pure and applied geophysics最新文献

The Piton de la Fournaise volcano (PdlF) on the island of La Réunion is one of the most active and best monitored volcanoes in the world. Its frequent eruptions make it a natural laboratory for developing new methods and evaluating their performance over multiple eruption sequences. In this work, we present a Deep Learning (DL) model for volcanic earthquake detection and two models for classification based on DL and Machine Learning (ML) algorithms. The detection model is based on encoder–decoder layers that extract high-order features in the time domain that are hidden in the seismograms. The first classification model consists of a simple convolutional neural network that uses the short-time Fourier transform of the signals as input data. A second classifier is based on ML approach and uses hand-crafted features. We show that our detection model, trained on ~ 7 000 volcano-seismic events recorded at PdlF between 2014 and 2021, outperforms previous DL-based models in detecting volcano-seismic events, achieving an accuracy of 98.15% on the testing dataset. Seven classes of signals are considered for classification models: volcano-tectonic (VT) events, rockfall, long-period events, volcanic tremors, tectonic events, anthropogenic noise and environmental noise. Both tested classification models achieve an accuracy of 96.55% in the testing dataset. By applying these models to the continuous data recorded at PdlF in 2019, we are able to detect and classify 1.5 times more VT events than the catalog provided by the Observatory. The detection model takes 28 s to process 24 h seismograms and from a few to a maximum of 70 s for classification.

The summer monsoon is a major weather phenomenon and has a significant socio-economic influence on India. In this context, understanding the long-term trend and the dynamics of extreme rainfall events (EREs), especially over the arid and semiarid regions like northwest India, remains limited. In this study, we delve into not only understanding the long-term trend and how they evolve in a warming climate but also elucidate the complex driving mechanisms of these events over the arid and semiarid regions of northwest India. Using high-resolution long-term rainfall datasets, EREs are classified into three major categories based on their spatial extent: widespread extreme rainfall events (WEREs), localized extreme rainfall events (LEREs), and EREs (comprising both WEREs and LEREs). Trend analysis spanning over a century indicates a significant upward trend in the frequency of WEREs. Additionally, the contribution of EREs to seasonal rainfall shows a notable increase, especially over western Gujarat. The composite analysis reveals distinct patterns in WEREs and LEREs, showcasing the evolution of rainfall and anomalous low pressure over the region. Results suggest strong vertical velocity and middle-level tropospheric heating are the key drivers of these EREs. In addition, high values of potential vorticity and moist static energy in the mid-level are key features of these systems, with snow hydrometeor being the major contributor to rainfall. This study's findings underscore the growing concern about EREs affecting the arid and semiarid region, which is already vulnerable due to their limited vegetation and scarce water resources.

In the geotechnical field, sediment layer thickness determination is critically important, as it provides invaluable information for structural and infrastructure planning and design. The Bakauheni area, characterized by numerous calderas and ancient volcanic deposits from the Pliocene to Holocene epochs, presents a compelling case for studying sediment layer thickness. Using 64 Horizontal-to-Vertical Spectral Ratio (HVSR) measurement points, we investigated sediment thickness distribution and its correlation with ancient calderas in Bakauheni. A 1D shear wave velocity (Vs) model was derived through inversion using the Particle Swarm Optimization (PSO) algorithm, revealing an average Vs of ~ 600 m/s across the study area. This relatively high Vs value suggests dense, compacted sediments or weathered bedrock. The average HVSR curve yielded a natural frequency (f₀) of 15.12 Hz, corresponding to an estimated sediment thickness of 9.92 m (assuming Vs = 600 m/s). This aligns closely with the median thickness of 10.55 m calculated from all 64 measurement points. Observed sediment thicknesses ranged from 4.39 to 103.57 m, with a mean of 18.22 m, indicating a general thickness range of 10–18 m in Bakauheni. The thickest deposits (> 30 m) correlate with caldera locations and low-topography zones, implying substantial sediment accumulation over ancient calderas. While the HVSR method effectively estimates sediment thickness for caldera identification, local Vs variations due to sediment composition necessitate further research to fully characterize the subsurface properties.

