<p>In field seismic data acquisition, seismic traces are often affected by substantial data gaps and strong noise interference due to environmental and instrumental factors, thus degrading the resolution and signal-to-noise ratio (SNR) of the seismic profiles. Effective seismic data reconstruction and noise suppression techniques are therefore essential to recover missing signals and improve data quality. In this study, a fast projection onto convex sets (FPOCS) algorithm is proposed by incorporating an inertial parameter that involves a linear combination of the two preceding iterations based on the traditional projection onto convex sets (POCS) algorithm. Then, a weighting factor is introduced to achieve simultaneous data reconstruction and noise suppression using the weighted fast projection onto convex sets (WFPOCS) algorithm. To further suppress residual random noise in the updated solution, an optimization strategy is adopted by swapping the order of the iterative hard thresholding operator and the projection operator. The final algorithm, termed the improved weighted fast projection onto convex sets (IWFPOCS), achieves high-efficiency reconstruction and effective noise suppression. Compared with WFPOCS, the proposed method maintains fast reconstruction speed while demonstrating superior denoising performance on irregularly missing and noisy datasets. Field data experiments confirm that the proposed method significantly improves the SNR and resolution of seismic data, offering strong practical potential for subsequent processing and interpretation.</p>