pure and applied geophysics最新文献

Adequate estimation of S-wave velocity (Vs) structure is a significant parameter in the seismic micro zonation studies. To this purpose, different techniques, such as down-hole measurements and inversion of surface wave’s dispersion curves are proposed for modeling V_S profile. In the last decade, modeling Vs profile from the Rayleigh wave’s ellipticity curve (H/V) is more applicable owing to its rapid and simple data gathering procedure. However, regarding the ambiguities in the inversion of H/V curves, obtaining the reliable results priori information, such as down-hole measurement, to constrain the final Vs model is vital. This study addressed this challenge, and based on a hybrid artificial intelligence method introduced a new technique to invert the Rayleigh wave ellipticity curve with acceptable performance. To do that, first model parameters (i.e. number of layers and corresponding thicknesses and shear wave velocities) by the ensemble of neural networks (ENN) were predicted, and then further inversion by jellyfish searching (JS) algorithm (named ENN-JS inversion method) was carried out to obtain a more reasonable Vs model. To build the ensemble system, ten base networks were arranged. To train the neural networks, synthetic Rayleigh wave ellipticity data by forward modeling approach were generated. The combination of the outputs of based networks was performed using the averaging method. Then, JS inversion algorithm was applied to estimate the final adequate Vs model. ENNs provide essential information to the JS searching algorithm on the number of layers and proper search spaces for model parameters. Synthetic and actual datasets tested the ENN-JS inversion technique. Findings show the proposed method provides a robust method for the inversion of Rayleigh wave ellipticity data.

The relationship between particulate matter and economic growth, as well as the relationship between economic growth and Greenhouse Gas emissions, has been the topic of considerable investigations over the past two decades. Kuwait has a hot, dry, and desert climate that makes the outside air affected by natural and other unnatural factors. Fine Particulate Matter (PM_2.5) samples were monthly collected for a 41-years (from 1980 to 2021) over the state of Kuwait. This study presents a detailed investigation of possible correlation and regression analysis between PM_2.5 mass column concentration and socioeconomic factors, and they are as follows: GDP per Capita (GDPP), Greenhouse Gas emissions, and population density during the same period. The correlation between per Capita GDP and PM_2.5 concentration is statistically positive and supported at the highest level of significance. The Greenhouse Gas emissions and population density proportion exhibit significant positive effects, demonstrating that these two factors strongly affect PM_2.5 pollution. The results of the regression analysis for Kuwait shows a significant positive relationship between GDP per Capita and PM_2.5, all of which remained significant at the 1% level. The consequence of the increase in per Capita GDP, according to the results reported in the study, should be an increase in the level of PM_2.5 column density and vice versa. A significant positive correlation with a value of 0.8805 was found between Physiological Equivalent Temperature (PET) in extremely hot years and Gross Domestic Product (GDP). Human activities lead to an environmental imbalance, and this will certainly affect future generations, so what is required to do is to feel a moral responsibility towards the environment around us.

Marine heatwaves (MHWs) are extreme ocean events with prolonged discrete periods of anomalously warm water, that have significant impacts on fisheries, tourism, and marine ecosystems. We identify MHWs as discrete periods (≥ 5 days) when the sea surface temperature exceeds the threshold (90th percentile) of the sea surface temperature distribution for specific calendar days and analyze their main properties in the Baltic Sea for the period 1993−2022. Also, we investigate the main mechanisms of evolution one of the most intense and continuous MHW, observed from October 2000 to March 2001. We use temperature, salinity, mixed layer depth, and current velocity daily data from regional reanalysis of the Baltic Sea (2 nautical mile horizontal resolution, vertical step from 1 m on the surface to 24 m on the bottom). We also use monthly data from global climate reanalysis ECMWF ERA5 (0.25° × 0.25°) and meteorological stations of the Swedish Meteorological and Hydrological Institute. From 40 to 90 MHWs with an average duration and intensity (8−24 days and 1.75−3.25 °C) were detected in various parts of the Baltic Sea during the period 1993−2022. The maximum cumulative values (> 2400 °C days) were observed in the Gotland Basins, the Gulf of Finland, and the Gulf of Riga. The mean intensity and cumulative values of MHWs are stronger in summer (3.6 °C and 740 °C days). A long existence of MHW in the autumn–winter period 2000–2001 was associated with positive air temperature anomalies (> 4 °C) and a sharp weakening of wind speed in the Baltic region.

Kinematic simulations of strong ground motions require representation of the temporal functional form of fault slip. There is a range of source time functions that are commonly used: those that are generalized from numerical simulations of crack dynamics or those that radiate the seismic spectra of the omega-n type. All are physical plausible, while the modern source-inversion studies are still unable to better constrain the choices available. The uncertainty in the kinematically simulated motions due to the ambiguity in assigning an underlining form of fault slip still requires rigorous quantification. The representation integral of elasticity is an appropriate analytical tool, providing the exact seismic field in the entire practically relevant frequency band and including all near- and far-field terms. The smooth dynamically compatible version of the source time function, in which the rise time is the governing parameter, has the drawback of implicitly leading to unreasonably high slip rates and, as a consequence, unrealistically extreme ground velocities and accelerations. On the other hand, the functions, both dynamic and of the omega-n type, in which the static offset U and peak rate of slip v_max are the two independent controlling parameters, all provide nearly the same peak-motion values that match the prescribed, realistically observed coseismic fault-slip rates. With U and v_max as the correctly prescribed slip parameters, respectively controlling the low- and high-frequency ends of the radiated spectra, the choice between a dynamic or omega-n function leads to insignificant differences in radiation, causing the uncertainty in peak motions not exceeding approximately 10%.

