Automatic first-arrival picking method via intelligent Markov optimal decision processes

Abstract

Abstract Picking the first arrival is an important step in seismic processing. The large volume of the seismic data calls for automatic and objective picking. In this paper, we formulate first-arrival picking as an intelligent Markov decision process in the multi-dimensional feature attribute space. By designing a reasonable model, the global optimization is carried out in the reward function space to obtain the path with the largest cumulative reward value, to achieve the purpose of automatically picking up the first arrival. The state-value function contains a distance-related discount factor γ, which enables the Markov decision process to pick up the first-arrival continuity to consider the lateral continuity of the seismic data and avoid the bad trace information in the seismic data. On this basis, the method of this paper further introduces the optimized model that is a fuzzy clustering-based multi-dimensional attribute reward function and structure-based Gaussian stochastic policy, thereby reducing the difficulty of model design, and making the seismic data pick up more accurately and automatically. Testing this approach in the field seismic data reveals its properties and shows it can automatically pick up more reasonable first arrivals and has a certain quality control ability, especially the first-arrival energy is weak (the signal-to-noise ratio is low) or there are adjacent complex waveforms in the shallow layer.