pure and applied geophysics最新文献

The southeast–northwest variation in the trench–volcano distance along the Middle America subduction zone is widely recognised to be due to the horizontal (flat) geometry of the descending Cocos plate (slab) in its northwestern part. In the topographic and gravitational expressions, the southeastern segment forms doubled low–high (i.e. trench–continental shelf edge–oceanic basin–volcanic arc) belts, whereas the northwestern segment forms wide doubled low–high belts. To define their attribution, the surface uplift rates were computed from a two-dimensional dislocation-based subduction model with hypocentre-based plate interface geometries. The trench-perpendicular plate-interface profiles exhibited similar curvature compositions, which were reflected in three (shallow, moderate depth and deep) convexities (downward bends) and one concavity (upward bend) between the moderate-depth and deep convex sections. The steady subduction along the first and second positive curvatures (shallow and moderate-depth convexities) in the southeastern and northwestern segments could serve as mechanical sources of short-wavelength double-arc formation. The latter two (negative and third positive) curvatures (concavity and deep convexity) in the northwestern segment produced distinct features including the narrow continental shelf, large trench–volcanic-arc distance and relevant long-wavelength double arcs. This was attributed to the direct contact of the flat elastic slab with the overriding plate.

Warm-sector rainstorms (WSR) are among the main weather events that cause significant casualties in the Sichuan Basin (SCB). These events are challenging to predict accurately using numerical models, partly due to the locally high air pollution that complicates WSR microphysical and precipitation processes. Aerosols affect the initial cloud droplet number concentration (CDNC) directly, and the CDNC is a key parameter in microphysical schemes that directly influences precipitation prediction. However, how and to what extent the CDNC affects WSR predictions in the SCB remains unclear. In this study, sensitivity experiments were conducted using a cloud-resolving model to investigate how the CDNC affects WSRs in the SCB. The study showed that when the CDNC is high, warm rainfall is reduced, while the cold rainfall is increased, which changes with convection development. First, a higher initial CDNC inhibits warm rainfall during the initial stage of convection. Second, during convection development, a higher initial CDNC accelerates graupel growth and its transformation into rainwater. The cold rainfall process plays a dominant role in this process, leading to an increase in rainfall intensity. Finally, during the convection mature stage, the promoting effect of the CDNC on the cold rainfall process weakens, leading to a decreased rainfall intensity in the higher initial CDNC. In the “initial-development-mature” stage, a higher initial CDNC distinctly affects the precipitation intensity in the form of "suppression-promotion-suppression." The findings of this study contribute to the ability to anticipate the development of WSRs based on pollution conditions in the SCB.

In recent years, new groundwater conservation policies in the Beijing-Tianjin-Hebei region have led to a rapid increase in groundwater level and a synchronous decrease in geo-electrical resistivity (GER) around Tianjin. In constrast, during the few years preceding the 1976 Tangshan M7.8 earthquake, both groundwater levels and GER around Tianjin decreased synchronously. This significant contrast seems to indicate that abnormal GER changes in Tianjin and Tangshan before the M7.8 earthquake may serve as seismogenic precursors. Therefore, in this paper, we employ combined hard inclusion models to quantitatively calculate the body strains preceding the M7.8 earthquake, and analyze their relationship with GER decreases. So far in this field, very few researches have performed theoretical calculations on the definite parameters such as the size, elastic modulus, and spatial attitude of the rheological hard inclusions, which can reasonably explain the distribution of ground deformation and seismic gaps in Tangshan and surrounding areas, as well as the asynchronous and unequal rate of GER decline. The calculation results indicate that for the 1976 Tangshan earthquake, the distribution and evolution of the precursor can be well studied by setting three ellipsoidal rheological hard inclusions with different elastic moduli through aftershock distribution. This work provides a valuable case for applying hard inclusion theory to the medium-term and short-term evolution of other strong earthquake.

The gravity value at the surface of the Earth can be changed due to land subsidence and underground water depletion. Absolute gravity measurements show a gravity increase of ~ 169 µgal at Kerman station in southeastern Iran during 2004–2017. InSAR vertical map (2017–2019) reveals displacement rates of -3.5 cm/year at the Kerman site and a maximum of -25 cm/year at the center of the plain. Kerman GPS measurements (2011–2018) indicate -4.3 cm/year of vertical displacement rate. The geometrical contribution of the subsidence to the gravity variation at this site is + 140.2 and + 172.2 μgal using InSAR and GPS, respectively. In situ measurements of the groundwater table show a 17 cm/year depletion rate, leading to minimum and maximum values of − 27.8 and − 46.4 µgal in the induced gravity change assuming a 30–50% porosity range. The sum of induced hydrological and geometrical gravity changes is found to be smaller than the observed gravity variation at Kerman station, underlying a variable subsidence rate in time. The decrease in subsidence rate, observed at some urban leveling benchmarks, is probably due to the westward development of Kerman city, the lack of a proper sewage system, as well as the decrease in water extraction because of land use change. Assuming that the subsidence rate was larger at the beginning of the absolute gravity measurement period and decreases with time, most of the gravity increase at the Kerman station can be explained by subsidence with only a small water mass change contribution.

