Acta Geophysica最新文献

{"title":"Deep sequence models for predicting average shear wave velocity from strong motion records","authors":"Baris Yilmaz,&nbsp;Erdem Akagündüz,&nbsp;Salih Tileylioglu","doi":"10.1007/s11600-026-01885-6","DOIUrl":"10.1007/s11600-026-01885-6","url":null,"abstract":"<div><p>This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface (<span>(V_textrm{s30})</span>) at strong motion recording stations in Türkiye. <span>(V_textrm{s30})</span> is a key parameter in site characterization and, as a result for seismic hazard assessment. However, it is often unavailable due to the lack of direct measurements and is therefore estimated using empirical correlations. Such correlations however are commonly inadequate in capturing complex, site-specific variability and this motivates the need for data-driven approaches. In this study, we employ a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to capture both spatial and temporal dependencies in strong motion records. Furthermore, we explore how using different parts of the signal influence our deep learning model. Our results suggest that the hybrid approach effectively learns complex, nonlinear relationships within seismic signals. We observed that an improved P-wave arrival time model increased the prediction accuracy of <span>(V_textrm{s30})</span>. We believe the study provides valuable insights into improving <span>(V_textrm{s30})</span> predictions using a CNN-LSTM framework, demonstrating its potential for improving site characterization for seismic studies. Our codes are available via this https://github.com/brsylmz23/CNNLSTM_DeepEQ GitHub repository.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829873","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":"A hybrid signal decomposition-machine learning benchmarking framework for multi-station precipitation prediction in the Kébir Rhumel basin (Algeria)","authors":"Aykut Erol,&nbsp;Issam Rehamnia,&nbsp;Hatice Citakoglu","doi":"10.1007/s11600-026-01888-3","DOIUrl":"10.1007/s11600-026-01888-3","url":null,"abstract":"<div><p>Reliable multi-station precipitation forecasting is challenging due to nonstationarity, noise, and spatial heterogeneity. This paper introduces a hybrid signal decomposition-machine learning benchmarking framework that integrates four decomposition methods (TQWT, MODWT, EWT, VMD) with three learners (Bagging, LSBoost, KNN), yielding twelve hybrid models. These models were rigorously tested across twelve stations in the Kébir Rhumel Basin using eight statistical metrics and distributional diagnostics to assess accuracy, stability, and generalization. Two dominant families emerged: TQWT-based hybrids achieved localized accuracy at four stations, while MODWT-Bagging led at eight stations and delivered the most consistent cross-station performance. MODWT-Bagging achieved <i>R</i>² = 0.984–0.993 and NSE = 0.981–0.993, with RMSE ranging from 2.64 to 6.34, demonstrating strong predictive skill under varying hydro-climatic conditions. In noise-rich environments, it substantially reduced errors; for example, at El Milia, RMSE dropped from 12.57 (VMD-LSBoost) to 6.03, a ≈ 52% reduction, and improvements of up to 63% were observed at other stations. Its superiority stems from MODWT’s shift-invariance and noise robustness combined with Bagging’s variance reduction. Taylor diagrams and violin plots confirmed centered, compact error structures, while scatter plots verified accurate phase and magnitude tracking. By clarifying how decomposition structure and learner characteristics interact across heterogeneous regimes, this framework fills a key gap in signal decomposition-machine learning model selection. The findings support adaptive hybrid design for early warning, water resource management, and precipitation-driven forecasting systems. Overall, MODWT-Bagging is established as a robust default for complex precipitation modeling, and the proposed framework provides a scalable foundation for next-generation hybrid predictive tools.