WebDeep learning-based methods gain great popularity because of their powerful ability to obtain exact solutions for geophysical inverse problems. However, those deep learning … WebSeismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by …

Deep-Learning Inversion of Seismic Data - arxiv.org

WebJun 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 … WebJan 6, 2024 · Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural … cybercops

Deep learning for multidimensional seismic impedance inversion

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. 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 … WebDeep-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. raissa dassel