Automatic Source Point Offset via REINFORCE Based on Transformer

Source point offset (SPO) plays a crucial role in geophysical prospecting, as it places the source points away from obstacles to facilitate exploration efforts. However, it is time-consuming for manual work to consider various intricate conditions, such as the smoothness of source lines and uniformity of fold distribution. Moreover, existing methods cost much time in optimizing one specific objective, which limits the applicability to diverse construction areas. To address this challenge, this letter leverages the widely adopted transformer architecture as the model and uses REINFORCE to train this model, by formulating the SPO as a combinatorial optimization problem. To enhance communication among candidate nodes in SPO, a graph attention layer extracts distinct information among these nodes. Experimental results on four field datasets demonstrate comparable performance of our method with the conventional method, while providing a reference for deviated geometry design quickly in field test survey.