pure and applied geophysics最新文献

{"title":"Reflectivity-Rain Rate Relationship for Orographic Rainfall at Mahabaleshwar Over the Indian Western Ghats","authors":"Amit Kumar,&nbsp;Atul Kumar Srivastava,&nbsp;Kaustav Chakravarty,&nbsp;Manoj Kumar Srivastava","doi":"10.1007/s00024-025-03761-8","DOIUrl":"10.1007/s00024-025-03761-8","url":null,"abstract":"<div><p>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 (<span>(Z{=aR}^{b})</span>) 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 &gt; 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 &lt; 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.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 7","pages":"3033 - 3045"},"PeriodicalIF":1.9,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Singular Spectrum Analysis for Noise Reduction and Feature Extraction in Hybrid Deep Learning Models: Integrating Meteorological Variables for Improved SGI Predictions","authors":"Erdal Koç,&nbsp;Okan Mert Katipoğlu","doi":"10.1007/s00024-025-03764-5","DOIUrl":"10.1007/s00024-025-03764-5","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 8","pages":"3219 - 3254"},"PeriodicalIF":1.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-025-03764-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying Machine Learning to Understand Rainfall–Runoff Interactions in the Tigris River Basin of Turkey","authors":"Oguz Simsek,&nbsp;Hatice Citakoglu,&nbsp;Veysel Gumus,&nbsp;Selmin Dere Çetin","doi":"10.1007/s00024-025-03749-4","DOIUrl":"10.1007/s00024-025-03749-4","url":null,"abstract":"<div><p>The modeling of rainfall (<i>P</i>_i) and runoff (<i>Q</i>_i) represents a significant challenge currently facing the field of hydrology. Numerous methodologies can be employed in this regard, spanning the spectrum from conceptual approaches to those that are entirely data-driven and physically based. This paper presents a method for estimating rainfall values at nine observation stations in the Tigris River Basin using four machine learning algorithms: the adaptive neuro-fuzzy inference system (ANFIS), the long short-term memory (LSTM) algorithm, the support vector machine (SVM) algorithm, and the Gaussian process regression (GPR) algorithm. The methodology is founded upon rainfall data obtained from seven meteorological observation stations within the basin. Thiessen polygons were employed to associate rainfall and runoff stations. In the study region, 11 models were constructed using the input parameters <i>P</i>_i, <i>P</i>_i−1, <i>P</i>_i−2, <i>P</i>_i−3, and <i>Q</i>_i−1 to ascertain the rainfall–runoff relationship. The efficacy of the estimation methods was evaluated using the mean absolute error (MAE), root mean square error (RMSE), determination coefficient (<i>R</i>²), Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency (KGE), and percent bias (PBIAS) criteria. The study’s findings indicated that the LSTM method demonstrated superior performance compared to the other models in all cases. In the LSTM method, the average MAE, RMSE, <i>R</i>², NSE, and PBIAS criteria for all models (from Model 1 to Model 11) were obtained as 7.14, 9.99, 0.97, 0.96, and 7.38 for training and 6.46, 9.06, 0.96, 0.91, and −2.59 for testing, respectively. The analysis of variance (ANOVA) test results indicated the efficacy of the methods, except for Models 9, 10, and 11, which employed the ANFIS method. Moreover, the exceptional predictive performance of the LSTM model is clearly illustrated in the graphical representation of the results, as demonstrated in violin plots and Taylor diagrams.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 8","pages":"3107 - 3138"},"PeriodicalIF":1.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-025-03749-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Occurrence of Local Meteotsunamis and the 2022 Tonga Tsunami along the Argentine and Uruguayan Coasts, Southwestern Atlantic Ocean","authors":"Walter Dragani,&nbsp;Iael Perez,&nbsp;Fernando Oreiro,&nbsp;Marcos Saucedo","doi":"10.1007/s00024-025-03760-9","DOIUrl":"10.1007/s00024-025-03760-9","url":null,"abstract":"<div><p>The Hunga Tonga–Hunga Ha’apai volcano erupted on January 15, 2022, generating atmospheric and oceanic wave responses that were studied along the Atlantic coasts of Argentina and Uruguay, as well as within the Río de la Plata estuary. Following the eruption, a warm front crossed the southeastern South American continental shelf, producing local atmospheric gravity waves (AGWs) that triggered meteotsunami activity. Remote atmospheric waves (Lamb waves) were also observed, arriving with timing consistent with theoretical predictions and displaying uniform waveforms and peak heights across all analyzed locations. However, no evidence suggests that these Lamb waves induced meteotsunamis in the study region. At Puerto Deseado and Bahía Blanca, sea-level disturbances coincided closely with the arrival of the oceanic tsunami generated by the eruption. In the outer Río de la Plata estuary, AGWs associated with the warm front exhibited predominantly southward propagation. This southward progression of meteotsunamis, driven by AGWs generated by the warm front’s passage, supports their local origin.