pure and applied geophysics最新文献

Surface irregularities and subsurface cavities have a significant impact on seismic wave propagation, leading to either amplification or reduction of ground motion. This study focuses on creating a ground motion prediction model using artificial neural network techniques driven by a synthetically generated database. In this study, we focus on the Erlang Mountain region in Sichuan Province, China, to simulate surface ground motion using the spectral element method, considering the presence of underground cavities in the research area. The classical back propagation neural network model is used to predict changes in ground motion. The model is designed to forecast the PGA influence coefficient, and 5% damped PSV amplification ratio (for periods ranging from 0.33 to 10 s). Input parameters include the buried depth of the cavity, the distance between the surface and the cavity in the mountain projection, elevation, the first gradient of the elevation, and the second-order gradient in two orthogonal directions. The model’s performance falls within acceptable error limits. Additionally, the significance of input features is analyzed, and the model’s applicability in other regions of the ErLang Mountain is validated.

Complex measurements being performed with a network of stations at different epicenter distances allowed to register numerous effects in various geophysical fields at the moment of peak activity of the Tonga Volcano 15/01/2022. Monitoring was also performed at the "Mikhnevo" Geophysical Observatory of Sadovsky Institute of Geospheres Dynamics of Russian Academy of Sciences. The aim was to detect the links between disturbances in the atmosphere, changes in ambient seismic noise, and variations in underground water level. For the first time in an aseismic region the reaction of unevenly aged aquifers to a global disturbance in the form of propagating Lamb atmospheric waves (direct and antipodal ones) as they passed around the Earth have been registered. The responses recorded in different geophysical fields were compared to background values of probability density function of power spectral density. A difference in the intensities of the responses of the confined and weakly confined aquifers to two passages of atmospheric fronts from the Tonga volcanic eruption was established. This difference corresponds to the values of barometric efficiency. Delays of the hydrogeological responses with respect to the passage of Lamb waves were observed. Synchronous measurements of hydrogeological responses to passing atmospheric disturbances produced by the Tonga volcanic eruption performed in an aseismic region add to the world database of interacting geospheres and require further consideration involving the analysis of available experimental data.

This study investigates the impact of the magnitude 6.9 Menyuan earthquake in Qinghai, 2022, on surrounding areas using the elastic half-space dislocation model. It calculates the co-seismic displacement and stress fields of the Menyuan earthquake and examines the mainshock's triggering effect on aftershocks based on co-seismic Coulomb stress changes. The results show: (1) The materials converge from the southwest and northeast before dispersing from the northwest and southeast around the epicenter, with subsidence in the southwest and northeast and uplift in the northwest and southeast. (2) Stress-wise, expansion occurs southwest and northeast, while compression occurs northwest and southeast of the epicenter. (3) The Coulomb stress change displays positive and negative areas. At a depth of 5 km, the aftershock triggering ratio of magnitude 3 and above is 33.33%, while at depths of 10 km and 15 km, the aftershock triggering ratio is 100%. (4) The overall pattern of distribution of Coulomb stress variations remains consistent across the different rupture models and different depths, but the distribution in the near-field close to the fault is quite significantly different. Furthermore, the differences in overall stress distribution patterns produced by changes in crustal velocity models and friction coefficients are relatively small compared to those caused by changes in rupture models, although specific numerical discrepancies can occur at certain points.

Sea surface temperature (SST) is one of the key Essential Climate Variables for studying and monitoring Earth’s climate, besides playing an important role in physical oceanographic processes and as a boundary condition in the numerical prediction models. Understanding these processes requires the availability of accurate and consistent SST products over the global ocean, which can be fulfilled by retrieving them from satellite-based observations. Therefore, the present study exploits a supervised machine learning technique, Deep Neural Network (DNN), for the retrieval of SST using thermal infrared (TIR) split-window observations from Imager onboard India’s geostationary satellite, INSAT-3D, which was launched in 2013. A matchup dataset is prepared to train and test the DNN, comprising the collocated brightness temperatures of TIR channels of INSAT-3D Imager with the in-situ SST measurements for 2017–2020. The DNN-based algorithm exhibits a similar statistics with reference to the in-situ SST for both training and testing datasets. It is further assessed on independent INSAT-3D observations for May 2021- February 2022 to demonstrate its robustness. Moreover, the performance of the DNN is also compared to the widely used regression-based non-linear SST (NLSST) algorithm, which is presently operational for INSAT-3D. Validation against the in-situ SST shows an improvement of about 37.5% in the accuracy of SST retrieved using DNN (RMSE = 0.5 K) over the NLSST (RMSE = 0.8 K) algorithms for INSAT-3D Imager.