Vertical Electrical Sounding (VES) data inversion is a complex non-linear problem requiring robust global optimization methods. While techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are widely used, the Flower Pollination Algorithm (FPA) offers distinct advantages: (1) faster convergence due to its Lévy flight mechanism, (2) handling the overfitting problem, and (3) superior balance between exploration and exploitation. In this study, we enhance FPA with adaptive switch probability to further optimize its performance for VES inversion. The adaptive switch probability in FPA dynamically adjusts the balance between global exploration (early iterations) and local exploitation (late iterations), improving convergence speed and accuracy compared to fixed-probability FPA. This is achieved by gradually reducing the switch probability p from = 0.8 to = 0.2 over iterations, optimizing the search process for VES inversion. Tests were conducted using synthetic VES data with 3-layer (Q-type and H-type) and 4-layer. The adaptive FPA demonstrated superior performance to conventional FPA, reducing misfit errors by 30–50% across synthetic models (e.g., 0.0011% vs. 0.0044% for H-type) while maintaining 40% faster convergence rates. Improvement was quantified using three key criteria: (1) final misfit error, (2) iteration count to convergence, and (3) parameter uncertainty, with the adaptive version consistently outperforming in all metrics. Adaptive switch FPA was also applied to VES field data for groundwater exploration in Leihitu village, Central Maluku, Indonesia. The adaptive FPA's subsurface reconstructions were validated through direct comparison with IP2WIN commercial software, showing superior resolution in identifying layer boundaries (≤ 5% deviation in thickness estimates) and resistivity values (≤ 8% relative error). In Leihitu village, the freshwater aquifer (18–22 Ωm) at 22–33.8 m depth sandwiched between impermeable limestone layers (> 300 Ωm) and the aquifer is sandstone which has good porosity. In the future, the FPA adaptive switch can be tested to solve complex non-linear geophysical inversion problems such as Controlled Source Audio-frequency Magnetotellurics (CSAMT), Magnetotelluric (MT), Transient Electromagnetic (TEM), and others.

Drought is not only a problem that challenges scientists but also one of the most difficult natural disasters to combat for local governments and decision-makers. Like many parts of the world suffering from drought, the western Mediterranean region of Turkey is also affected by drought. In this study, innovative drought prediction models were created with different machine learning algorithms and deep learning methods to create a model that will help decision-makers regarding drought. 4 different monthly lagged model structures were established using SPI12 values calculated with precipitation data between 1967 and 2020 for the Acipayam, Bodrum and Fethiye regions located in the west of Turkey. While providing data, attention was paid to the distance between stations and data continuity. The models were analyzed with Long-Short Term Memory (LSTM), Artificial Neural Network (ANN) and Random Forest (RF) algorithms. In addition, Discrete Wavelet Transform (WT) was used to obtain better model results. The hyper-parameters of these algorithms were determined by taking into account the most commonly used parameters in the literature. The analysis results were evaluated by correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe Efficiency (NSE), and Combined Accuracy (CA). As a result of the comparison of these methods, the best results were obtained in the M01 model of LSTM, LSTM-WM01, for Acipayam after WT (r: 0.9910, NSE: 0.9733, RMSE: 0.1637, and CA: 0.1030). While the best prediction for Bodrum was obtained in LSTM-WM02 after WT (r:0.9657, NSE:0.9325, RMSE:0.3101, and CA: 0.1929), for Fethiye it was obtained in LSTM-WM02 having performance metrics r:0.9539, NSE:0.8973, RMSE:0.3689, and CA: 0.2359. It is expected that the results obtained with this study will help researchers and decision-making authorities on drought.

