Earth and Space Science最新文献

Obtaining reliable low-frequency seismic data is crucial for effectively reducing cycle-skipping in full waveform inversion. However, acquiring high signal-to-noise ratio low-frequency information from field data remains a challenge. An effective solution to mitigate cycle-skipping is to utilize low-frequency information synthesized by neural networks to obtain low-wavenumber initial models. Previous attempts to reconstruct synthetic low-frequency data using supervised learning methods have shown feasibility but were limited to training with synthetic data that required labeled information. In this study, we employed an unsupervised learning method, namely cycle-consistent adversarial networks (CycleGAN), to reconstruct large-scale-feature related low-frequency information based on the high-frequency input data. Unlike supervised learning, CycleGAN allows the use of field data as input to train the network, which is more closely aligned with practical applications. Nevertheless, this approach presents challenges in terms of training complexity and potential output stability. To overcome these challenges, we reconstructed an appropriate target data set that combines high, medium, and low-frequency components and incorporated additional loss functions to enhance the network's output performance. We conducted quantitative evaluations of the method's sensitivity to the target data set and its ability to handle low-quality input data through numerical testing. The final results from field data testing confirmed the feasibility and effectiveness of the proposed method.

Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the transfer function between geophysical variables and DAS measurements. This study investigates the quantitative relationship between surface gravity DAS-recorded wave-generated strain signals along the seafloor and the pressure at a colocated sensor. A remarkable linear correlation is found over various sea conditions allowing us to reliably determine significant wave heights from DAS data. Utilizing linear wave potential theory, we derive an analytical transfer function linking cable deformation and wave kinematic parameters. This transfer function provides a first quantification of the effects related to surface gravity waves and fiber responses. Our results validate DAS's potential for real-time reconstruction of the surface gravity wave spectrum over extended coastal areas. It also enables the estimation of waves hydraulic parameters at depth without the need from offshore deployments.

We present observations from two field deployments of a calibrated tiltmeter that we name the Self-Calibrating Tilt Accelerometer (SCTA). The tiltmeter is based upon a triaxial quartz crystal accelerometer; the horizontal channels measure tilt and are periodically rotated into the vertical to obtain a measurement of the acceleration of gravity. Changes in the measured total acceleration are ascribed to drift in the vertical channel and used as calibrations for removing that same drift from the tilt time series observed between calibrations. Changes in the span (sensitivity) of the accelerometer channels can also be measured by calibrating them pointing up and down. A 3-year test on the seafloor at Axial Seamount show that the calibrations are consistent with a linear-exponential model of drift to a RMS residual of ∼0.5 μg (μrad). The calibrated tilt time series was impacted by platform settling for the first 2 years, but after repositioning the tiltmeter, the calibrated observations were consistent for the final year with the tilt observed on a nearby LILY tiltmeter, within an assumed level of drift for the unconstrained LILY sensor. A separate 15-month test in a stable vault at Piñon Flat Observatory was complicated by seasonal temperature variations of >5°C; the calibrations are consistent with a linear-exponential model of drift to ∼2 μg RMS when temperature and temperature time-derivative dependence is included. Similarly, the calibrated tilt time series was impacted by thermal deformation of the SCTA assembly. A future test in a thermally and tectonically stable borehole will be required to assess the accuracy of the SCTA.

This study examines the characteristics and development of Tropical Storm Maliksi, which is a special case of tropical cyclone developed in the northwestern part of the South China Sea during a southwest monsoon outbreak. Detailed analyses were conducted using observational data and forecast products. Surface observations, radar wind profilers, aircraft data, and satellite products were used to evaluate Maliksi's wind structure, revealing multiple circulation centers and gale force winds. Vertical wind profiles, warm core structure, wind waves, and the influence of sea temperatures and salinity on Maliksi's intensification were investigated. Regarding forecasting, AI-based models outperformed conventional numerical weather prediction (NWP) models in predicting Maliksi's initial development, though both struggled to capture the persistence of strong winds as the system moved inland. High-resolution NWP simulations were employed to examine terrain-induced wind variability around Hong Kong International Airport, revealing the mountain wake effect and uneven wind and turbulence distribution. These findings provide insights into the challenges of forecasting and monitoring such tropical cyclones, and highlight the need for enhanced observational platforms and forecasting tools along coastlines vulnerable to these systems.

In GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high-accuracy global grid-wise weighted mean temperature (T_m) values, which can be utilized to calculate the conversion factor. However, the T_m provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new T_m lapse rate model for each grid point was developed for the purpose of reducing its surface T_m to the elevation of the GNSS site. Then the reduced T_m values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated T_m for the GNSS station. The sample data for the development of the new model were the T_m profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the T_m lapse rate model significantly enhanced the accuracy of interpolating T_m values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new T_m lapse rate model to adjust surface T_m data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology.

Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in-depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low-quality observations was to combine the quality flag and difference from TanDEM-X, striking an optimal balance between eliminating poor-quality data and preserving a maximum number of high-quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.

We propose a workflow for the recognition of the hierarchical segmentation of faults through earthquake hypocenter clustering without prior information. Our approach combines density-based clustering algorithms (DBSCAN and OPTICS), and principal component analysis (PCA). Given a spatial distribution of earthquake hypocenters, DBSCAN identifies first-order clusters, representing regions with the highest density of connected seismic events. Within each first-order cluster, OPTICS further identifies nested higher-order clusters, providing information on their number and size. PCA analysis is applied to first- and higher-order clusters to evaluate eigenvalues, allowing discrimination between seismicity associated with planar features and distributed seismicity that remains uncategorized. The identified planes are then geometrically characterized in terms of their location and orientation in the space, length, and height. This automated procedure operates within two spatial scales: the largest scale corresponds to the longest pattern of approximately equally dense earthquake clouds, while the smallest scale relates to earthquake location errors. By applying PCA analysis, a planar feature outputted from a first-order cluster can be interpreted as a fault surface while planes outputted after OPTICS can be interpreted as fault segments comprised within the fault surface. The evenness between the orientation of illuminated fault surfaces and fault segments, and that of the nodal planes of earthquake focal mechanisms calculated along the same faults, corroborates this interpretation. Our workflow has been successfully applied to earthquake hypocenter distributions from various seismically active areas (Italy, Taiwan, and California) associated with faults exhibiting diverse kinematics.