Loss function comparison for fault interpretation of three-dimensional seismic data based on deep neural network
ZHANG Miaomiao1, WU Bangyu1, MA Debo2, WANG Zhiguo1
1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; 2. Research Institute of Petroleum Exploration and Development, Beijing 100083, China
Abstract：Fault interpretation is one of the key steps for seismic data interpretation.The rapid development of deep learning，represented by neural networks，has greatly improved the efficiency and accuracy of seismic fault interpretation.The neural networks are trained by stochastic gradient descent optimization.The parameters of the network model are updated iteratively by using the loss function to measure the error of the model.The selection of the loss function is crucial for the seismic fault interpretation.In this paper，to interpret 3D seismic fault，we use the 3D U-Net model as the network structure and Adam as the optimizer to train the network with 3D synthetic samples.In terms of fault interpretation effects，we compare 10 loss functions including Balanced Cross-Entropy （BCE），Dice，Focal，Cosine，Log-Cosh Dice，Tversky，Focal-Tversky，Wasserstein，BCE-Dice，and BCE-Cosine.Normalization and data augmentation are applied to the trained data to mitigate the discrepancy between synthetic and field data.With the same network model，training parameters，and stopping criteria，we compare the convergence speed，calculation efficiency，and anti-noise performance of the 10 loss functions on 3D U-Net and analyze the fault prediction effect by using actual seismic data of the F3 field from offshore Netherlands.The experimental results show that 3D U-Net trained by Tversky and focal-Tversky loss functions can predict fault with better continuity.When crossed or parallel faults are densely distributed，and adjacent fault features can influence each other，the 3D U-Net prediction faults trained by BCE，BCE-dice，and BCE-cosine loss functions are complete，clear，and rich in detail.The research can provide a reference for selecting appropriate loss functions in different scenarios for seismic fault interpretation.
张苗苗, 吴帮玉, 马德波, 王治国. 深度神经网络三维地震资料断层解释损失函数对比[J]. 石油地球物理勘探, 2023, 58(6): 1299-1312.
ZHANG Miaomiao, WU Bangyu, MA Debo, WANG Zhiguo. Loss function comparison for fault interpretation of three-dimensional seismic data based on deep neural network. Oil Geophysical Prospecting, 2023, 58(6): 1299-1312.
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