This study classifies synoptic-scale atmospheric patterns associated with hailstorms in the Democratic People’s Republic of Korea (DPRK) using observational hail data (2010–2018) and multivariate statistical methods. Principal Component Analysis and k-means clustering identified four distinct regimes: (1) Summer Monsoonal Ridge-Trough (60.6% of hail days), characterized by a weak 500-hPa trough over northern DPR Korea, warm advection at 850 hPa, and high convective instability (CAPE > 1460 J kg⁻¹, lapse rate 26 K); (2) Autumn Cold Frontal (7.7%), driven by Siberian cold-air incursions and moderate CAPE (~ 1160 J kg⁻¹); (3) Spring Closed Low (16.6%), marked by a cyclonic low over northeast China and orographically enhanced uplift in northern highlands; and (4) Spring–Autumn Shortwave Trough (15.1%), featuring mobile mid-level troughs and frontal convergence. Discriminant Analysis validated the classification (93.4% accuracy), with key predictors including 500-hPa geopotential height and 850-hPa temperature loadings. Topography critically modulated hail genesis, amplifying updrafts in mountainous regions despite weaker synoptic forcing. These findings provide actionable insights for forecasting, emphasizing CAPE thresholds (> 1500 J kg⁻¹) and ridge-trough interactions as precursors to severe hail. The study establishes a transferable framework for hail prediction in topographically complex regions, addressing a critical gap in East Asian climatological research.

The Uttarakhand state of the Indian Himalayan region is primarily occupied  by needle-leaf and broad-leaf forests Understanding the behavior of a land surface model (LSM) to different vegetation types and forcings is crucial to configuring LSM for this particular region, which is the aim of the study. Various reanalysis products (ERA5-Land, IMDAA, GLDAS, MERRA2, and NEP FNL) are validated against in situ observations at three stations (Kosi-Katarmal, Kantli, and Gangolihat stations) to find the best forcing, and ERA5-Land performed the best. Therefore, one-dimensional Noah-multi parameterization (Noah-MP) LSM is forced with ERA5-Land analysis and IMERG rainfall during 2011–2021. Three vegetation types (Deciduous needle-leaf forest, Evergreen broad-leaf forest, and Barren/Sparsely vegetated type) along with no-vegetation type are considered in LSM and referred to as EXP2, EXP3, EXP4, and EXP1, respectively. Note that the vegetation types in EXP2 and EXP3 are closely related to the actual vegetation observed at in- situ stations, while EXP1 and EXP4 provide sensitivity of land surface conditions to the tree density. The diurnal variation of soil temperature (ST) from EXP2 and EXP3 reasonably agrees with in-situ observations and is better than global/regional analyses, unlike EXP1 and EXP4. EXP2 and EXP3 are comparable for surface sensible and latent heat fluxes in pre-monsoon and southwest-monsoon seasons and could be due to matching with vegetation type and density. The Noah-MP soil moisture (SM) is overestimated (~ 0.09 to 0.15 m³ m⁻³) against observation, comparable with ESACCI and CYGNSS (− 0.065 to 0.03 m³ m⁻³) on daily and monthly scales. The SM variations are marginal among the seasons, unlike the ST and surface fluxes. The Noah-MP simulated evapotranspiration is comparable to in-situ observation in EXP2 and EXP3. The study demonstrates the value of LSM in simulating land-surface processes when driven by correct vegetation type, density, and best forcing.

The Late Cretaceous Naparima Hill Formation argillites are the primary source for most of the oil and gas production within the last century in Trinidad. Hydrocarbon production has declined in recent years, necessitating the exploration of new petroleum plays and the use of improved techniques to increase production. The Naparima Hill Formation is also considered as an unconventional reservoir due to its low permeability and high hydrocarbon content. Hydraulic fracturing, which depends on mechanical properties such as brittleness and fracturability, is necessary for the exploitation of this formation. In this study, we measured the unconfined compressive and tensile strengths of four organic-rich outcrop lithofacies (siliceous-calcareous argillite, calcareous argillite, carbonate rich siliceous argillite, and siliceous argillite), under dry and water-saturated conditions. The strength measurements were then used to determine the strength-based brittleness index and fracture toughness. A fracturability evaluation model that integrates brittleness index, fracture toughness, and minimum horizontal insitu stress was used to evaluate the fracturability. Our results showed that saturation reduced the uniaxial compressive and tensile strengths up to 51% and 59%, respectively. The siliceous calcareous argillite and siliceous argillite experienced the highest reductions in strength due to their relatively high porosities (21% and 31%, respectively) and lack of carbonate cement. The brittleness index and fracturability ranged from 60 to 75% and 0.17 to 0.43 MPa^-2.m^0.5, respectively, under dry conditions, and increased to 69 to 78% and 0.26 to 0.55 MPa^-2.m^0.5, respectively, when saturated. Our results imply that all the argillites are brittle, and only the siliceous calcareous argillite and siliceous argillite are easily fractured when water saturated for hydraulic fracturing operations. Thus, the siliceous calcareous argillite and siliceous argillite are more susceptible to tensile fracture initiation and propagation during hydraulic fracturing.

