pure and applied geophysics最新文献

Certain regions of Algeria, particularly in the Northeast, are currently facing heightened seismic activity alongside considerable social and economic challenges. Should a seismic event akin to the Djidjelli (now Jijel) earthquake of August 21 and 22, 1856, strike again, numerous coastal cities may suffer significant damage. This study is part of a broader project aimed at estimating seismic risk and damage levels following seismic events, with a particular focus on initial acceleration computation, which serves as a crucial tool for our modeling. Given the significance of conducting studies that enable the estimation of seismic risk and potential damage in urban agglomerations, the overall goal of this work is to assess seismic risk in an urban agglomeration using a deterministic scenario to estimate the risk, seismic vulnerability and damage potential. We provide a seismic risk scenario for Jijel city, with a particular focus on the susceptibility of its historically significant districts: Bourmel-Ben Achour, Ouled Aissa–Camp Chevalier, and the Old City. Using a Ground Motion Prediction Equation, we calculated the maximum expected ground acceleration based on the following considerations: (a) the 1856 Jijel seismic event as a reference; (b) site impacts associated with the area’s geological characteristics; (c) building damage; and (d) seismic vulnerability. This research presents a Peak Ground Acceleration (PGA) map that incorporates the influence of site lithology (Avib). The highest acceleration was recorded in the city center, with EC8 offering a reliable estimate of acceleration across all three examined areas: Bourmel-Ben Achour, Ouled Aissa–Camp Chevalier, and the Old City. The strongest tremors are felt in Jijel’s city center and eastern regions. Correlation with the geological features reveals an estimated PGA of 0.28 g in the Old Town area. This estimate closely aligns with the PGA of 0.52 g obtained from our independent analysis, which accounts for local lithology and site conditions. Furthermore, according to the RPA (Algerian earthquake engineering code) the Jijel province is classified as Zone IIa (medium seismicity), with an acceleration data of 0.25 g. This study integrates Geographic Information Systems (GIS) data into risk models.

The impacts of climate change on precipitation and drought are of great importance for agriculture, water resources and ecosystems. The CMIP6 models developed by the Intergovernmental Panel on Climate Change (IPCC) within the scope of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate future climate conditions under various climate scenarios and provide a better understanding of possible changes at regional and global levels. In this study, 4 different CMIP6 models, namely CANESM5, EC-EARTH3, MIROC6 and MRI-ESM2, were used to model future precipitation and temperature data in Isparta region located in the Lakes Region. Six different optimistic and pessimistic Shared Socioeconomic Pathway (SSP) scenarios, namely SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5, were considered in the modelling phase. In the projections made until 2100, it is predicted that in optimistic and pessimistic scenarios, temperature increases may reach up to 2.84 °C, 3.3 °C, 4.06 °C, 5.18 °C, 4.77 °C and 5.78 °C, respectively, and precipitation may decrease by approximately 14.9%. In addition, the results obtained from drought analyses using the Standardized Precipitation Index (SPI) show that the severity and duration of current droughts will increase significantly in the future due to decreases in precipitation and increases in temperatures in the coming years. In Isparta, which is located in the Lakes Region, a region vulnerable to drought, it is very important to develop drought management strategies in order to minimize the effects of severe droughts that may occur in the future.

Graphical abstract

Earth science has embraced the application of deep learning (DL) across various fields. The research aimed to enhance the Analog Data Assimilation (AnDA) approach by integrating a DL technique. This involved using a representative catalog of the dynamical model to rebuild the system dynamics. The outcome of this was the development of the Deep Data Assimilation (DeepDA) technique, which uses ensemble-based assimilation methods like the Ensemble Kalman Filter (EnKF) and Particle Filter (PF) along with DL to model system dynamics. To achieve this, an artificial recurrent neural network with a long short-term memory (LSTM) architecture was utilized for data-driven forecasting. To assess the effectiveness of DeepDA as compared to the AnDA model-driven assimilation methods, a series of numerical experiments were conducted using the chaotic dynamical model Lorenz-63. The results demonstrated that DeepDA exhibits highly efficient computational capabilities and satisfactory prediction accuracy and skills compared to AnDA.

