Abstract:  Most seismic data processing techniques have been designed to solve specific practical geophysical problems, with limited focus on the fundamental properties of seismic data. Seismic data vary simultaneously in time, space, and frequency, making conventional signal classification methods inadequate. Currently, there is no clear definition of the essential attributes of seismic data, which limits the development of advanced processing techniques. Here, we propose a new definition of nonstationarity tailored to seismic data, based on the unique ordering relationships of data in seismic acquisition systems and the presence of practical nondeterministic factors. Under this framework, we identify six representative nonstationary features, including statistical properties of random seismic noise, and amplitude compression, energy spectrum predictability, time-frequency spectral attenuation, waveform similarity and amplitude periodicity of effective signals. To address these features, we review the recent advances in seven corresponding processing strategies, including median filtering, sparse transformation, predictive filtering, time-frequency analysis, inverse Q-filtering with Q (quality factor) estimation, similarity analysis, and chaotic system modeling. These methods support the emerging of “two-wide and one-high” integrated acquisition schemes for massive datasets collected in “three-complex” environments—complex near-surface conditions, structural complexity, and heterogeneous lithologies—and target the “three-high and one-fast” processing goals: high signal-to-noise ratio, high resolution, high fidelity, and fast computation. This work lays the theoretical and technical groundwork for high-precision seismic exploration.

Key words:  , seismic data, nonstationary characteristics, characterization and processing techniques, high-precision seismic exploration