Fault Identification of Post Stack Seismic Data by Improved Unet Network

doi:10.19596/j.cnki.1001-246x.8684

Abstract

Abstract:

In order to improve the accuracy of fault identification, an improved Unet model is proposed. A multi-branch parallel structure M-block (Multi-branch block) is designed for the encoder part. It can capture multi-scale context information, and multi branch parallel structure will bring high performance benefits. Self-Attention block and attention gating mechanism are added to the decoder. Self-Attention not only enables the attention module to flexibly focus on different areas of the image, but also makes up for the shortcomings of the local CNN (Convolutional Neural Network) and brings more possibilities to the neural network through the weighted average operation of the input feature context. It is verified by synthetic data and actual data that the model combines the advantages of weight sharing in traditional convolution with the advantages of Self Attention's dynamic calculation of Attention weight to improve the accuracy of fault identification. Compared with Unet, the test loss is reduced by 33.68%. The model not only identifies fault features accurately, but also is more accurate than the current popular depth learning method.

Key words: fault identification, many branches, self-attention, Unet, encoder

CLC Number:

O469

Guixin LIU, Zhonghua MA. Fault Identification of Post Stack Seismic Data by Improved Unet Network[J]. Chinese Journal of Computational Physics, 2023, 40(6): 742-751.

Figures/Tables 14

Fig.1 Multibranched Self-Attention Eunet structure

Fig.2 Multiple-branching structure

Fig.3 (a) Training losses and (b) validation losses of Unet and M-Unet

Fig.4 An improved Self-Attention structure

Fig.5 Select 2 pictures in the test set and input them to the network

Fig.6 The feature diagram of the Unet network on the left, and the feature diagram of M-SA-Unet after adding Self-Attention in the blue dotted box

Fig.7 (a) Five epoch feature diagrams selected from the Unet network, and (b) the feature diagram of the improved M-SA-Unet network

Fig.8 Image enhancement

Fig.9 Comparison of M-SA-Unet and M-SA-Eunet with other model fault recognition results in the test set

Fig.10 Comparison of M-SA-Unet and M-SA-Eunet with other model fault recognition results on the forecast frame

Table 1 Sets and results of parameters in the training and verification process

	Train Loss	Val Loss	Epoch	Batchsize	优化器	学习率	训练miou	测试miou
Unet	0.078	0.095	30	16	AdamW	0.001	0.831	0.800
Eunet	0.058	0.091	30	16	AdamW	0.001	0.873	0.801
Segnet	0.055	0.069	30	16	AdamW	0.001	0.890	0.875
Unet++	0.035	0.065	30	16	AdamW	0.001	0.911	0.904
Unet+++	0.065	0.113	30	8	AdamW	0.001	0.848	0.749
Attention-Unet	0.055	0.076	30	16	AdamW	0.001	0.911	0.877
M-SA-Unet	0.033	0.062	30	16	AdamW	0.001	0.914	0.882
M-SA-Eunet	0.034	0.063	30	16	AdamW	0.001	0.926	0.911

Fig.11 Unet, Eunet, M-SA-Unet and M-SA-Eunet (a) training loss, (b) validation loss, (c) training miou and (d) validation miou

Fig.12 (a) Fault label data and (b) actual seismic data

Fig.13 Comparison of M-SA-Eunet and other models on actual earthquake prediction data sets

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