Accurately picking the arrival of the P-wave is of great significance for earthquake relief and emergency rescue. The picking accuracy of some previous methods was easily disturbed by noise. We propose an automatic picking algorithm for seismic P-wave first arrivals based on the inaction method (IM) and the Akaike information criterion (AIC), called the IMAIC method, to alleviate the above problems. We use the IMAIC method to directly extract the seismic phase characteristics from the normal time-frequency transform (NTFT) spectrum to pick the P-wave arrival, which does not require inverse transformation of the NTFT. The proposed method essentially implements bandpass filtering to avoid noise interference to the greatest extent possible while optimally preserving the important features of the signal, such as the sharp direct P-wave arrival. We applied the IMAIC method to 9909 aftershock records from Wenchuan in 2008 to verify its reliability and robustness, taking the manually picked P-wave arrival as a reference. The average deviation is -0.010 ± 0.170 s when using the proposed IMAIC method. Furthermore, a comparative study of P-wave arrivals that are automatically derived using multiscale wavelet analysis and singular value decomposition (SVD)-like picking algorithms with the same data set demonstrates the effectiveness of the IMAIC method. The proposed method offers much promise for picking seismic phase onsets, even when using data sets with a low signal-to-noise ratio (SNR).

With the continuous advancement of target detection technology in remote sensing, target detection technology in images captured by drones has performed well. However, object detection in drone imagery is still a challenge under rainy conditions. Rain is a common severe weather condition, and rain streaks often degrade the image quality of sensors. The main issue of rain streaks removal from a single image is to prevent over smoothing (or underclearing) phenomena. Aiming at the above problems, this paper proposes a deep learning (DL)-based rain streaks removal framework called GSDerainNet, which properly formulates the single image rain streaks removal problem; rain streaks removal is aptly described as a Gaussian Shannon (GS) filter-based image decomposition problem. The GS filter is a novel filter proposed by us, which consists of a parameterized Gaussian function and a scaled Shannon function. Two-dimensional GS filters exhibit high stability and effectiveness in dividing an image into low- and high-frequency parts. In our framework, an input image is first decomposed into a low-frequency part and a high-frequency part by using the GS filter. Rain streaks are located in the high-frequency part. We extract and separate the rain features of the high-frequency part through a deep convolutional neural network (CNN). The experimental results obtained on synthetic data and real data show that the proposed method can better suppress the morphological artifacts caused by filtering. Compared with state-of-the-art single image rain streaks removal methods, the proposed method retains finer image object structures while removing rain streaks.

P-wave first-motion polarity is a critical parameter for determining earthquake focal mechanisms. Extracting relative P-wave arrival times and polarity information using waveform cross-correlation techniques can enhance the accuracy of earthquake location and focal mechanism inversion. However, seismic noise severely hampers the reliable detection of P-wave onsets and their first-motion polarities. To address this issue, we propose a noise-resistant polarity detection method based on the normal time–frequency transform (NTFT), termed the time–frequency similarity coefficient (TFSC). The TFSC method computes relative delays and similarity coefficients by calculating the real part of the NTFT coefficients between two seismic signals. We validated the proposed approach using both synthetic and real earthquake data. Without any filtering or preprocessing, the TFSC method demonstrated significantly greater robustness and reliability compared to the conventional time-domain normalized cross-correlation (NCC) method. These results indicate that the TFSC method has strong potential for practical application and provides a novel perspective for accurate detection of P-wave first-motion polarity in noisy seismic environments.