Combined identification and attenuation of anomalous amplitude noises in nodal land seismic data

Seismic data acquisition inevitably faces disruptions from various environmental sources, such as factories, machinery, and highways. These disturbances introduce anomalous amplitudes in seismic data, significantly compromising the signal-to-noise ratio (SNR). At present, the primary approach for denoising such noise involves direct attenuation when amplitudes surpass a predefined threshold. However, determining this threshold relies on the window size of the selected data. Small windows are unable to suppress continuous anomalous noise traces, while using larger windows to calculate thresholds results in inaccurate outcomes. Furthermore, the window-based threshold computation can inadvertently damage non-noise strong amplitude signals, like near-offset traces or surface waves. In this study, we take advantage of the distinctive characteristics of seismic data acquired by nodal instruments. These instruments not only record seismic data but also preserve pure environmental signals which are recorded ahead of the shooting time. Combining the statistical distribution of the amplitudes of pure environmental signals and deep learning techniques, we identify data samples potentially contaminated by anomalous amplitude noise in the seismic data. Based on noise identification, we propose a novel pointwise adaptive threshold (PAT) method. This entails calculating an individual threshold value for each noise sample and subsequently applying noise attenuation. The proposed method offers several benefits, including more accurate threshold computation, preservation of effective signals with strong amplitudes, reducing the dependence on picking precise first-break, and simultaneously addressing multi-trace and single-trace anomalous amplitudes. Moreover, this approach has high robustness, as misidentification in local sample points does not influence the outcomes of noise attenuation.