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01888-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147830007","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":"Prediction of groundwater equivalent thickness using the integration of the improved RSA–ORELM model and GRACE satellite images","authors":"Mahtab Badkoubeh Hezaveh,&nbsp;Mohsen Najarchi,&nbsp;Mohammad Reza Jalali,&nbsp;Hosein Mazaheri,&nbsp;Saeid Shabanlou","doi":"10.1007/s11600-025-01778-0","DOIUrl":"10.1007/s11600-025-01778-0","url":null,"abstract":"<div><p>In this study, a novel hybrid modeling framework combining the reptile search algorithm (RSA) with the outlier robust extreme learning machine (ORELM) was developed to enhance the prediction accuracy of groundwater equivalent thickness derived from the Gravity Recovery and Climate Experiment (GRACE) satellite observations. The investigation focuses on the Lake Urmia Basin, located in northwestern Iran. A total of 163 monthly GRACE datasets spanning the period from April 2002 to June 2017 were employed. To isolate the groundwater signal, hydrological components simulated by the GLDAS model were subtracted from the GRACE-derived total water storage variations. The output of the satellites includes 6 points located in the chosen basin in which the results show the decreasing trend of the groundwater equivalent thickness with the changing domain of − 50 to + 50 for the certain mentioned points within the basin. The satellite output results obtained from the six study points are compared with the data of piezometric wells existing in each point zone. The comparison of the output of the GRACE satellites with the observed data displays that the correlation coefficient value in six points is 0.57 on average. In addition, the values of <i>RMSE</i>, <i>MARE</i>, and <i>RMSRE</i> are on average 8.8, 1.4, and 3.1, respectively, showing the appropriate performance of the GRACE satellites in approximating the groundwater equivalent thickness in the study region. After that, the time series obtained by the GRACE satellites for the six points are modeled by the RSA–ORELM model. Regarding the same trend in all points, the data are modeled simultaneously for them. The obtained results prove the suitable performance of the RSA–ORELM model so that the correlation coefficient in the training and testing stages is 0.97 and 0.87, respectively. The <i>RMSE</i> values in both stages are very close to each other. Also, the <i>RMSRE</i> value in the testing stage has a better performance than the training stage, showing the high efficiency of the model in simulating the GRACE data.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829146","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":"High-resolution crustal thickness and VP/VS ratio in Shanghai and its neighboring areas with its tectonic implications","authors":"Peng Wang,&nbsp;Shanshan Wu,&nbsp;Xiuqing Song,&nbsp;Suxiang Zhang","doi":"10.1007/s11600-026-01875-8","DOIUrl":"10.1007/s11600-026-01875-8","url":null,"abstract":"<div><p>We present a high-resolution crustal model for Shanghai and its neighboring areas, derived from H-κ stacking of receiver functions at 114 seismic stations. Our model corroborates the major tectonic subdivisions of the area by revealing significant lateral variations in crustal structure. The average Moho depth is 31.4 km. A distinct “thick-outside, thin-inside” structure is observed, with a deeper Moho beneath the Jiangsu-Zhejiang Uplift Zone (average 34.2 km) and a shallower one in the Yangtze River Delta Depression Zone (average 30.4 km). A notable fault-bounded crustal thickening anomaly within the Depression Zone further indicates complex deformation. The V_P/V_S ratio exhibits large variation (1.6–2.1), featuring a prominent Y-shaped high V_P/V_S corridor (average 1.98) interpreted as mantle-derived mafic modification of the lower crust, and low V_P/V_S zones attributed to granites and Cenozoic fluid--rock interactions. The consistency of high V_P/V_S and crustal thickening suggests a process of magmatic addition and subsequent cooling. Collectively, these complex structures are the product of multi-stage tectonic events, including the Paleozoic Yangtze--Cathaysia collision and subsequent Mesozoic to Cenozoic extension and magmatism linked to the Paleo--Pacific Plate subduction and rollback. These findings provide crucial insights into the regional tectonic evolution and have important implications for seismic hazard assessment.