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Abstract
En 中文
For the successful inversion and interpretation of multicomponent seismic data, it is crucially important to map the PS-wave to the time domain of the PP-wave. The accuracy of traditional methods decreases when the data differ due to noise, amplitude, phase, and frequency perturbations. Recently, deep learning (DL) has been used for seismic data registration, requiring fewer assumptions and lower computational costs. However, current methods primarily focus on direct matching through neural networks, often neglecting physical constraints. To address the above issue, we propose a seismic data registration method based on a physically constrained unsupervised (PCUS). First, to avoid undesirable foldings in the warped PS-wave, we require a monotonic warping function that preserves the waveform of the warped PS-wave. We guarantee the monotonicity of the warping function by parameterizing it with a physical constraint of the relationship between the warping function and the velocity ratio $V_{\mathrm { P}}/V_{\mathrm { S}}$ . Then, we consider the continuity of seismic events and add a smooth regularizer to guarantee the smoothness of the warping function. Finally, we utilize the unsupervised DL framework to address this issue. Experiments on synthetic and field datasets indicate the validity and flexibility of the PCUS method. In the synthetic data, the proposed PCUS method demonstrates robustness against amplitude, phase, frequency, and noise perturbations. The relative root-mean- square error (RRMSE) is at least one-tenth of that produced by the traditional dynamic image warping (DIW) method and a DL-based registration (DLR) method. In the field data, the proposed PCUS method aligns the seismic events of the PS-wave and PP-wave under the premise of keeping the waveform of the PS-wave.
Keywords:
Deep learning (DL)
physical constraint
physically constrained unsupervised (PCUS)
seismic data registration
unsupervised
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W