On May 4, 2026, a catastrophic explosion occurred at a fireworks factory in Liuyang, Hunan Province, China. To effectively extract waveform characteristics of the explosion and evaluate its scale, this study conducts a single-station continuous waveform analysis based on weak seismic signals recorded by the Liuyang seismic station, located approximately 39 km from the explosion center. The results indicate that the Diting seismic waveform foundation model exhibits remarkable recognition capabilities for weak signals under low signal-to-noise ratio (SNR) conditions. It accurately identified two major explosion events at 16:42 and 16:50 from the continuous waveform signals. Furthermore, the back-azimuth and epicentral distance calculated from the three-component waveforms are highly consistent with the actual spatiotemporal parameters of the explosions. The waveform characteristics of the identified explosion events show that the energy is mainly concentrated in the low frequency band below 10 Hz. The dominant frequency of the S-wave is notably low, and the signal duration is relatively long, reflecting the complex physical process of near-surface multi-point continuous chain detonations of fireworks. Based on the local magnitude and the empirical magnitude-yield relationship, the TNT equivalent yields of the two events are estimated to be approximately 6.0 t and 9.9 t, respectively. These results demonstrate the potential of artificial intelligence methods for automated event detection in single-station continuous waveform records. The proposed approach may support rapid emergency assessment of explosions and other non-natural seismic events.

The southern Sichuan industrial mining area is an important region of shale-gas hydraulic fracturing. It has experienced an exponential growth in seismic activity since 2016. It is one of the most active earthquake swarm zones on the eastern margin of the Sichuan-Yunnan block. This study addresses the scientific challenge of intelligently extracting spatiotemporal evolution laws from massive seismic data by employing multisource data fusion and artificial intelligence-enhanced analysis to reveal the spatiotemporal evolution and migration patterns of seismic activity under mining disturbances. The three main aspects of this work are as follows: (1) a coupled tectonic-engineering analysis was performed to analyze the tectonic controlling factors of the earthquake distribution by integrating surface fold morphology, basement burial depth zoning, spatial configuration of the Silurian Longmaxi Formation mining layers, and high-resolution seismic reflection profiles. (2) Through intelligent identification of earthquake clusters using machine learning algorithms such as HDBSCAN clustering and K-nearest neighbors supervised classification, 31 typical earthquake clusters were extracted from precisely located seismic catalogs, and spatial vulnerability zones and their dynamic migration patterns were quantitatively characterized. (3) The spatiotemporal characteristics were decoupled by combining time-series clustering to isolate interference events, and an evolution model of “spatial morphology-temporal clustering” of earthquake clusters was constructed. This model revealed the synergistic driving mechanism of mining stress and regional tectonic stress. The results showed that earthquakes are concentrated in fold structural units with weak control by the known faults in the Changning Anticline and the basement junction zone on the southern flank of Weiyuan. The earthquake clusters exhibit directional migration southeastward in the Weiyuan-Changning block and south-westward in the Rongchang-Luxian block, ultimately forming a new earthquake-prone area in Luxian-Da’an. The spatiotemporal evolution model of earthquake clusters offers a decision-making framework for long-term regional economic development planning and oil and gas extraction management.

The 2015 Mw 7.8 Nepal earthquake occurred in the Himalayan tectonic belt, where the Indian Plate collides with the Tibetan Plateau. High-precision ground gravimetry can be used to detect transient deep mass changes before the earthquake but limited by sparse absolute gravity stations and the effects of complex surficial factors. In this study, we present new evidence from hybrid terrestrial gravity measurements, carried out in southern Tibet near the epicenter of the 2015 Nepal earthquake. Measurements from 9 relative and 3 absolute gravity stations are integrated using a novel adjustment method. The results confirm the significant regional gravity increase before the 2015 Nepal earthquake, with a rate of about or larger than 15 mu Gal/yr (1 mu Gal = 10- 8 m/s2) at four stations during 2010-2013. We show that this gravity increase cannot be explained by vertical ground motion and/or local hydrological processes. Furthermore, due to inadequate spatial resolution, the observed gravity change could not be detected by the Gravity Recovery and Climate Experiment (GRACE) measurements. We suggest that the gravity increase could be caused by preseismic strain and mass (fluid) transfer in a broad seismogenic source region north of the Main Frontal Thrust. Absolute gravity observations show that the gravity increase stopped after the 2015 Nepal earthquake. Our results contribute to the exploration of possible precursors of large continental earthquakes and shed light on the potential mechanisms of large earthquakes in the Indo-Asian collision zone.