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829143","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":"Vegetation flow interaction and suspended sediment transport in a moderately vegetated channel: a modeling approach","authors":"Tanmoy Majumder,&nbsp;Sushant K. Biswal,&nbsp;Nabina Khanam","doi":"10.1007/s11600-026-01879-4","DOIUrl":"10.1007/s11600-026-01879-4","url":null,"abstract":"<div><p>A numerical investigation was conducted to analyze flow attributes and suspended sediment, within and around both emergent and submerged vegetation, employing a two-dimensional (2D) model. In this model, vegetation was represented as an absorbent region to consider the drag and flow resistance induced by plant structures. The simulation model was validated against experimental data obtained by Lu (Experimental study on suspended sediment distribution in flow with rigid vegetation, 2008), confirming its reliability and precision. The research examines key flow attributes and spatial distribution patterns of suspended sediments within sediment-laden vegetated flows, considering a range of hydraulic conditions. By comparing emergent and submerged vegetation scenarios, the study provides insights into how vegetation and flow regime influence sediment suspension, transport mechanisms, and associated flow structures. Furthermore, detailed analyses were performed to explore the correlations among key variables influencing suspended sediment dispersal, covering flow velocity, vorticity, and turbulent kinetic energy (TKE) fields. It was observed that the TKE field beyond the canopy demonstrated a positive relationship with sediment concentration, while near the bed; TKE field was almost associated with the spatial distribution of suspended grains. In the present study, under conditions of low flow blockage, sediment deposition in the wake zone extended over a considerable distance downstream. However, when the flow blockage was sufficiently large to generate a vortex street, improved sediment deposition was concentrated in two distinct areas: nearly downstream of the vegetation canopy and in the recirculation area. The spanwise distribution of particles is characterized using a probability density function (PDF), which reveals temporal irregularity in the particle field, quantified by the variance of the PDF.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829393","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":"Quantification of uncertainties in the hydrological models for streamflow prediction in a humid tropical river basins in India: a case study of the Chaliyar river basin, Kerala","authors":"K. Naveena,&nbsp;P. V. Shahanas,&nbsp;T. M. Sharannya,&nbsp;Baheerah Shada,&nbsp;T. K. Drissia,&nbsp;Chinni V. Naga Kumar Kommireddi,&nbsp;U. Surendran,&nbsp;N. K. Rajesh Kumar","doi":"10.1007/s11600-026-01869-6","DOIUrl":"10.1007/s11600-026-01869-6","url":null,"abstract":"<div><p>Reliable streamflow prediction in humid tropical basins is challenging due to multiple sources of uncertainty. This study aims to develop an integrated framework to simultaneously quantify and evaluate the effects of input, parameter, terrain, and structural uncertainties. Affecting daily streamflow prediction in the Chaliyar River Basin, a representative humid tropical river system in Kerala, India. Daily streamflow records (1988–2005) were used for model calibration (1988–2000) and validation (2001–2005). Precipitation gaps were addressed using Kalman smoothing with ARIMA. Parameter uncertainty was assessed through three algorithms: Sequential Uncertainty Fitting (SUFI-2), Particle Swarm Optimization (PSO), and Generalized Likelihood Uncertainty Estimation (GLUE). Terrain uncertainty was evaluated using SRTM (30 m), ASTER (30 m), and CARTOSAT (10 m) Digital Elevation Models (DEMs). Structural sensitivity was tested using 300, 500, and 1000 calibration iterations. Streamflow predictions were evaluated with the coefficient of determination (R²) and Nash–Sutcliffe efficiency (NSE).The base SWAT model showed moderate performance for daily streamflow prediction (calibration: <i>R</i>² = 0.58, NSE = 0.52; validation: <i>R</i>² = 0.51, NSE = 0.42). Integrating input uncertainty reducing techniques (Kalman–ARIMA) and parameter optimization improved predictions: GLUE (calibration/validation <i>R</i>²/NSE: 0.81/0.78, 0.68/0.66), PSO (0.80/0.79, 0.70/0.70), and SUFI-2 (0.82/0.81, 0.73/0.72). Compared to the base model, SUFI-2 achieved significant improvements of 56% in calibration NSE (from 0.52 to 0.81) and 71% in validation NSE (from 0.42 to 0.72) for daily streamflow predictions. Cartosat DEM (10 m) outperformed SRTM and ASTER (30 m), with base model NSE values of 0.67/0.58 versus 0.52/0.42 and 0.50/0.40, respectively. The optimal configurationSUFI-2 with CARTOSAT and Kalman–ARIMA achieved calibration <i>R</i>² = 0.82, NSE = 0.81 and validation <i>R</i>² = 0.74, NSE = 0.73.Results demonstrate that integrated uncertainty quantification combining high-resolution DEMs, advanced gap-filling, and SUFI-2 optimization significantly enhances SWAT performance in humid tropical basins, providing a robust framework for flood forecasting and water resource planning.