Rapid retrieval and classification of passive-source body wave events using a convolutional self-attention encoder: Application to gas storage monitoring

ABSTRACT Passive-source seismic interferometry (SI) demonstrates significant potential in geophysical monitoring owing to its low cost and nondestructive attributes. However, uneven noise source distributions, pervasive surface waves, and other sources of noise often obscure body wave reflections, leading to very low signal-to-noise ratios in reconstructed signals. Additionally, long-term data acquisition generates large, high-noise data sets, increasing processing costs. Thus, rapidly retrieving body wave-dominated segments is essential for preprocessing. In this study, a convolutional self-attention encoder (CSE) was introduced to address these challenges. This method adopted multimodal inputs consisting of noise segments and their corresponding frequency attributes, enhancing overall performance. An attention mechanism was integrated to construct a binary classifier based on the CSE, enhancing contextual understanding. These steps helped the CSE to better learn features from the data sets, thereby improving generalization ability and prediction accuracy. Accordingly, the network was updated through incremental learning on small data sets, enabling continuous prediction and gradual adaptation to new data. This enabled us to rapidly generate large, high-quality training data sets and to train models with high performance. To further improve prediction data quality, the intermediate features extracted from the deep convolutional layers corresponding to the retrieved body wave events were extracted, and these features were clustered to perform quality classification of the retrieved data. Synthetic simulation data and field data from Northwest China validated that the proposed method retrieves and classifies body wave reflections with high accuracy and efficiency, providing robust data for subsequent monitoring and enhancing imaging results. The proposed method provides efficient and reliable data preprocessing for the application of passive-source SI in energy transition scenarios, such as subsurface fluid migration monitoring and carbon dioxide geological sequestration, and facilitates the development of low-carbon exploration and green transformation in the energy industry.