Supervised-Learning-Based Method for Restoring Subsurface Shallow-Layer Q Factor Distribution

Abstract

The distribution of shallow subsurface quality factors (Q) is a crucial indicator for assessing the integrity of subsurface structures and serves as a primary parameter for evaluating the attenuation characteristics of seismic waves propagating through subsurface media. As the complexity of underground spaces increases, regions expand, and testing environments diversify, the survivability of test nodes is compromised, resulting in sparse effective seismic data with a low signal-to-noise ratio (SNR). Within the confined area defined by the source and sensor placement, only the Q factor along the wave propagation path can be estimated with relative accuracy. Estimating the Q factor in other parts of the area is challenging. Additionally, in recent years, deep neural networks have been employed to address the issue of missing values in seismic data; however, these methods typically require large datasets to train networks that can effectively fit the data, making them less applicable to our specific problem. In response to this challenge, we have developed a supervised learning method for the restoration of shallow subsurface Q factor distributions. The process begins with the construction of an incomplete labeled data volume, followed by the application of a block-based data augmentation technique to enrich the training samples and train the network. The uniformly partitioned initial data are then fed into the trained network to obtain output data, which are subsequently combined to form a complete Q factor data volume. We have validated this training approach using various networks, all yielding favorable results. Additionally, we compared our method with a data augmentation approach that involves creating random masks, demonstrating that our method reduces the mean absolute percentage error (MAPE) by 5%.