The Transient electromagnetic method is widely used in coal mine goaf exploration. However, data inversion is a complex problem requiring nonlinear solution equations, and the electromagnetic response of double-layer goafs is complex, leading to low accuracy in identifying their types and ranges. A three-dimensional model conforming to the geological characteristics of the study area is constructed, and the transient electromagnetic induction electromotive force data at each measurement point are obtained via numerical simulations. Based on the smoke ring inversion and depth correction of the data, apparent resistivity and its gradient along the apparent depth, as well as the logarithm of induced electromotive force and its gradient along the time plane, are selected as the sample parameters, which are sensitive to the electrical reflection and abnormal characterization of goafs. Principal component analysis of the multilayer depth parameters yields the standard values of each parameter at the depth of the target layer. Training samples for different goaf types are established, and the backpropagation (BP) neural network algorithm is used to train the samples to construct a neural network model of each goaf type. The training samples are back-estimated, and the type of goaf in the entire area is predicted. Results show that the back-estimation prediction accuracy of the No.2 coal seam samples is 84.7%, and the prediction accuracy of the entire area is 84.3%. Meanwhile, the back-estimation prediction accuracy of the No.3 coal seam samples is 94%, and the prediction accuracy of the entire region is 87.87%. The proposed method is used to identify the type of double-layer goaf in coal mines, and the distribution characteristics of the goaf type in the entire area are rapidly obtained. The back-estimation prediction accuracies of the No. 2 and 3 coal seam samples are 91.1% and 93.3%, respectively. Discrimination results are confirmed via drilling. Findings show that the proposed BP neural network discriminant model based on the transient electromagnetic multiparameters realizes the rapid and accurate identification of double-layer goaf types in coal mines and avoids multiple solutions and low recognition caused by the inversion of the transient electromagnetic data.

Thénia fault is well characterized in its eastern part whereas in its western part, between Cap-Matifou and Boumerdes, the fault trace, although invisible, is marked by a Plio-quaternary scarp with a direction N110°. Questions persist regarding its existence, its continuity and its exact position in its western segment which remains poorly documented. In this work, we study the spectral ratios (HVSR) and horizontal polarization of ground motion using ambient vibration noise recordings made in the western part of the Thénia fault, with the objective of asserting or not its continuity. Clustering of HVSR curves based on their trends, their frequency peaks and their amplitudes highlighted three distinct zones: both the plateau area north of the scarp and the lower part of the plain to the south are characterized by high frequency curves while the scarp area is characterized by a mix of low and high frequencies. Moreover, polarization analysis of the ambient vibration noise recordings, carried out by the covariance matrix method, indicates well-polarized signals on the scarp and weak ones on the northern and southern parts. This analysis also made it possible to highlight a mean direction of predominant polarization between 10° and 30°, whose transverse projection coincides perfectly with the direction of the scarp which is N110°. This polarization direction is observed for low and high frequencies which represent the same soil response for different depths. All of these results support the hypothesis of the extension of the Thénia strike-slip fault in its western part, which until now only showed a banal escarpment. The results obtained will make it possible to refine the mapping of the Thénia fault zone at the seismic microzoning scale and to have a better assessment of the regional seismic hazard and good management of the seismic risk, particularly in urbanized areas.