A near-surface Q value prediction method based on neural network algorithm

The near-surface Q value describes the absorption and attenuation characteristics of near-surface formations on seismic waves. Obtaining accurate near-surface Q values and effectively compensating seismic data is crucial for improving the resolution of seismic data. While the accuracy of near-surface Q value estimation based on micro-logging data is high, it is also costly. Therefore, it is important to integrate the results of near-surface Q value surveys and make full use of past near-surface velocity surveys for predicting near-surface Q values. Current methods for predicting near-surface Q values primarily utilize location or velocity information, but the stability of the prediction results is poor and there are certain errors. Therefore, this paper proposes a near-surface Q value prediction method based on the BP neural network algorithm. By leveraging the non-linear characteristics of the neural network algorithm, information such as near-surface velocity, thickness, travel time of seismic waves near the surface, and near-surface location are incorporated into the prediction of near-surface Q values, which improves the stability and accuracy of the prediction.