The March 11, 2011, MW9.0 Tohoku-Oki earthquake, in Japan, caused rapid strain release near the epicenter, while the Boso Peninsula, located farther away, experienced stress redistribution, leading to changes in the recurrence interval of slow slip events (SSEs) and regional strain. This study focuses on three detected post-2011 Boso SSEs, utilizing a segmented model displacement time series measured by Global Navigation Satellite System (GNSS) to calculate velocity and strain rate fields for eight periods before, during, and after the SSEs. Results show that the 2011 earthquake and the three SSEs significantly alter the velocity field in the Boso region, with SSE velocities predominantly oriented southeast, reaching maximum values of 26.9 cm/a, 10.6 cm/a, and 38.5 cm/a—nearly opposite to non-SSE periods. After the third SSE, the velocity field nearly returns to its pre-earthquake state, with a maximum of 1.8 cm/a. The maximum shear strain rates during the three SSEs are 25.88 × 10-7 a-1, 11.38 × 10-7 a-1, and 29.02 × 10−7 a−1 (i.e., per annum), significantly higher than those during non-slow slip periods, with principal strain rates following a similar pattern. The spatial distribution of strain rates during the SSEs indicates greater deformation compared to the non-slip periods, dominated by northwest-southeast extension and southwest-northeast compression. Spatiotemporal analysis reveals a strong correlation between seismic frequency and strain rate during the SSEs, with time correlation coefficients of 0.85, 0.88, and 0.9. Although larger accumulated strain results in stronger strain release during the latter two SSEs, not all strain is fully released, suggesting that earthquake swarms accompanying the SSEs may contribute to the partial release of unreleased strain. This study, through the analysis of GNSS data, evaluates the spatiotemporal distribution of strain fields during periodic SSEs, contributing to further research on strain accumulation and release, and aiding in the analysis of this regional seismic activity.

Various slow slip events(SSEs) with distinct characteristics have been detected globally, particularly in regions with dense Global Navigation Satellite Systems(GNSS) networks. In the Hikurangi subduction zone of New Zealand, SSEs frequently occur alongside seismic activity, especially in the Manawatu and Kapiti regions. This study analyzes the 2021—2023 Kapiti-Manawatu long-term SSE using daily displacement data(2019—2023) from 53 GPS stations. The network inversion filter(NIF) method is applied to extract slow slip signals, revealing spatial migration with alternating slip between Kapiti and Manawatu, characterized by distinct phases of acceleration and deceleration. Manawatu exhibits higher slip rates, exceeding 4 cm/month, with greater cumulative slip and surface displacement than Kapiti. A moderate temporal correlation(coefficient 0.59) between seismic activity in the region and slip acceleration in Manawatu suggests that seismic events may contribute to the slip, while no significant correlation is observed in Kapiti.

Various slow slip events (SSEs) with distinct characteristics have been detected globally, particularly in regions with dense Global Navigation Satellite Systems (GNSS) networks. In the Hikurangi subduction zone of New Zealand, SSEs frequently occur alongside seismic activity, especially in the Manawatu and Kapiti regions. This study analyzes the 2021u20132023 Kapiti-Manawatu long-term SSE using daily displacement data (2019u20132023) from 53 GPS stations. The network inversion filter (NIF) method is applied to extract slow slip signals, revealing spatial migration with alternating slip between Kapiti and Manawatu, characterized by distinct phases of acceleration and deceleration. Manawatu exhibits higher slip rates, exceeding 4 cm/month, with greater cumulative slip and surface displacement than Kapiti. A moderate temporal correlation (coefficient 0.59) between seismic activity in the region and slip acceleration in Manawatu suggests that seismic events may contribute to the slip, while no significant correlation is observed in Kapiti.

Geophysical exploration is important for road construction and maintenance. Before road construction, geophysical exploration is required to detect the geological structure to ensure the construction of the road; after the road is completed, geophysical exploration is still required to detect diseases in time. As a traditional geophysical exploration method, seismic exploration is important in road detection for its large detection depth and high resolution. The seismic attribute information obtained from seismic data can reflect many hidden information. As an important seismic attribute extraction method, time-frequency analysis method can simultaneously describe the energy density and intensity of seismic signals at different times and frequencies, which is of great significance to geological interpretation. However, traditional time-frequency analysis methods are low resolution and insufficient focusing. In this paper, on the basis of linear time-frequency analysis, the L1 norm constraint will be introduced, and the time-frequency analysis method will be implemented from the perspective of inversion, so as to reduce the influence of the multi-solution of the method and improve the method's resolution and focusing. In this paper, two numerical simulation data and one real seismic data of road detection are employed to test the proposed new method.

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The identification of pavement cracks is critical for ensuring road safety. Currently, manual crack detection is quite time-consuming. To address this issue, automated pavement crack-detecting technology is required. However, automatic pavement crack recognition remains challenging, owing to the intensity heterogeneity of cracks and the complexity of the backdrop, e.g., low contrast of damages and backdrop may have shadows with comparable intensity. Motivated by breakthroughs in deep learning, we present a new network architecture combining the feature pyramid with the attention mechanism (PSA-Net). In a feature pyramid, the network integrates spatial information and underlying features for crack detection. During the training process, it improves the accuracy of automatic road crack recognition by nested sample weighting to equalize the loss caused by simple and complex samples. To verify the effectiveness of the suggested technique, we used a dataset of real road cracks to test it with different crack detection methods.