Nowadays, studies investigating variations in Total Water Storage (TWS) have gained significant recognition through the analysis of the Global Navigation Satellite System (GNSS) vertical coordinate time series. This study focuses on the island of Haiti. The vertical loading displacements caused by TWS are calculated using the Global Land Data Assimilation (GLDAS) hydrological data model. A comparison between the annual signals of the hydrological model and GNSS data from 32 stations reveals the presence of the TWS signal in the GNSS time series. Despite the island of Haiti exhibiting a relatively small GNSS signal, the correction for hydrological effects computed with GLDAS results in a reduction of the Root-Mean-Square (RMS) scatter of the GNSS time series vertical displacement for 62.5% of the stations. The station with the most notable improvement shows a significant 50% reduction in RMS, along with a correlation coefficient of 0.88 between the GNSS and hydrological displacements.

Integration of land and marine vertical datums is an important aspect of geospatial reference systems. Therefore, this study has been conducted to identify an optimum approach to integrate the marine and land vertical datums. Two hybrid geoid models have been developed and fitted to the the land levelling datum at benchmark and to the tide gauge-benchmark station (TGBM). The differences between the two hybrid geoid models were computed to establish a vertical datum transformation model (VDT). Among the 305 GNSS-levelling points, 295 have been used in the hybridization process and 10 have been used for validation. Based on the comparison, the geoidal differences at the 10 points range from −7.2 to 7.0 cm while the mean and RMSE of differences are 1.3 cm and ± 4 cm, respectively. The second hybrid geoid, which was fitted to local MSL, was developed by directly adding to the offset between the gravimetric geoid and local MSL at nine TGBM stations. The result indicates that the offset derived at Tanjung Gelang is the optimum one with an RMSE of ± 0.045 m. The VDT model developed shows a transformation accuracy of approximately ± 4 cm.

Modeling monthly stream flows most accurately is of vital importance for water resource management, agricultural irrigation and efficient hydroelectric energy production, especially in semi-arid areas. Soft computing approaches have recently taken an important place in estimating streamflow time series. The potential of various data-driven approaches to predict streamflow in challenging climate conditions was evaluated. The study used machine and deep learning algorithms to model average monthly stream flows in two stream gauging stations in semi-arid region of the Konya closed basin where agriculture is at the forefront, accurate and reliable estimation of the stream flows is the basis of the study. For this, the performances of emotional neural network algorithm (EmNN), long-short term memory (LSTM), Elman neural network (ENN), nonlinear autoregressive exogenous model (NARX), recurrent neural network (RNN), group method of data handling (GMDH) were compared. The study’s unique contribution lies in its comprehensive comparison of these diverse algorithms, including newer approaches like EmNN, in the specific context of semi-arid hydrology. Partial autocorrelation analysis was applied to select input combinations, and lagged values exceeding 95% confidence limits were presented to the models as the most essential features. Artificial intelligence (AI) models use lagged stream flows to predict the streamflow time series. Statistical parameters, scatter diagrams and a time series approach are used to compare model performance. The GMDH model produced the following test results for 1604 no station: KGE: 0.656, R²: 0.608, NSE: 0.343, RMSE: 27.021, MAE: 3.834, MAPE: 0.662, MBE: −0.217, BF: 0.972. Similarly, for 1623 no station, the GMDH model yielded the following test results: KGE: 0.770, R²: 0.615, NSE: 0.531, RMSE: 0.006, MAE: 0.047, MAPE: 0.217, MBE: −0.012, BF: 0.956. In addition, the EmNN algorithm was the approach with second prediction accuracy. The findings of the study are important resources for optimizing the selection of AI models for streamflow prediction in semi-regional areas. The study also provides critical information for policymakers and decision-makers in similar climate zones worldwide for water resource management.

