As the eLoran signal propagates, its strength gradually diminishes with increasing distance, making subsequent signal capture and terminal development challenging. To address this phenomenon, this paper proposes an improved method based on wavelet hard thresholding. This method applies hierarchical processing to the coefficients obtained after wavelet decomposition, based on the signal’s center frequency. It effectively addresses issues like the disappearance of trailing edges and the presence of the noise with large coefficients. Simulation results show that the improved method has the largest output signal-to-noise ratio and effectively improves the problem of tailing vanishing and eliminates the noise with large coefficients. In analog source signal testing, the results show that the method can extract signals of 30 dBμv/m and above well. In actual signal testing, the improved method can extract eLoran signals transmitted over a distance of approximately 1000 km. Based on the results, it can be deduced that the input signal-to-noise ratio is −28.8 dB. Therefore, this method is a suitable and effective solution for extracting weak eLoran signals, providing strong support for signal monitoring in areas at the coverage boundaries of eLoran signals.

Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky waves propagate over long distances by reflecting off the ionosphere. This paper utilizes the raw observation data received by the Low-Frequency Time Code dissemination monitoring stations before and after the 6.8 magnitude earthquake in Luding, Sichuan, China on 5 September 2022. A Low-Frequency Time Code time service monitoring system was built in Xi’an to continuously monitor the 68.5 kHz time signal broadcast by the BPC station. The data was then processed and analyzed through visualization. Simultaneously, we analyzed the signal fluctuation for multiple days before and after the earthquake to see the changes in the Low-Frequency Time Code signal during the earthquake. By combining seismic activity, solar activity, and geomagnetic data, this study aims to explore the causes and patterns of signal parameter variations. The results show that the field strength of the Low-Frequency Time Code signal fluctuated significantly within a short period during the earthquake. The value began to decrease about 60 min before the earthquake, dropping by approximately 8.9 dBμV/m, and gradually recovered 2 h after the earthquake. The phase also mutated by 1.36 μs at the time of the earthquake, and the time deviation fluctuated greatly compared to the 2 days before and after. Earthquake occurrences influence ionospheric variations, leading to changes in the sky wave propagation of Low-Frequency Time Code signals. Analysis of the influence of earthquakes on the propagation of Low-Frequency Time Code signals can provide references for research on Low-Frequency Time Code signal propagation models and earthquake prediction.