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We introduce a fast approach to adaptive PF for random noise attenuation in the $ t$ -$ x$ domain. Our approach uses neighboring similarity of PF to handle time-space variations in nonstationary seismic data, which is based on elementary algebraic operations and a streaming method instead of an iterative strategy. Compared with $ f$ -$ x$ deconvolution and $ f$ -$ x$ RNA methods, $ t$ -$ x$ SOPF can capture a reasonably detailed signal while avoiding artifacts that occur in the frequency-domain method. Moreover, the $ t$ -$ x$ SOPF is superior in terms of its computational costs. Experiments with synthetic examples and field data demonstrate that the proposed method is effective at attenuating the random noise in nonstationary seismic data even in the presence of curved and conflicting events.