the depth domain seismic records using the proposed method and the input seismic data are compared. kevin michael wood boone county north carlina

"WebJan 23, 2024 · 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). " - Deep-learning inversion of seismic data

Deep-learning inversion of seismic data

WebJul 25, 2024 · Deep learning (DL) has achieved promising results for impedance inversion via seismic data. Generally, these networks, composed of convolution layers and residual blocks, tend to deliver good results with deep architectures. Nevertheless, deep networks accompany a large number of parameters and longer training time. The volume of … WebApr 10, 2024 · The increasing volume of seismic data and human manipulation not only affects efficiency, but also generates subjective errors. To overcome the shortcomings of traditional denoising methods, we introduce deep learning methods to more intelligently and efficiently attenuate traffic noise in seismic data.

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WebOct 13, 2024 · We investigate a supervised deep learning (DL) approach which predicts salt geometry by using seismic and electromagnetic data simultaneously. Different … WebDec 11, 2024 · Deep-Learning Inversion of Seismic Data. Abstract: We propose a new method to tackle the mapping challenge from time-series data to spatial image in the …

WebTraining the Deep Neural Network for 4D Seismic Inversion The model training is carried out in multiple phases. solely trains on un-augmented simulation data to determine an ideal network structure. The second phase trains on the fixed architecture with data augmentation to transfer the network to noisy field data. The WebWe propose to denoise and reconstruct the 3D seismic data simultaneously using an unsupervised deep learning (DL) framework, which does not require any prior information about the seismic data and is free of labels. We use an iterative process to reconstruct the 3D highly incomplete seismic data.

WebJul 1, 2024 · The main objective of this work is the implementation of Deep Learning (DL) solutions to generate synthetic seismograms from 1D acoustic models without solving the wave equation. This is done by training a NN model which after training is able to predict common shot gathers from 1-D velocity models. WebMar 17, 2024 · Data-driven deep-learning full-waveform inversion (DD-DLFWI) can efficiently reconstruct a velocity image of the subsurface from prestack seismic …

WebJan 22, 2024 · Deep-learning assisted regularized elastic full waveform inversion using the velocity distribution information from wells Yuanyuan Li, T. Alkhalifah, Zhen-dong Zhang Geology 2024 Elastic full waveform inversion (EFWI) can, theoretically, give high-resolution estimates of the subsurface.

WebJan 1, 2024 · The depth domain seismic data, initial model and logging data are input into the inversion module of the network model. Then, the output acoustic impedance data … kevin michael thomasville ncWebJun 3, 2024 · Data observation uses mainly noninvasive techniques such as seismic waves, gravity fields, and remote sensing. Data processing techniques, including denoising and reconstruction, retrieve useful … kevin michell john small schoolWebApr 22, 2024 · Deep learning has been widely adopted in seismic inversion. One of the major obstacles when adopting deep learning in seismic inversion is the demand for labeled data sets. There are mainly two approaches to address this problem. One is to generate massive numbers of synthetic data and then transfer the trained model to real … is jedburgh a nice place to liveWebDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning … kevin michaels canton maWebJan 23, 2024 · Deep-Learning Inversion of Seismic Data. 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 … is jedburgh castle jail openWebDec 21, 2024 · This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed … kevin mickits mediation calendarWebDeep Learning Seismic Inversion: A Data Driven Approach. Report this post kevin michals cross properties