Chromite, a crucial source of metallic chromium, plays a vital role in a nation’s industrial and economic development. The Sittampundi Layered Complex (SLC) in southern India, an Archean-layered igneous complex, hosts chromitite deposits interlayered with anorthosite, gabbro, and pyroxenite, making it geologically significant. This study addresses a gap in chromite exploration in the SLC, applying a combined analysis of ground gravity, magnetic, very low-frequency electromagnetic (VLF-EM), electrical resistivity tomography (ERT), and self-potential (SP) data along three profiles. Data were systematically collected, processed, and analyzed to delineate subsurface chromitite bodies. Residual gravity and magnetic anomalies, coupled with SP inverted model and VLF-EM current density pseudo-sections, successfully identified high-density, conductive zones corresponding to chromitite mineralization. ERT sections revealed low-resistivity anomalies, further corroborating the results revealed by other methods. The integrated analysis of these geophysical methods provided consistent horizontal extensions and depth estimates of chromitite deposits across all profiles, with the highest depth range of 1 m to 60 m and the most frequent depths around 15 to 16 m. SP inverted model indicates that chromitite bodies in the SLC exhibit horizontal cylindrical geometry with shallow depth. Anomaly pattern correlations across multiple methods confirm the presence of chromite-rich zones, including probable new concealed zones. Notably, 2D forward modeling of residual gravity suggests deeper extensions of chromitite between 100 and 200 m. Integrated analysis of five geophysical methods corroborating each other has significantly enhanced the accuracy of subsurface investigations for chromite exploration in the SLC and proven its efficacy.

Improving the imaging accuracy of geological formations under dual complex conditions (including complex surfaces and structures) is essential for precisely illustrating structural morphology and understanding reservoir characteristics. The multi-focus imaging is a real surface imaging method that takes into account signal-to-noise ratio (SNR) and resolution. Drawing upon the principles of paraxial ray theory and Hubra's two-wavefront theory, this approach employs a global optimization inversion algorithm to determine the radii and exit angles of the two wavefronts. Furthermore, it incorporates a non-hyperbolic travel time formula for accurate correction. By combining receiving channels from different CMP channels within the same Fresnel band radius, this method effectively enhances both the SNR and resolution of seismic data. The multi-focus imaging technique is a surface imaging method that considers both SNR and resolution. Drawing upon the principles of paraxial ray theory and Hubra's two-wavefront theory, this approach employs a global optimization inversion algorithm to determine the radii and exit angles of the two wavefronts. Furthermore, it incorporates a non-hyperbolic travel time formula for accurate correction. By combining receiving channels from different CMP channels within the same Fresnel band radius, this method effectively enhances both SNR and resolution of seismic data.

The impacts of climate change on hydroclimatic variables (HV) in the form of untimely rainfall or increasing temperature are well known and of great concern. This paper aims to analyse trend variations and identify the possible intrinsic nonlinear impact of HV on one another. For this purpose, trend variations are assessed for monthly temperature, precipitation, evapotranspiration, and river flow at the Chitral River at Chitral and the Indus River at Gilgit and Tarbela (river flow only), Pakistan, using the Mann–Kendall (MK), Sen’s slope, and nonparametric approaches: innovative trend analysis (ITA) and innovative polygon trend analysis (IPTA). The IPTA approach specifically examines the potential intrinsic nonlinear contribution of HV to the hydroclimatic cycle using statistical quantities average (AVG) and standard deviation (STD) in this paper. Moreover, MK analysis identified a trend in 20 out of 108 months, while ITA identified trends for the majority of the 95 months. ITA indicated impacts of temperature and precipitation on river flow during monsoon at Chitral and Gilgit, respectively, while their mixed impacts are observed post-monsoon at both stations. Overall, IPTA indicates uniformity in the behaviour of evapotranspiration with temperature at Chitral and Gilgit. Furthermore, STD polygons indicated possible impacts of temperature and precipitation in enhancing the river flow at the beginning of and during the monsoon at Gilgit, respectively. Additionally, IPTA plots of both AVG and STD reveal the strong seasonal pattern of actual river flow variation at all stations. These results will be beneficial for predicting irregular trends in HV for adapting climate change mitigation technology for urban, agriculture, and water resource planning sectors.

