Enhancing data-driven seismic inversion using physics-guided spatiotemporal data augmentation

Yang, Yuxin; Zhang, Xitong; Guan, Qiang; Lin, Youzuo

doi:10.1190/segam2021-3583172.1

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…FWI in active seismology relies heavily on low-frequency components [30], while field microseismic data generally contain higher frequency contents than active seismic data, and the high-frequency information might be missing in synthetic data considering the computational expense. Yang et al [31] found that integrating physical information with synthetic data can improve the effectiveness of the training data and network performance. Alkhalifah et al [32] employed the domain adaptation approach to introduce real signal features into the synthetic data by correlation and convolution operations.…”

Section: Introductionmentioning

confidence: 99%

Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation

Li,

Zeng,

Pan

et al. 2024

Applied Sciences

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Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy.

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Section: Introductionmentioning

confidence: 99%