Using Global Positioning System (GPS) coordinate time series, we detect three transient slow slip events (SSEs) offshore the Boso Peninsula in central Japan during 2011–2019. To extract the tiny SSE signals obscured by the significant post-seismic deformation after the 2011 MW9.0 Tohoku earthquake, we develop a new GPS coordinate time series processing software to obtain these SSE-induced deformations from high-noise GPS data. In addition, we apply the principal component analysis-based inversion method (PCAIM) to get the spatio-temporal slip distribution of the three SSEs. The spatio-temporal evolutions of these slips reveal that the nucleation styles are different. Compared to the 2011 and 2018 SSEs, the 2013–2014 SSE displays faster slip spatio-temporal variation, deeper slip, shorter slip duration, minor seismic moment, and lower maximum slip rate. The 2018 SSE exhibits the most significant seismic moment, the maximum slip, and the maximum slip rate of these three SSEs. The spatio-temporal variations of the 2011 SSE are the most complex, containing two acceleration and deceleration phases. The slip zone expanded along the eastern side of the Boso Peninsula in the acceleration phase and shrank back in the deceleration phase. Furthermore, the recurrence interval of SSEs spans from 2.2 to 4 years during 2011–2019, suggesting that the recurrence interval might become shorter and non-periodic due to the enormous earthquake. After the 2013–2014 SSE, the recurrence interval of the SSE gradually returns to normal. Thus, we can infer that the SSE may occur every 4–7 years after the 2018 SSE if there is no large earthquake.

Seismic wave travel time is an important seismic attribute information and is widely used in various seismic forward and inversion methods, including seismic migration imaging, seismic tomography, seismic wave forward modeling and other core seismic data processing methods. The accuracy and efficiency of the travel time algorithm are important for the above methods. In the practical application of seismic exploration, it is often carried out under the condition of undulating surface, which has a significant impact on the travel time calculation. Therefore, it is of great significance to study an efficient and high-precision travel time calculation method that can adapt to undulating surface condition. In this paper, Fast iterative method (FIM) is modified to a topography travel time calculation method. The method employs an iterative method to solve the equation of function to obtain seismic wave travel time by maintaining a narrow band called the active list, and the algorithm can update all grid nodes in the active list at a time. We will verify the travel time computing power of the FIM method through several different velocity models.

Geophysical exploration methods are important tools for landslide disaster assessment, landslide treatment scheme design, and landslide prevention engineering. Seismic exploration, as an important geophysical exploration method, plays an critical role in geological disaster evaluation. Traveltime is one of the most frequently used seismic attributes. Among many different traveltime calculation methods, the fast marching method（FMM） is featured for its advantages in high efficiency, high accuracy and strong stability. In this paper, the velocity models are established according to the real landslide models, and then the topography FMM is applied to these landslide models. The calculation results show that topography FMM outperforms in calculating the traveltime for landslides.

The Born scattering formula can be used to simulate the single-scattering wavefield by omitting high-order terms. Based on this theory, we propose a Gaussian beam Born modeling method for the 2D visco-acoustic medium. In this method, the Green's function is obtained by accumulating Gaussian beams, which can calculate the multi-arrival time wavefield, ensuring the accuracy of the modeling method. At the same time, in order to improve the computational efficiency, the Born forward modeling method employs the wavelet-bank way to synthesize local plane waves. Different from the wavelet-bank method used for acoustic wavefield modeling, we integrate the viscosity information of the medium into the wavelet-bank method, so as to simulate the single-scattering wavefield for 2D visco-acoustic media. The tests on two models show that the Gaussian beam Born forward modeling method for visco-acoustic media proposed in this paper has high calculation accuracy and efficiency.

The fast marching method (FMM) is an efficient, stable and adaptable travel time calculation method. In the realization of this method, it is necessary to select the minimum travel time node from the narrow band many times. The selection method has an important influence on the calculation efficiency of FMM. Traditional FMM adopts the binary tree heap sorting method to achieve this step. Fibonacci heap sort method to FMM will be applied in this study. Compared with the binary tree heap sorting method, the Fibonacci heap sort method can realize the minimum travel time node selection in the narrow band in a more efficient way when the number of the narrow-band nodes is huge. The new method will be verified through error analysis and two numerical model calculations.

China transported the lunar rovers to the lunar surface via the Chang'E rockets for lunar exploration. An important detection tool in the lunar rover is the lunar penetrating radar (LPR). LPR can obtain the lunar surface structure information by transmitting and receiving high-frequency electromagnetic waves. There are many types of waves received by LPR sensors, among which diffracted waves are often ignored. The diffractions in the LPR data contain a lot of underground structure information, especially for faults and small targets with high resolution. A major technical problem in effectively using the information is how to separate diffractions from different types of wave field information. This paper focuses on the diffraction issue and employs the plane wave decomposition filter method to separate the diffractions in LPR data sets. First, the records are converted into a dip field, and then the non-diffraction waves in the dip field is filtered to achieve the purpose of separating the diffractions. Three numerical models will be used to verify the effectiveness of the diffraction separation method.

