Passive source seismic interferometry is a cost-effective geophysical method that converts noise signals into valuable information. The fidelity of the resultant common-shot gather is pivotal for effective imaging. The quality of reconstructed records via seismic interferometry directly correlates with the duration of background noise observation. However, practical applications often encounter difficulties in obtaining stable and usable long-duration observations of noise-based passive seismic records. Short-duration observations may introduce spurious physical events, thereby compromising the reliability of seismic wavefield imaging and geological interpretation. In this study, we introduce MDUNETR, an advanced passive data reconstruction network amalgamating Transformer and multiscale dense blocks (MDBs) to enhance accuracy. By integrating Transformer and MDB, the network effectively captures both global and local information. Utilizing the MDUNETR network, we can reconstruct accurate passive source interferometric seismic records from short-duration noise interference signals. This overcomes the time limitations imposed by seismic interferometry on the original noise records. Theoretical data applications demonstrate the stability and fidelity of the seismic records reconstructed by this network, ensuring reliable results.

中文翻译：

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多尺度密集网络变压器集成短时被动地震数据精确重建

无源源地震干涉测量是一种经济高效的地球物理方法，可将噪声信号转换为有价值的信息。所得共射集合的保真度对于有效成像至关重要。通过地震干涉测量重建记录的质量与背景噪声观测的持续时间直接相关。然而，实际应用在获得基于噪声的被动地震记录的稳定且可用的长期观测方面经常遇到困难。短时观测可能会引入虚假物理事件，从而损害地震波场成像和地质解释的可靠性。在本研究中，我们引入了 MDUNETR，这是一种先进的被动数据重建网络，它将 Transformer 和多尺度密集块 (MDB) 结合起来以提高准确性。通过集成 Transformer 和 MDB，网络可以有效捕获全局和本地信息。利用 MDUNETR 网络，我们可以从短时噪声干扰信号中重建精确的被动源干涉地震记录。这克服了地震干涉测量对原始噪声记录施加的时间限制。理论数据应用证明了该网络重建的地震记录的稳定性和保真度，确保了结果的可靠。