Automatic First Arrival Picking for Seismic Data using Kalman Filter

The first arrival time of seismic waves is a crucial parameter for seismic data analysis, which is used to determine the depth and location of subsurface structures. However, the estimation of first arrival time is often challenging due to the presence of noise and uncertainties in the data. In this study, we propose a novel approach that utilizes the Kalman filter with generalized likelihood ratio (GLR) to estimate the first arrival time in seismic data. First, a discrete time linear system with unknown amplitudes changes occurring at unknown time instants is used to reformulate the convolutional model. Then, to provide residual signals, we apply a Kalman filter based on the no change hypothesis to the linear system. Finally, to estimate the first arrival time, a generalized likelihood ratio (GLR)-based change detection technique is utilized. Using the simulated data, we verified the performance of the proposed approach. Overall, this study presents a promising approach for improving the accuracy of first arrival picking in the presence of different noise levels.