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 7","pages":"2703 - 2722"},"PeriodicalIF":1.9,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climatological Analysis in Identification of Pre-monsoon Convective Potential Zones over Eastern and North-Eastern States of India During the Last Four Decades","authors":"Anup Mahato,&nbsp;A. N. V. Satyanarayana","doi":"10.1007/s00024-025-03743-w","DOIUrl":"10.1007/s00024-025-03743-w","url":null,"abstract":"<div><p>This study investigates the climatological variability and trends of thermodynamic parameters in four eastern and north-eastern Indian states Odisha, Bihar, Jharkhand, and Assam, during the pre-monsoon season (March–May) from 1980 to 2020 using high-horizontal resolution (0.25° × 0.25°) European Centre for Medium-Range Weathe (ECMWF) reanalysis (ERA-5) and hourly time interval data. For this purpose, the Mann–Kendall test has been implemented to analyse the spatial patterns of inter-annual variability and trends in convective available potential energy (CAPE), convective inhibition (CIN), CAPE/CIN ratio, and <i>K</i>-index to identify convective potential zones for the occurrence of thunderstorms. The climatological trend analysis of parameters reveals significant spatial variation in convective potential over the study region. The southeastern area of Odisha showed high atmospheric instability, with low CIN, higher CAPE ranging from 2000 to 3500 J kg⁻¹, and high values of CAPE/CIN ratios associated with the minimal spatial pattern of interannual variability, indicating a higher convective potential zone for the occurrence of thunderstorms. The analysis reveals that eastern Bihar and southeastern Jharkhand are noted to be convective potential zones. Results have shown that southwestern Assam is a significant convective, potentially active zone during the last two decades. Significant increases in dew point temperature, which represent greater availability of moisture and air temperature trends, were observed over these regions, aligning with the trends of the convective indices, signifying the convective potential of these regions. In alignment with the climatological analysis, higher occurrences of thunderstorms over southwestern Assam since 2000 are based on India Meteorological Department (IMD) reports.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 7","pages":"3005 - 3032"},"PeriodicalIF":1.9,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Gutenberg–Richter Relation May Not Hold for the Anthropogenic Seismicity","authors":"Anastasios Kostoglou,&nbsp;Beata Orlecka-Sikora,&nbsp;Stanislaw Lasocki,&nbsp;Francis Tong","doi":"10.1007/s00024-025-03754-7","DOIUrl":"10.1007/s00024-025-03754-7","url":null,"abstract":"<div><p>The empirical Gutenberg–Richter (GR) relation corresponds to an exponential model of magnitude distribution, the most widely used in the probabilistic assessments of seismic hazard and related risk. However, due to the complexity of seismic processes induced by technological activities, this model may not be applicable to anthropogenic seismicity (AS). Applying statistical hypotheses testing procedures, we investigate 63 AS catalogs resulting from various anthropogenic activities such as reservoir impoundment, underground mining, conventional and unconventional hydrocarbon extraction, geothermal energy production, and underground gas storage. In 30 cases (47.6%) the exponential model for magnitude is rejected. Furthermore, in 16 out of these cases, the magnitude probability density functions are complex, having either modes or bumps or both. We discuss possible reasons for the discovered statistically significant deviations of the actual magnitude distributions from the exponential distribution and hence from the GR relation. We demonstrate that using the exponential distribution may lead to unacceptable inaccuracy of seismic hazard estimates in AS. As a remedy, we recommend the use of kernel nonparametric estimators of magnitude distribution.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 8","pages":"3067 - 3089"},"PeriodicalIF":1.9,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-025-03754-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-based Statistical Prediction of Cyclonic Disturbance Frequency during Post-monsoon over the Bay of Bengal","authors":"Javed Akhter,&nbsp;Aditi Bhattacharyya,&nbsp;Subrata Kumar Midya","doi":"10.1007/s00024-025-03740-z","DOIUrl":"10.1007/s00024-025-03740-z","url":null,"abstract":"<div><p>Deadly tropical cyclones (TCs) form quickly over the Bay of Bengal (BoB) during the post-monsoon season (October to December; OND) and cause significant socio-economic damage across India and the neighbouring countries. For better planning to reduce the risks associated with cyclonic activities, seasonal forecasting in advance would be beneficial. The current study has assessed the influences of large-scale dynamic and thermodynamic parameters of the preceding seasons, i.e., June to September, on the post-monsoon cyclonic disturbance (CD) frequency over BoB to develop statistical models for seasonal prediction. Six parameters, including sea surface temperature, sea level pressure, relative humidity at 500 hPa, zonal wind at 200 hPa and 850 hPa, and meridional wind at 850 hPa levels, with significant correlations with the formation of CDs over BoB from 1982–2020 (39 years), were selected as potential predictors. By utilizing the selected predictors, four machine learning (ML) models, namely Principal Component Regression (PCR), Support Vector Regression (SVR), Random Forest (RFR) and Artificial Neural Network (ANN), were built to forecast the frequency of CDs. The RFR model showed relatively better skills in both quantitative and categorical forecasts among the four models. Hence, it can be utilized more reliably for seasonal CD frequency over BoB.