Dec 11, 2019 · We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration.
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Apr 25, 2024 · Task definition. DNNs based seismic inversion is to learn the function that maps seismic data to its corresponding velocity model. …
Seismic inversion is performed by means of wave inversion of a simple prior model of the subsurface and by using a back-propagation loop to infer the subsurface ...
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Abstract—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., ...
Inversion of pre-stack seismic data is important for building accurate models of hydrocarbon reservoirs used to estimate reserves and set up efficient ...
May 6, 2024 · Our approach directly maps seismic data to reflection models, eliminating the need for post-processing low-resolution results. Through extensive ...
Dec 21, 2021 · This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification ...
A comparison of deep learning methods for seismic impedance ...
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The most intuitive inversion method is using 1-dimensional CNN as a mapping function from a seismic trace to an impedance sequence. Unlike model-driven methods, ...
We adopt the recent proposed SeisInvNet, which reconstructs the velocity model by treating each seismic trace as the essential element.
Mar 25, 2022 · Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO2 ...