The vertical seismic profile (VSP) considerably aids attenuation analysis and velocity calibration, enabling high-resolution seismic exploration. However, the imaging and interpretation of VSP data require pure upgoing and downgoing wavefields, and their separation is critical in processing and interpretation. Often, achieving high precision and efficient wavefield separation can be regarded as a challenging issue. To automate this process with higher accuracy, we develop a deep-learning high-precision intelligent separation method. First, to construct a physics-driven and data-generic training set, the well-log data from different geologic environments are used to simulate the upgoing and downgoing VSP data independently. Thereafter, we train a multitask-learning neural network to regress the network weights before and after separation to tune a mapping model, which is used to implement intelligent upgoing and downgoing wavefield separation. Validation data confirm the feasibility of our method. To further study the generalizability and robustness of the method, we apply the trained model to separate the modeled and real VSP data. By analyzing the separated profiles and frequency-wavenumber spectra, it is observed that the intelligent separation method is superior to conventional methods with a balance between accuracy and efficiency. It can be concluded that our method can separate upgoing and downgoing wavefields in VSP data with excellent performance, even in complex geologic environments. Our study indicates that the multitask-learning neural network combined with a physics-driven and data-generic training set is a new strategy to separate upgoing and downgoing wavefields and deserves to be popularized and applied.

You do not have access to this content, please speak to your institutional administrator if you feel you should have access.