Automatic earthquake detection and phase picking are increasingly performed with deep-learning models, yet how they behave under adverse, out-of-distribution conditions remains poorly constrained, particularly in regions absent from existing training datasets. We assemble a deliberately challenging benchmark of 846 manually reviewed events recorded by a compact local network in Central Asia, dominated by low-magnitude seismicity on noisy stations across local to regional distances, and use it to evaluate four detectors under a single protocol: the DiTing seismic foundation model (DT-SFM), the open-source PhaseNet and EQTransformer pickers, and a tuned STA/LTA detector. The deep-learning models are applied with their default configurations and without regional fine-tuning, while the STA/LTA detector is tuned to provide a strong baseline, and all are scored against the same manual reference picks. Ranked by P-phase F1, the order is DiTing (0.307), tuned STA/LTA (0.161), PhaseNet (0.127), and EQTransformer (0.077): only DiTing surpasses the classical detector, while both open-source pickers fall below it as their recall declines sharply with epicentral distance. A threshold analysis shows that the missed arrivals of PhaseNet are largely recoverable by lowering its probability threshold, so its position below the classical baseline is specific to the default operating point; those of EQTransformer are not recoverable in the same way. Timing accuracy is comparable across all methods, so detection rather than timing is the discriminating factor. The results delineate the limits of current pickers under demanding conditions and provide guidance for their use where no local training data exist.

Gravity changes resulting from deep geo-fluid mass transfer can be detected through in-situ repeated observations. Starting from late 2017 and early 2019, respectively, two regions located on the southeast and northeast of the 2021 Yangbi MS6.4 earthquake, in China, exhibited significant gravity increases. For modeling this gravity change, we adopt an equivalent mass source model with multiple disks, together with a Monte Carlo-based inversion algorithm to estimate model parameters. Tests on synthetic data suggest that this approach simultaneously estimates geometric parameters and density change of the disk models, even in the presence of noise interference. We applied this method to invert a double-disk model to approximate the mass “hypocentroids” based on the gravity increases in two regions. Our analysis identified fluid diffusion footprints reflected by background seismicity migration, with estimated rates of 5 m2/s and 3 m2/s, aligning with areas of significant gravity increase. This study suggests that the gravity increases before the Yangbi earthquake may be related to deep yearly-scale deep crustal fluid migration in the broad regions.

The Late Mesozoic Yunmengshan (YMS) batholith, exposed along the northern margin of the North China Craton, comprises the Shicheng diorite and the main granitoids, and provides important records of Late Jurassic–Early Cretaceous magmatism in the Yanshan Belt. We present in situ zircon U-Pb ages and Hf-O isotopes, together with apatite geochemistry, to constrain its magma sources and emplacement history. Zircon U-Pb dating identifies three magmatic episodes at ca. 156 Ma, ca. 143 Ma, and ca. 139–137 Ma for the Shicheng diorites, Yunmengshan medium-grained monzogranites, and fine-grained monzogranites, respectively, demonstrating incremental batholith assembly. The Shicheng diorite has zircon εHf(t) values of −17.8 to −13.4 and δ18O values of 5.7 ‰ to 6.9 ‰. Combined with its major and trace element compositions, it suggests derivation mainly from an enriched lithospheric mantle source with variable involvement of crustal materials. In contrast, the medium- and fine-grained monzogranites display similarly negative but variable zircon εHf(t) values (−21.3 to −14.7 and − 25.6 to −12.1, respectively) and relatively homogeneous zircon δ18O values (5.8 ‰ to 7.0 ‰). These isotopic characteristics indicate derivation mainly from isotopically heterogeneous Neoarchean to Paleoproterozoic mafic lower crust beneath the North China Craton. Apatite trace element compositions further reveal contrasting crystallization histories between the two granitoid facies. Apatite from the medium-grained monzogranites record stronger effects of progressive plagioclase crystallization prior to or during apatite growth and a greater influence of allanite fractionation, whereas these effects are weaker in apatite from the fine-grained monzogranites. The two granitoid facies are therefore interpreted as genetically related but distinct magma batches rather than products of continuous fractional crystallization from a single parental magma. The Yunmengshan batholith thus records multiple magma sources and incremental assembly, involving mantle-derived magmatism, crust-mantle interaction, and subsequent reworking of ancient lower crust during subduction-related lithospheric modification beneath the North China Craton.

