Optimal Seismic Sensor Placement Based on Reinforcement Learning Approach: An Example of OBN Acquisition Design

Abstract

Seismic acquisition costs are directly associated with the number of sensors used in the survey. Limiting the number of sensors in a seismic survey can be beneficial, especially when sensors are expensive to purchase, deploy, and maintain. This work explores an optimal design method for ocean bottom node (OBN) detector deployment. The proposed method is based on a reinforcement learning approach. We assume access to an initial dataset over the area of study. These data are used to extract an over-complete pre-learned basis library via the proper orthogonal decomposition method, which leads to a fast least-squares seismic data reconstruction algorithm. Then, the sensor selection procedure entails using Q-learning to find the sensor configuration that maximizes the reconstruction quality.