Very high-rate global positioning system (GPS) data has the capacity to quickly resolve seismically related ground displacements, thereby providing great potential for rapidly determining the magnitude and the nature of an earthquake's rupture process and for providing timely warnings for earthquakes and tsunamis. The GPS variometric approach can measure ground displacements with comparable precision to relative positioning and precise point positioning (PPP) within a short period of time. The variometric approach is based on single-differencing over time of carrier phase observations using only the broadcast ephemeris and a single receiver to estimate velocities, which are then integrated to derive displacements. We evaluate the performance of the variometric approach to measure displacements using 50 Hz GPS data, which were recorded during the 2013 MW 6.6 Lushan earthquake and the 2011 MW 9.0 Tohoku-Oki earthquake. The comparison between 50 and 1 Hz seismic displacements demonstrates that 1 Hz solutions often fail to faithfully manifest the seismic waves containing high-frequency seismic signals due to aliasing, which is common for near-field stations of a moderate-magnitude earthquake. Results indicate that 10---50 Hz sampling GPS sites deployed close to the source or the ruptured fault are needed for measuring dynamic seismic displacements of moderate-magnitude events. Comparisons with post-processed PPP results reveal that the variometric approach can determine seismic displacements with accuracies of 0.3---4.1, 0.5---2.3 and 0.8---6.8 cm in the east, north and up components, respectively. Moreover, the power spectral density analysis demonstrates that high-frequency noises of seismic displacements, derived using the variometric approach, are smaller than those of PPP-derived displacements in these three components.

Satellite elevation angle and Signal-to-Noise Ratio (SNR) are usually used as measurement quality indicators for global navigation satellite system (GNSS) measurements. The relationship of quality indicators and accuracies of measurements can be expressed as stochastic models. To model the relationship for Beidou navigation satellite system (BDS) and global position system (GPS), five basic stochastic models are presented from satellite elevation angle and SNR. Also, coefficients of these models are refined. It's found that SNR stochastic models with same coefficients can't treat all measurements from BDS and GPS. Moreover, stochastic models with an additive constant could model the relationship better. The performance of the five models are tested, independent and combined, in BDS/GPS precise positioning. The results show that refined stochastic models could improve the success rate of integer ambiguity single-epoch solution 8 % comparing to empirical models. Models with an additive constant could improve the success rate 10 % comparing to models without additive constants. SNR model with an additive constant performs better in performance for integer ambiguity resolution, especially for low elevation satellite or combined system. Using stochastic models with an additive constant, ratios of posteriori and prior variances are closer to 1 in precise positioning. Therefore, for the used receivers, we suggest to choose refined stochastic models with an additive constant, and give priority to SNR model. Here, a refinement and assessment method is proposed to derive proper stochastic models for GNSS data processing, taking into account the differences between navigation satellite systems (e.g. BDS and GPS) and stochastic models.

High-rate GNSS plays an important role in monitoring the process of strong ground motion, such as seismic activity. To achieve real-time monitoring, a reasonable solution and filtering method are needed, especially for the 50 Hz data. Based on the analysis of differential and combination method, effects of ionosphere and troposphere have obvious trend within a short time, but cannot be treated as linear trend completely. To monitor real time surface deformation accurately, a newly developed strategy is proposed in this paper, it can effectively reduce atmospheric error of double-difference in real-time. 50 Hz data are obtained during Lushan earthquake (Ms7.0), Sichuan, China; the coseismic deformations are captured successfully on the sites of continuous operation reference station (CORS) stations. With 3D grid searching method, the obtained earthquake focus is basically consistent with USGS published results. It shows that the proposed method is effective and reasonable for strong ground motion monitoring by using high-rate GNSS observations, especially for seismic activity.

50 HZ Global Positioning System (GPS) high rates data of 8 Continual Operation Reference System (CORS) sites nearby Lushan are used to analyze the earthquake trigger time, epicenter and magnitude. All sites position time series on N, E directions are resolved with the track software, and horizontal accuracy can reach to 2 cm at least. Horizontal position time series analyses show that the earthquake mainly affects QLAI, SCTQ, YAAN sites, the horizontal peak amplitudes can reach to 50 mm, the maximum instantaneous velocity can reach to 72.36 mm/s and the maximum instantaneous acceleration have reached to 105.9 mm/s(2). Analyzing the position time series by the method of S transformation, the arrival time of seismic wave is estimated. With seismic wave arriving time and coordinates of three sites which are first detecting the seismic wave, the earthquake's epicenter and trigger time can be fast determined by three-dimensional search method. Moreover, the earthquake magnitude can also be estimated by horizontal peak amplitudes from the sites using the regression method. These suggest exiting GPS infrastructure could be developed into an effective component of earthquake assessment.