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 7","pages":"2983 - 3003"},"PeriodicalIF":1.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inversion of Rayleigh Wave Dispersion Curves via BP Neural Network and PSO","authors":"Yijian Luo","doi":"10.1007/s00024-025-03752-9","DOIUrl":"10.1007/s00024-025-03752-9","url":null,"abstract":"<div><p>Rayleigh wave analysis serves as a critical tool for subsurface characterization in geotechnical engineering and geophysical exploration, while reconstructing stratigraphic velocity profiles from dispersion curves remains challenging due to inherent nonlinearity and solution multiplicity. This study proposes a hybrid inversion framework integrating a backpropagation (BP) neural network with particle swarm optimization (PSO). A statistically representative training database encompassing realistic stratigraphic configurations is systematically established through random perturbation of shear-wave velocity profiles. Then, a BP neural network is employed to establish the nonlinear correspondence between dispersion curves and stratum-specific shear-wave velocity profiles. The trained BP neural network demonstrates computational efficacy in generating geophysically plausible velocity estimates, albeit with limited spatial resolution. These network-derived models serve as physics-informed initial inputs for the subsequent PSO inversion framework, forming a dual-phase inversion framework. This synergistic methodology specifically targets two persistent challenges in geophysical parameter estimation: (i) the non-iterative nature of standard BP architectures that restricts progressive model improvement, and (ii) the suboptimal search efficiency of standalone PSO implementations when initialized without physically meaningful constraints. Benchmark synthetic experiments confirm the enhanced robustness of the dual-phase inversion framework, exhibiting a significant reduction in mean relative error compared to BP neural network and PSO under controlled noise conditions. Furthermore, field implementation at the Baotou–Yinchuan railway site successfully identified weak interlayers, as confirmed by the borehole data.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 7","pages":"2871 - 2893"},"PeriodicalIF":1.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Study of Waves Generated by Landslides in U-Shaped Bays","authors":"Rani Sulvianuri,&nbsp;Sri Redjeki Pudjaprasetya","doi":"10.1007/s00024-025-03744-9","DOIUrl":"10.1007/s00024-025-03744-9","url":null,"abstract":"<div><p>The modeling of waves caused by landslides is an active area of intensive research, with studies including landslides on flat-bottom or sloping beaches, using analytical, numerical, or experimental approaches. This article focuses on the study of landslide waves generated in long and narrow bays, on which the Saint-Venant equations hold. In this article, our previously developed numerical scheme is revisited and modified to accommodate landslide movements. The ability of the scheme to simulate the wave generated by landslide in U-shaped bays is validated using analytical solutions. Further, we investigate the shoaling processes experienced by waves in the U-shaped bay, as well as resonance phenomena, obtaining results that align perfectly with analytical predictions. We also analyze the nonlinear effects revealed by numerical simulations. Finally, we apply our model to a more realistic scenario, providing estimates of wave run-up in Palu Bay.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 7","pages":"2855 - 2870"},"PeriodicalIF":1.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dense Fog Episode Over Kolkata Airport During 23/01/2025 to 24/01/2025: The Synoptic Influence","authors":"Pravat Rabi Naskar,&nbsp;Gyan Prakash Singh","doi":"10.1007/s00024-025-03758-3","DOIUrl":"10.1007/s00024-025-03758-3","url":null,"abstract":"<div><p>In this study, we have analyzed a dense fog episode over Kolkata airport that occurred during 23/01/2025 to 24/01/2025. We have utilized half-hourly meteorological data from the NSCBIA METAR, radio sounding data from the University of Wyoming, as well as upper air wind, specific humidity, mean sea level pressure, 10-m wind, and vertical integral of moisture flux divergence data from ERA5. Our findings indicate that the dense fog during these dates was strongly influenced by the prevailing anti-cyclonic circulation, which facilitated essential moisture advection from the Bay of Bengal to Gangetic West Bengal. Additionally, we have observed differences in the characteristics of the dense fog on the two dates based on the boundary layer characteristics and meteorological setup. The fog on January 23/01/2025, 00 UTC, has been classified as radiation + advection fog, meeting the necessary criteria, while the fog on 24/01/2025, 00 UTC, has been identified as cloud base lowering fog.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 8","pages":"3047 - 3066"},"PeriodicalIF":1.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}