Microseismic Data Augmentation Based on Improved Generative Adversarial Network and Policy Gradient

Microseismic data plays a crucial role in seismic exploration and oil/gas exploitation. However, its high acquisition cost leads to scarcity, making effective augmentation of microseis-mic datasets a critical issue. In this paper, a new approach based on policy gradient and generative adversarial network (GAN) is proposed, which aims to improve the scale and variety of micro-seismic data sets. We integrate GANs with policy gradient algorithms, employing a tailored policy network to better capture the distribution characteristics of microseismic data. Through adversarial training, the generator network continually refines synthesized microseismic data, while the discriminator network assesses the authenticity of generated samples. This adversarial approach effectively enhances the quality of generated data, aligning it more closely with the distribution characteristics of real microseismic data. Incorporating self-attention mechanisms within the generator facilitates improved learning of long-term temporal features in microseismic signals, thereby generating synthetic samples that closely resemble real micro-seismic signals. Experimental validation on real microseismic datasets demonstrates that the proposed method based on policy gradient and GANs effectively augments microseismic datasets. The synthesized data exhibits statistical properties and geological significance comparable to real data, providing richer and more diverse data resources for subsequent geological analysis and exploration efforts.