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English(EN) Real-world application of deep learning in large-scale seismic interference attenuation: A case study in the Camie field of Angola

深度学习提升安哥拉大规模勘探中的地震干扰去除效果

研究人员在安哥拉Camie油田的大规模海上地震勘探中应用了深度神经网络(DNN),显著提高了地震干扰的衰减效果。该基于DNN的工作流程采用监督学习框架,并使用手动生成的训练数据进行训练,与传统的地球物理算法相比,在去除地震干扰方面表现出更高的准确性。该项目覆盖面积超过345平方公里,凸显了DNN在实现更高地震干扰去除精度、减少信号泄漏和更彻底地消除噪声方面的有效性,预示着深度学习在其他地震去噪应用中的潜力。 AI

影响 展示了深度学习在复杂数据处理挑战中的实际应用,有望提高地球科学领域的效率和准确性。

排序理由 该条目是一篇研究论文,详细介绍了深度学习模型在特定科学问题中的应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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深度学习提升安哥拉大规模勘探中的地震干扰去除效果

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该条目是一篇研究论文,详细介绍了深度学习模型在特定科学问题中的应用。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    深度学习在大规模地震干扰压制中的实际应用:安哥拉Camie油田案例研究

    In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured. It typically appears as coherent noise with linear or non-linear movement and varying amplitudes across different sail lines. SI is commonly observed an…