Extracting the effective information of a tectonic stress field from in-situ stress data is significantly important for engineering projects and solid Earth sciences but extremely difficult to perform because of the scattered distribution of stress data and the complexity of rock mechanical properties. A depth–strength in-situ stress model is proposed in this study by deriving the local maximum horizontal strain (ε_H) via an analysis of the depth trend of the maximum horizontal principal stress (S_H) for granite in the southeastern Tibetan Plateau (SETP). In this model, the depth trend of S_H is divided into three segments in accordance with the values of S_H and the variations in rock mechanical properties. The first segment (0–15 MPa) shows the complexity of S_H affected by multiple factors of local settings on the near-surface. The second segment (15–45 MPa) exhibits the slope of S_H against depth mainly caused by elastic variations against the decrease in porosity (or fracture closure) and increase in pressure. The third segment (> 45 MPa) explains the stable depth trend of S_H in relation to the increase in pressure only. The ε_H can be extracted using the second segment, and it is in the range of 4 × 10⁻⁴–6 × 10⁻⁴ in the granite area of the SETP. The depth trend of stress in the shallow layer of the crust calculated based on the extracted ε_H is consistent with that from in-situ stress measurements. Moreover, the stress distribution along the Xianshuihe fault calculated based on the extracted ε_H suggests that the values of differential stress range within 200–250 MPa at a depth range of 10–16 km, a result that agrees well with local focal depths. The proposed model connects the in-situ stress in the shallow layers with the tectonic stress environment in the deep crust.

The increase in average temperatures in the last century has caused global warming, which has increased the frequency of natural disasters. Floods are one of the most important natural disasters and harm the environment and especially human life. Flood routing techniques also play an important role in predicting floods. For this reason, the accuracy and precision of flood routing calculations are of vital importance in taking all necessary precautions before the floods reach the region and in preventing loss of life. This study aims to compare the performance of machine learning, deep learning and hybrid algorithms for flood routing prediction models in the Büyük Menderes River. In this research deep learning model Long-Short Term Memory (LSTM), machine learning model Artificial Neural Network (ANN), and hybrid machine learning models empirical mode decomposition (EMD)-ANN, and particle swarm optimization (PSO)-ANN algorithms were compared to forecast the flood routing results in two discharge observation stations in the Büyük Menderes river. The analysis results of the established ML algorithms were compared with statistical criteria such as mean error, mean absolute error, root mean square error and coefficient of determination. Additionally, Taylor diagrams, box plots, and beeswarm plot visual graphs were also used in this comparison analysis. At the end of the research, it was determined that the hybrid algorithm PSO-ANN was the most successful algorithm in forecasting flood routing results among other models according to the error values of MAE: 0.2514, MSE: 0.4613, RMSE: 0.6791, NSE: 0.941 and MBE: 0.047. Moreover, the LSTM algorithm was the approach with second estimation accuracy. The findings are vital in terms of taking necessary precautions and gaining time before floods reach any region.

Nowadays, several applications in engineering and science are considering fractional partial differential equations. However, this type of equation presents new challenges to obtaining analytical solutions, since most existing techniques have been developed for integer order differential equations. In this sense, this work aims to investigate the potential of fractional derivatives in the mathematical modeling of the dispersion of atmospheric pollutants by obtaining a semi-analytical solution of the time-dependent fractional, two-dimensional advection–diffusion equation. To reach this goal, the GILTT (Generalized Integral Laplace Transform Technique) and conformal derivative methods were combined, taking fractional parameters in the transient and longitudinal advective terms. This procedure allows the anomalous behavior in the dispersion process to be considered, resulting in a new methodology called α-GILTT. A statistical comparison between the traditional Copenhagen experiment dataset (moderately unstable) with the simulations from the model showed little influence on the fractional parameters under lower fractionality conditions. However, the sensitivity tests with the fractional parameters allow us to conclude that they effectively influence the dispersion of pollutants in the atmosphere, suggesting dependence on atmospheric stability.

The dependency of rotations on translations in the classical elastic theory and the mismatched experimental rotations to the theoretical one leads to the exploration of the reduced micropolar theory. A mathematical model is presented for the response of a layered reduced micropolar half-space subjected to a seismic buried source. In this study, the Cartesian coordinate system is used to derive the proposed formulation, which is helpful to understand the effect of extended seismic sources. In the sequel, full space solutions are derived subjected to impulsive seismic moment tensor in the reduced micropolar medium. The numerical results for the multilayered medium are presented for the point sources, and obtained rotations in the layered reduced micropolar half-space (L-RMHS) are high from the layered elastic half-space (L-EHS). The response in homogeneous reduced micropolar half-space (H-RMHS) is comparatively low compared to the L-RMHS for point sources and as well for finite-fault. The rotational faulting effect is found to be negligible in the far-field region.

We adapt the Metropolis–Hastings (MH) algorithm to facilitate construction of the ensemble solution of the nonlinear implicit inverse problem. The solution variable is the aggregation of the parameters of interest (model parameters) and the data. The prior probability density function (pdf) is the possibly-non-Normal joint pdf of the prior model parameters and the noisy data, and is defined in a high-dimensional space. The posterior pdf of the solution (estimated model parameter and predicted data) is the prior pdf evaluated on the lower-dimensional manifold defined by the theory. We adapt the MH algorithm to ensure that successors always satisfy the theory (that is, are on the manifold) and provide a rule for computing the probability of a given successor. Key parts of this adaption are the use of singular value decomposition to identify subspaces tangent to the manifold, and orthogonal projection, to move a preliminary estimate of a successor onto the manifold. We apply the adapted methodology to three exemplary problems: fitting a straight line to (x,y) data, when both x and y have measurement noise; fitting a circle to noisy (x,y) data, and finding a filter that takes one noisy time series into another. In these cases, the scatter of the ensemble solution about the linearized maximum likelihood solution is roughly consistent with the linearized posterior covariance, but with some non-Normal behavior. We demonstrate the usefulness of the ensemble solutions by computing empirical pdfs of several informative statistical parameters, the calculation of which would be difficult by traditional means.