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829540","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":"Integrated meteorological and hydrological drought assessment using a GIS–PCA framework in a humid tropical catchment of the Western Ghats, India","authors":"T. M. Sharannya,&nbsp;K. A. Mary Ashna,&nbsp;K. Naveena","doi":"10.1007/s11600-026-01873-w","DOIUrl":"10.1007/s11600-026-01873-w","url":null,"abstract":"<div><p>Drought is a critical natural hazard that poses significant challenges across various sectors, particularly in regions with high population density and water dependency. Despite their slow onset, droughts are difficult to manage due to their uncertain duration, intensity, and spatial extent. This study presents a comprehensive analysis of drought events over a 20-year period (2002–2022) in the Bharathapuzha River Basin (BRB), a humid tropical catchment in Kerala, India. Both meteorological and hydrological droughts were assessed using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), respectively, across multiple timescales (1-, 3-, 6-, and 12-month). The study integrates SPI and SDI using Principal Component Analysis (PCA) to derive a Composite Drought Index (CDI) for assessing overall drought severity. The CDI effectively captures the compounded impacts of precipitation deficits and streamflow reductions. Results reveal a strong relationship between SPI and SDI, particularly at the 12-month timescale, highlighting the potential of the composite approach for comprehensive drought assessment. The 2016–2017 drought emerged as the most severe during the study period, corroborated by records from the Kerala State Disaster Management Authority (KSDMA). While validated against historical drought records, the CDI showed good agreement, indicating its potential utility for drought assessment. Spatial mapping of drought severity revealed significant intra-basin variability, emphasizing the need for region-specific mitigation strategies. The study recommends adaptive measures such as rainwater harvesting, recharge well construction, and catchment reforestation to build long-term resilience. These insights are essential for informed water resource planning and drought-risk reduction in the BRB and similar humid tropical regions.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11600-026-01873-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829392","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":"Investigating hidden disaster factors in coal mines using UAV: case study of Tongxin Coal Mine, Shanxi Province, China","authors":"Youfang Liao,&nbsp;Meng Chen","doi":"10.1007/s11600-026-01883-8","DOIUrl":"10.1007/s11600-026-01883-8","url":null,"abstract":"<div><p>Traditional investigations into hidden disaster-causing factors in coal mines are severely constrained by low efficiency, long detection cycles, and terrain limitations, which hinder timely safety management in mining operations. To address these challenges, this study employed unmanned aerial vehicle (UAV)-borne visible light and infrared thermal remote sensing technologies to conduct rapid, high-precision scanning and identification of hidden hazards at Tongxin Coal Mine, a representative mine in the Datong Mining Area of Shanxi Province, China. By optimizing UAV flight parameters (altitude, azimuth, and shooting width), key geometric parameters of ground fissures (extension length, strike direction, and development scale) were quantitatively extracted and calculated with a relative error of less than 8%. Visible light imagery achieved full-coverage detection of surface water bodies (rivers, ponds, springs, etc.), while the fusion of infrared and visible light data enabled the capture and dynamic tracking of ground high-temperature anomalies, with a hidden fire zone identification accuracy of 92%. This integrated technology also identified surface bedrock outcrops, refined the boundaries of weathered rock areas (coincidence rate &gt; 88%), and delineated the spatial location, scale, and potential hazard range of waste rock piles. Additionally, UAV remote sensing effectively detected illegal mining shafts and geomorphic features prone to landslides, which are difficult to identify via traditional methods. The results demonstrate that UAV-based remote sensing overcomes the shortcomings of conventional ground surveys and satellite remote sensing, providing a low-cost, high-efficiency, and high-safety technical approach for the detection of hidden coal mine disasters. This research lays a technical foundation for the construction of smart mines and the precise management of mining hazards.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829295","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":"Seismic low-frequency data extrapolation based on SES-UNet","authors":"Rongsen Du,&nbsp;Guoxin Chen,&nbsp;Jinxin Chen,&nbsp;Yuli Qi,&nbsp;Haiyang Lu,&nbsp;Jun Li,&nbsp;Chunfeng Li,&nbsp;Yinpo Xu","doi":"10.1007/s11600-026-01880-x","DOIUrl":"10.1007/s11600-026-01880-x","url":null,"abstract":"<div><p>The low-frequency components in seismic data serve as critical constraints for constructing macro-scale velocity models of subsurface media. However, due to data acquisition limitations and noise contamination, seismic data often lack low-frequency information, resulting in compromised accuracy in subsequent imaging outcomes. To tackle this issue, a Swin-UNet–based extrapolation framework with Squeeze-and-Excitation modules is proposed, combining the Swin Transformer’s global representation capability with the local feature extraction strength of convolutional neural networks. The Squeeze-and-Excitation modules improve the channel information of skip connections through an attention mechanism, while the structural similarity index is integrated into the loss function to preserve the structural integrity of seismic data. Experiments on synthetic data show that the network effectively extrapolates low-frequency data while demonstrating noise resilience and generalization ability. Full waveform inversion using the reconstructed seismic data yields improved delineation of stratigraphic boundaries and complex geological features. Finally, field data experiments confirm the practical utility of the proposed method.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829287","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":"Improvement and application of ESPAC method based on cross-correlation spectrum adaptive segmental fitting technology","authors":"Mingming Wu,&nbsp;Lianghong Zhang,&nbsp;Hesheng Zeng,&nbsp;Zhixian Gui,&nbsp;Guixi Liu","doi":"10.1007/s11600-026-01893-6","DOIUrl":"10.1007/s11600-026-01893-6","url":null,"abstract":"<div><p>As a convenient and efficient passive-source geophysical method, microtremor exploration is widely used. The traditional Extended Spatial Autocorrelation (ESPAC) method tends to generate high-frequency cross-artifacts with sparse arrays. Although the Modified ESPAC (M-ESPAC) method can eliminate these artifacts, its inversion depth (less than twice the array radius) is much lower than ESPAC’s 3–5 times. To resolve this contradiction, this paper proposes a Further Modified ESPAC (FM-ESPAC) method based on cross-correlation spectrum adaptive segmental fitting. First, it defines the first intersection frequency <i>f</i>₀₁ between the cross-correlation curve and the frequency axis as the adaptive segmentation threshold. Then, adaptive segmental fitting is performed using <i>f</i>₀₁: the low-frequency band (<i>f</i> ≤ <i>f</i>₀₁) adopts ESPAC’s zero-order Bessel function J₀ fitting to retain low-frequency responses, while the high-frequency band (<i>f</i> &gt; <i>f</i>₀₁) uses M-ESPAC’s analytic signal and first-kind zero-order Hankel function H₀⁽¹⁾ fitting to eliminate cross-artifacts. Finally, the array-averaged dispersion spectrum is obtained via superposition and normalization. Simulation experiments (triangular/linear arrays) and practical cases (Enshi geothermal exploration, Antarctic ice sheet detection) verify that FM-ESPAC not only eliminates high-frequency cross-artifacts but also inherits ESPAC’s low-frequency information to ensure inversion depth, showing significant advantages in the case of sparse arrays and insufficient spatial sampling.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829286","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}