With the wide application of the high-density and high-productivity acquisition technology in the complex areas of oil fields, the first-break picking of massive low signal-to-noise data is a great challenging job. Conventional first-break automatic picking methods (Akaike information criterion method, energy ratio method, correlation method and boundary detection method) require a lot of manual adjustments due to their poor anti-noise performance. A lot of adjustments affect the accuracy and efficiency of picking. First-break picking takes up about one-third of the whole processing cycle, which restricts petroleum exploration and development progress severely. In order to overcome the above-mentioned shortcoming, this paper proposes the first-break automatic picking technology based on semantic segmentation. Firstly, design the time window for primary wave and pick a certain quantity of first breaks from newly acquired data in different zones of the exploration area using the commonly used Akaike information criterion method and interactive adjustments; and then perform pre-processing on the data within the time window to extract multiple first-break attribute features and perform feature enhancement, to obtain multi-dimensional features data blocks, at the same time, label the first breaks. Secondly, u-shaped architecture network-like encoding and decoding network is used to implement end-to-end feature learning from primary wave attribute data to first-break labels. The encoding and decoding process of the encoding and decoding network is used to fuse the extraction and feature positioning of primary wave attribute features. At the same time, normalize each layer and use the rectified linear unit function as a non-linear factor to improve the generalization and sensitivity of network model to low signal-to-noise primary waves. Finally, an optimized deep network model is used to predict the first breaks of the data to improve the accuracy and efficiency of the first-break picking. This method innovatively fuses the multiple conventional automatic picking methods (Akaike information criterion method, energy ratio method, correlation method and boundary detection method) to extract multiple attribute features of primary wave, and improves the accuracy of the training network model to the first-break detection using the improved UNet-like encoding and decoding network. The feasibility of the new method is proved by model data. A comparative test is conducted between the new method and the Akaike information criterion method with the actual data, which verifies that the method in this paper has a higher picking accuracy and stable first-break processing capability for the data with low signal to noise, our method shows a significant advantage when applied to low signal-to-noise seismic records from high-productivity acquisition and can meet the demands of the accuracy and efficiency for near-surface model building and static calculation of massive data.

中文翻译：

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首创基于语义分割的自动拣选技术

随着高密度、高产能采集技术在油田复杂区域的广泛应用，海量低信噪比数据的首次采摘是一项极具挑战性的工作。传统的首破自动采摘方法（赤池信息准则法、能量比法、相关法和边界检测法）由于抗噪性能较差，需要大量人工调整。很多调整都会影响拣货的准确性和效率。首次采油约占整个加工周期的三分之一，严重制约了石油勘探开发的进展。为了克服上述缺点，本文提出了基于语义分割的首破自动拣选技术。首先，使用常用的赤池信息准则方法和交互调整，设计一次波的时间窗口，并从勘探区不同区域新获取的数据中选取一定数量的初波；然后对时间窗内的数据进行预处理，提取多个初破属性特征并进行特征增强，得到多维特征数据块，同时对初破进行标注。其次，采用u型架构网络式编解码网络，实现从一次波属性数据到首波标签的端到端特征学习。编解码网络的编解码过程用于融合一次波属性特征的提取和特征定位。同时，对每一层进行归一化，并使用整流后的线性单元函数作为非线性因子，以提高网络模型对低信噪比初级波的泛化性和敏感性。最后，采用优化的深度网络模型对数据的初断点进行预测，以提高初断点拣选的准确性和效率。该方法创新性地融合了多种常规自动拾取方法（赤池信息准则法、能量比法、相关法和边界检测法）提取一次波的多个属性特征，提高了训练网络模型对初波的精度使用改进的类 UNet 编码和解码网络进行检测。模型数据证明了新方法的可行性。