基于深度学习的地震首点自动拾取

First EAGE/PESGB Workshop Machine Learning Pub Date : 2018-11-30 DOI:10.3997/2214-4609.201803023

P. Xie, J. Boelle, C. Blais

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引用次数: 5

摘要

本工作实现了一种全卷积神经元网络，用于提取难处理的陆地地震数据的首点。与传统方法相比，大大提高了生产效率。目前的工作仅限于二维地震镜头采集，可以很容易地扩展到三维。在我们的测试数据集中，它的每次挑选只需几秒钟，并且具有可靠的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic Seismic First Arrival Picking With Deep-Learning

Summary This work implements a fully-convolutional neuron network to pick first arrival in difficult field land seismic data. Compared to traditional methods, it greatly improves the productivity. Current work is limited to 2D seismic shot gather and can be extended to 3D without much difficulty. In our test dataset, its picking takes few second per shot and has a credible precision.

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First EAGE/PESGB Workshop Machine Learning

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