Bedrock mapping is essential for understanding seismic amplification, particularly in sediment-filled valleys or basins. However, this can be hard in urban environments. We conducted a geophysical investigation of the sediment-filled Bolzano basin in Northern Italy, where three valleys converge. This study uses low-impact, single-station geophysical methods suitable for urban areas to address the challenges of mapping in such environments. A dataset of 574 microtremor and gravity measurements, along with three seismic reflection lines, allows for the inference of the basin’s deep bedrock morphology, even without direct stratigraphic data. The dataset facilitates a detailed analysis of the spatial patterns of resonance frequencies and amplitudes, revealing 1D and 2D characteristics of the resonances. Notably, 2D resonances predominate along the Adige valley, i.e., the deepest part of the basin with depths up to 900 m. These 2D resonances, which cannot be interpreted through simple 1D frequency-depth relationships, are better understood by integrating gravity data to develop a depth model. The study identifies resonance frequencies ranging from 0.27 to over 3 Hz in Bolzano, affecting different building types during earthquakes. Maximum resonance amplitudes occur at lower frequencies, specifically at 2D resonance sites, therefore primarily impacting high structures. The 2D resonances are directional, with the most significant amplification occurring longitudinally along the valley axes. The resulting 3D bedrock model aids in seismic site response modeling, hydrogeological studies, and geothermal exploration and provides insights into the geological history of the basin, highlighting the role of the Adige Valley as a major drainage pathway.

The present study was designed to provide a model for surface soil moisture numerical estimation. This assessment is done based on the direct ground measurement of soil moisture in 5 cm (SM5) and 10 cm (SM10) depths using machine learning models. To do this, various meteorological variables (16 variables) were used as model inputs. The data were evaluated on a daily scale during 2017–2020. Of these data, 75% of days were randomly considered as train and 25% as test. The components relevant to air and soil temperature, relative air humidity, evaporation, and vapor pressure are the most important factors that affect daily soil moisture. A mixture of these variables is used as model input. For this purpose, two machine learning models, including a multilayer perceptron (MLP) neural network and an adaptive neuro-fuzzy inference system (ANFIS) were used. Three agriculture meteoritical stations located in three different climates were assessed: (1) Gharakhil Station (semi-humid and moderate), Zarghan Station (semi-arid and cold), and Zahak Station (extra-arid and moderate). According to the comparison between estimates and measurements, both models had a relatively desired performance in Gharakhil and Zarghan (57% < R2 < 66% for SM5 and 45% < R2 < 58% for SM10). However, the performances were weak and almost unacceptable in the extra-arid Zahak climate (14% < R2 < 17% for SM5 and 18% < R2 < 22% for SM10). According to the relative root mean square error (RRMSE) and Nash–Sutcliffe value of stations, the models in humid climates, performed better than those in arid and extra-arid climates. The best RRMSE value was obtained by ANFIS in Gharakhil Stations (0.193 for SM5 and 0.178 for SM10), while the weakest RRMSE value was obtained in Zahak Station, which equaled 0.887 (via MLP) and 0.767 (via ANFIS) for SM5 and SM10, respectively. The applied models were not superior to each other; however, the ANFIS model was slightly superior to MLP in most cases.

A micrometeorological fog experiment was carried out in Budapest, Hungary during the winter half year of 2020–2021. The field observation involved (i) standard meteorological and radiosonde measurements; (ii) surface radiation balance and energy budget components, and (iii) ceilometer measurements. 23 fog events occurred during the whole campaign. Foggy events were categorized based on two different methods suggested by Tardif and Rasmussen (2007) and Lin et al. (2022). Using the Present Weather Detector and Visibility sensor (PWD12), duration of foggy periods are approximately shorter (~ 9%) compared to ceilometer measurements. The categorization of fog based on two different methods suggests that duration of radiation fogs is lower compared to that of cloud base lowering (CBL) fogs. The results of analysis of observed data about the longest fog event suggest that (i) it was a radiation fog that developed from the surface upwards with condition of a near neutral temperature profile. Near the surface the turbulent kinetic energy and turbulent momentum fluxes remained smaller than 0.4 m² s^–2 and 0.06 kg m^–1 s^–2, respectively. In the surface layer the vertical profile of the sensible heat flux was near constant (it changes with height ~ 10%), and during the evolution of the fog, its maximum value was smaller than 25 W m^–2, (ii) the dissipation of the fog occurred due to increase of turbulence, (iii) longwave energy budget was close to zero during fog, and a significant increase of virtual potential temperature with height was observed before fog onset. The complete dataset gives an opportunity to quantify local effects, such as tracking the effect of strengthening of wind for modification of stability, surface layer profiles and visibility. Fog formation, development and dissipation are quantified based on the micrometeorological observations performed in suburb area of Budapest, providing a processing algorithm for investigating various fog events for synoptic analysis and for optimization of numerical model parameterizations.