Reflection residual static correction optimized using stacked energy is an effective means to solve the problem of residual static correction with high accuracy.However,it suffers from a large amount of calculation and multi-domain data switching.Due to the growing volume of raw data,efficient computation has become a bottleneck in practical applications.In this paper,we analyze the computation-intensive and communication-intensive characteristics of reflection residual static correction optimized using stacked energy,and put forward a technical solution based on the Spark distributed in-memory computing model to address the is-sue of parallel computation.We realize high-efficiency transfer of elastically distributed mass seismic data and flexible switching of multi-domain data,and accomplish multi-node distributed parallel computing of reflection residual static correction optimized using stacked energy,with significantly improved computational efficiency and feasibility.Practical applications show that the software implementation of our method,featuring strong adaptability and high computational efficiency,is good enough for seismic data pro-cessing in the areas with complicated near-surface conditions,e.g.mountainous and desert areas.
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