Graphical abstract

Basin-scale modeling plays a crucial role in informing policymakers on how to optimize water resource management. This study implements an integrated modeling framework combining MIKE NAM and MIKE HYDRO Basin to evaluate water demand, deficits, and drought risks in Ninh Thuan Province, Vietnam. The MIKE NAM model accurately simulated runoff during both calibration and validation, while MIKE HYDRO Basin effectively reproduced reservoir storage, with percent deviations ranging from −17% to nearly 29% in calibration and −6% to nearly 8% in validation. The validated models were then applied to assess drought conditions under two periods: a 2017 baseline (SCE1) and a 2030 projection incorporating climate change (CC) and sustainable development (SCE2). Results indicate a significant increase in water demand under SCE2, primarily driven by CC. Agriculture and livestock remained the dominant water users, with agriculture alone accounting for over 70% of total demand in both periods, reflecting growing stress on water resources. Drought risk assessment showed increased spatial extent and severity, with conditions ranging from abnormally dry to extreme, especially in the agriculture-dependent districts of Thuan Nam and Thuan Bac. The analysis also revealed that the current infrastructure under SCE1 is insufficient for sustainable water management. While infrastructure enhancements were introduced in SCE2, their effectiveness varied: drought impacts were reduced in Thuan Bac but worsened in Thuan Nam. These findings provide critical insights into future regional drought dynamics and highlight the urgent need for localized, adaptive strategies to address CC impacts and ensure long-term water security in Ninh Thuan.

The reflectivity (Z)-rain rate (R) relationship is crucial for describing the microphysical characteristics of precipitating clouds and plays a vital role in assessing the performance of polarimetric Doppler radar and rain gauge measurements. For the first-time, the power-law Z-R relationship ((Z{=aR}^{b})) is determined for stratiform and convective rainfall during the pre-monsoon, monsoon, and post-monsoon seasons at Mahabaleshwar, a tropical station in the Western Ghats, using the in-situ Joss-Waldvogel Disdrometer (JWD) measurements from 2014 to 2019 at the High-Altitude Cloud Physics Laboratory (HACPL: 17.56 ^oN, 73.4 ^oE; ~ 1400 m above MSL). The proportion of convective precipitation to the total precipitation during the pre-monsoon, monsoon, and post-monsoon seasons are ~ 42%, 53%, and 27%, respectively. The Z-R equation was derived using the linear regression method for different seasons and rain types. Pearson correlation coefficient between Z and R is high (r > 0.90) in all three seasons. The analysis shows that derived Z-R equations overestimate the value of Z for the rain events having R < 10 mm/hr and underestimate for R ≥ 10 mm/hr. Notably, the Z-R equation for the Western Ghats differs from those reported for mid-latitude and oceanic regions, reflecting the strong influence of regional topography, season and rain microphysics on precipitation characteristics. The coefficients “a” and “b” of the derived Z-R equation show substantial variation with season and rain type in comparison to the earlier studies at Gadanki and Tirupati due to differences in local atmospheric dynamics and complex orographic effects. The region-specific Z-R relationship may improve the radar-based rainfall estimations and also our understanding for rain microphysics over the Western Ghats.

Within the scope of this study, a range of advanced machine learning and deep learning models—including Singular Spectrum Analysis (SSA), Adaptive Neuro-Fuzzy Inference System (ANFIS), Categorical Boosting (CatBoost), Convolutional Neural Network (CNN), Deep Autoencoder, Deep Neural Network (DNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)—were employed to estimate the Standardized Groundwater Index (SGI) in Erzincan Province. SSA was utilized as a preprocessing technique to decompose input variables such as precipitation, relative humidity, temperature, and past SGI values into distinct components including trend, seasonality, cyclicality, and noise. These decomposed components were then fed into the artificial intelligence models to construct hybrid forecasting frameworks. The performance of each hybrid model was evaluated using multiple statistical indicators and visual analyses. The findings demonstrated that incorporating all SSA-derived subcomponents as inputs generally improved the monthly SGI prediction accuracy. However, for 12-month SGI predictions, the results were more variable, with both improvements and deteriorations observed depending on the model configuration. Additionally, the elimination of noise components was found to enhance both model generalization capability and overall prediction performance. Among the models tested, ANFIS emerged as the most effective in capturing GWD dynamics. To further investigate variable importance, Sobol sensitivity analysis was applied to the ANFIS outputs. The analysis revealed that previous SGI-1 values (t − 1) and relative humidity were the most influential inputs in predicting current SGI-1 (t) values.