The challenges inherent in mining environmental liabilities, especially in radioactive mineral contexts, highlight the crucial importance of rehabilitating and properly managing degraded areas and tailings. In radioactive minerals mining, the challenges are accentuated due to the complexity of the materials and the environmental risks associated with persistent radioactivity. This scenario underlines the critical need for precise environmental management strategies, highlighting the importance of geophysical techniques for monitoring and mitigating environmental risks in radionuclide retention ponds. Geophysical techniques, such as electrical tomography and seismic tomography refraction, are interesting tools for identifying anomalies in the subsoil, such as leaks, fractures and contamination zones, which are not visible on the surface. These methods provide a non-invasive means of continuously monitoring the integrity of tailings storage facilities, allowing for early detection of potential failures or contamination pathways. By offering a more spatial understanding of subsurface conditions compared to traditional geotechnical instrumentation, geophysics plays an important role in mitigating environmental impacts, reducing risks to nearby ecosystems and informing rehabilitation efforts in radioactive mineral mining areas. This study applied electrical and seismic methods to assess two retention ponds at a uranium mine, demonstrating how these techniques can help in the safe decommissioning of mining facilities and the sustainable management of environmental liabilities. With a focus on two retention ponds of a uranium mine in South America in the process of decommissioning, the results revealed conductive electrical anomalies and variations in the geological layers identified by electrical tomography and refraction seismic, respectively, indicating potentially contaminated areas and alterations in the degree of fracturing of the foundation rock of the ponds. Comparing these results with a structural survey of fracture orientations in the study area demonstrates the preferential path of underground flow, conditioned by the fracturing pattern of the weathered rocks. These findings emphasize the importance of geophysics in the decommissioning phase of nuclear facilities, not only to monitor stored environmental liabilities, but also to assist in the recovery of degraded environments in the proximity of the mines.

The Black Sea coasts from the northwest of Turkey through Crimea to Georgia were strongly affected by severe storms in Autumn, 2023. The aim of the work is to compare the performance of different wave model approaches and wind datasets in extreme weather conditions in the Black Sea. The study covers the continuous period from the 1st to the 30th of November including two strong storms with wave heights up to 9–10 m. Wave simulations are performed using WAM and the 2D parametric model for surface wave development suggested in Kudryavtsev et al. (2021a). The wave models are forced by hourly wind fields from four datasets: ECMWF Reanalysis (ERA5), ECMWF Level-4 bias-corrected operational model, NCEP (CFSv2), and the regional WRF-ARW model with 6-hour NCEP/NCAR atmospheric forecast as input. The high-resolution Level-4 wave analysis for the Black Sea produced by CMEMS (also using WAM Cycle 6) is also considered. Simulation results are validated against along-track altimeter measurements of significant wave height, CFOSAT SWIM information on dominant wavelength and wave direction, and in-situ data from an oceanographic platform near Crimea. All models demonstrate their overall good performance, though third-generation wave spectral models give an expectedly higher correlation between simulations and observed data, while the parametric model is less accurate. Some recommendations to combine wind and wave models for the most accurate predictions are further given. As known, the wind speed fields produced by ECMWF are underestimated at winds higher than 15–20 m/s. While the wind correction is crucial when using the parametric model, WAM better reproduces the observed extreme waves without it. As also obtained, WAM simulations forced by NCEP and WRF winds lead to an overestimation of the largest storm waves. Increased resolution of the wind fields does not lead to significant improvement in the quality of wave predictions, which can be explained by the wind accumulation effect during wave development.

The aim of this study is the operational dynamical nowcasting application over different parts of India from Indian Meteorological Department (IMD) using Doppler Weather Radar (DWR) network of multiple radars and numerical weather prediction (NWP) model. The High Resolution Rapid Refresh (HRRR) approach has been adopted to achieve this objective in which the DWR data are hourly assimilated at convective-scale in the Weather Research and Forecasting (WRF) model. The designated NWP setup implemented for very short-range to nowcasting of weather is defined as the IMD-HRRR modelling system. Various quality controls are employed before assimilating DWR data in the IMD-HRRR system. Three different domains are specified over India that cover the entire Indian landmass, and next 12-h predictions are provided from hourly cyclic assimilation experiments. The results of all domains suggested that the IMD-HRRR predictions are not degraded with forecast lengths (up to 12 h) when compared against observations e.g. Synop, Metar, Buoy, total precipitable water (TPW) from GPS stations. Minimum errors are achieved when model predictions are compared against Buoy and TPW observations. The correlation values are higher than 0.9 for all domains. Furthermore, the IMD-HRRR model forecasts are also compared with observed DWR radial winds to demonstrate auxiliary applications of the DWR data for model verifications at high spatio-temporal resolution.