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- W4285803402 abstract "Accurate and efficient reconstruction of hidden geological structures under the surface is the main task of high-resolution Velocity Model Building (VMB). The most commonly used methods in practice are Tomography and Full Waveform Inversion (FWI), which rely heavily on the initial model. Recently, deep learning types of methods have received widespread attention and have performed well in many tasks such as image segmentation and classification. Therefore, it is of great significance to introduce deep learning algorithms into the VMB procedure to accelerate the production cycle, especially for the velocity anomalies detection, which is crucial for a high-resolution initial model. In this paper, a modified U-Net framework is proposed and applied directly on the seismic shot gathers to identify anomalies in the early stage of VMB, which can provide a suitable initial guess for the following large-scale VMB procedures such as FWI. The numerical examples show the power of the proposed method on synthetic data." @default.
- W4285803402 created "2022-07-19" @default.
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- W4285803402 date "2022-07-18" @default.
- W4285803402 modified "2023-09-29" @default.
- W4285803402 title "Seismic Velocity Anomalies Detection Based on a Modified U-Net Framework" @default.
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- W4285803402 doi "https://doi.org/10.3390/app12147225" @default.
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