A deep learning network for estimation of seismic local slopes
A deep learning network for estimation of seismic local slopes
复制标题
用于估计地震局部坡度的深度学习网络
DOI：
10.1007/s12182-020-00530-1
复制
发表时间：
2021
影响因子：
5.6
通讯作者：
Chuai Xiao-Yu
中科院分区：
文献类型：
--
作者：
Huang Wei-Lin;Gao Fei;Liao Jian-Ping;Chuai Xiao-Yu
局部斜坡包含丰富的反射几何信息，可用于地震速度拾取、正常移出校正、时间域成像和构造解释等后续处理。一般情况下，坡度估计是通过人工拾取或扫描沿各种坡度的地震剖面来实现的。这里我们提出了一种基于深度学习的技术来从地震数据中自动估计局部斜率图。该方法采用三层卷积层来提取局部窗口的结构特征，三层全连通作为分类器，根据提取的特征预测局部窗口中心点的斜率。深度学习网络只使用合成地震数据进行训练，但它可以准确地估计真实地震数据中的局部坡度。我们用模拟和实际地震数据检验了它的可行性。估计的局部斜率图证明了综合训练网络的成功性能。
The local slopes contain rich information of the reflection geometry, which can be used to facilitate many subsequent procedures such as seismic velocities picking, normal move out correction, time-domain imaging and structural interpretation. Generally the slope estimation is achieved by manually picking or scanning the seismic profile along various slopes. We present here a deep learning-based technique to automatically estimate the local slope map from the seismic data. In the presented technique, three convolution layers are used to extract structural features in a local window and three fully connected layers serve as a classifier to predict the slope of the central point of the local window based on the extracted features. The deep learning network is trained using only synthetic seismic data, it can however accurately estimate local slopes within real seismic data. We examine its feasibility using simulated and real-seismic data. The estimated local slope maps demonstrate the successful performance of the synthetically-trained network.