Machine Learning Application on Seismic Diffraction Detection and Preservation for High Resolution Imaging

Y. Bashir, Nordiana MOHD MUZTAZA, Amir Abbas Babasafari, Muhammad Khan, M. Mahgoub, S.Y. Moussavi Alashloo, A. H. Abdul Latiff
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Abstract

Seismic Imaging for the small-scale feature in complex subsurface geology such as Carbonate is not easy to capture because of propagated waves affected by heterogeneous properties of objects in the subsurface. The initial step for machine learning (ML) is to provide enough data which can make our learning algorithm updated and mature. If one has not provided the multiple shapes of diffraction data, then your prediction of ML will be not accurate or even ML not able to detect the pattern of diffraction in the data. After the learning, our machine, the detection of the target is the crucial part that compares with the target and searches the specific signature in the given data. In this paper, we feed it with data in the form of the image and feature. Which can pass through the learning algorithm to predict the target. The idea of ML is to get the difference between your prediction and the target as closely as much possible. Which leads to the better preservation of diffractions amplitude in laterally varying velocity conditions. ML destruction is used for diffraction data separation as the conventional filtering techniques mix the diffraction amplitudes when there are a single or series of diffractions.
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机器学习在地震衍射检测和高分辨率成像保存中的应用
碳酸盐岩等复杂地下地质的小尺度特征地震成像由于受到地下物体非均质性的影响而不易捕获。机器学习(ML)的第一步是提供足够的数据,使我们的学习算法更新和成熟。如果没有提供衍射数据的多种形状,那么您对ML的预测将不准确,甚至ML无法检测数据中的衍射模式。经过学习,我们的机器对目标的检测是与目标进行比较,在给定数据中搜索特定签名的关键部分。在本文中,我们以图像和特征的形式为其提供数据。其中可以通过学习算法来预测目标。机器学习的思想是尽可能接近你的预测和目标之间的差异。这使得衍射幅值在横向变速条件下能更好地保存。ML破坏用于衍射数据分离,因为当存在单个或一系列衍射时,传统的滤波技术会混合衍射振幅。
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