Fault Prediction Method Based on Convolutional Neural Network
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摘要: 针对传统相干体属性在预测断层时存在断层假象以及易受噪声影响等缺点，本文提出一种利用卷积神经网络进行断层预测的方法。首先构建适合实际工区断层特征的卷积神经网络模型，然后利用部分分频地震数据和人工解释出的断层标签进行网络模型训练，最后把训练好的模型应用到整个三维地震数据中进行断层预测。实际地震数据预测结果表明基于卷积神经网络断层预测结果与地震数据吻合较好，并且在断层细节刻画上要优于传统地震相干体属性方法。Abstract: Aiming at the disadvantages of traditional coherent volume attribute in fault prediction,such as false fault and poor noise resistance,this paper proposes a method for fault prediction using convolutional neural networks.First,construct a convolutional neural network model suitable for the fault characteristics of the actual work area,then train the network model using the partial frequency division seismic data and manually interpreted fault labels,and finally apply the trained model to the entire 3D seismic data for fault prediction.The actual seismic data prediction results show that the fault prediction results based on the convolutional neural network are in good agreement with the seismic data,and the fault detail description is better than the traditional seismic coherent volume attribute method.
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Keywords:
- convolutional neural network /
- faults /
- deep learning /
- coherent attribute
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期刊类型引用(2)
1. 王宝江，吴振锋，吉娃阿英，杨桂林，孙洪，钟昆，于强，任战利. 高角度走滑断缝体断裂识别及解释——以鄂尔多斯盆地镇泾区块为例. 天然气地球科学. 2025(01): 142-154 . 
百度学术
2. 梁雁，刘广峰. 基于卷积神经网络的人脸识别研究. 数字通信世界. 2021(01): 101-102 . 百度学术
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