Abstract Accurate identification of seismic event types is a key technique for improving earthquake monitoring capabilities and compiling high-quality seismic catalogs. In this study, we train a DiTing Seismic Event Classifier (DTSEC), which has been fine-tuned from the pretrained seismogram foundation model with over 100 million parameters to classify earthquakes, explosions, and collapses. DTSEC achieves an overall accuracy of 92.83% on the test set. Specifically, accuracies for earthquakes, explosions, and collapses are 96.35%, 91.23%, and 91.41%, respectively. Compared to models trained from scratch, DTSEC achieves strong performance using only 20% of the training data, demonstrating superior sample efficiency. To test regional adaptability, we apply DTSEC to a dataset from the Henan Seismic Network, China. Because of unique regional characteristics, the initial classification performance decreases. However, by adding a small number of local samples through joint fine-tuning, the results show that the performance of the model has been successfully improved in this region. This approach adapts the model to local features while preserving the generalization benefits from the large-scale global dataset. These results suggest that fine-tuning with limited local samples allows the model to adapt to regional characteristics effectively. This study presents a practical, deployable solution for seismic event classification in data-scarce regions.

Conventional gravity anomaly forward methods in the spatial-domain are grounded in the theoretical foundation of full-spectrum response. However, widely used gravity field spherical harmonic models such as Earth Gravitational Model (EGM) and European Improved Gravity model of the Earth (EIGEN) are finite-degree spherical harmonic expansions, inherently possessing a band-limited nature. This theoretical mismatch between the full-spectrum forward assumption and the band-limited data leads to unavoidable truncation errors, thereby compromising the accuracy and reliability of subsequent inversion. To address the above issues, this study systematically elaborates a wavenumber-domain rapid forward method for 3D gravity anomalies and their gradient tensors. By decomposing the dense Green's function matrix into the product of three sparse matrices in the wavenumber-domain, the computation workflow is straightforward. The truncation error between the wavenumber-domain and spatial-domain forward can systematically decreases as the observation points increases. The elaborated forward approach replaces the layer-by-layer calculation process with efficient sparse matrix multiplication; compared with spatial-domain methods, computational efficiency is significantly improved. The advantage of this method lies in the consistency between its inherent finite-frequency characteristics and the degree limitation of spherical harmonic models in the wave-number domain. This forward framework is more theoretically compatible with the band-limited nature of finite-degree gravity data, thereby effectively avoiding the theoretical mismatch problem inherent in traditional spatial-domain methods between the full-spectrum model and band-limited data. Therefore, the wave-number domain forward method elaborated provides a computationally efficient, low-memory, and theoretically self-consistent technical solution for processing modelling satellite, aerial, and ground-based gravity and its gradient tensor data.

Summary On 21 May 2021, a moderate earthquake with a magnitude of M6.4 occurred in western Yunnan Province, China. Several felt earthquakes had occurred in the region in the three days preceding this event. We aimed to investigate whether there are indications of crustal fluid migration in this region and, if so, the extent to which regional seismicity might be influenced by crustal fluids. Using the epidemic-type aftershock sequence (ETAS) model, we analyzed the earthquake triggering process and the nonstationary background rate. In addition, seismicity rates were used to estimate Coulomb stress changes. Specifically, the results suggest that the background rate increased from 0.2 to 15 events per day during the Yangbi earthquake sequence, and the spatial evolution of the background rate was broadly consistent with a fluid diffusion pattern. The evaluated cumulative stress change indicates that the stress rate increased by approximately 50 per cent following the M6.4 earthquake. Spatial stress changes suggest migration and expansion of regions with increased stress. These results imply the presence of crustal fluid flow in the study area, with a tendency to diffuse southward.

As the amount of seismic data increasing drastically worldwide, there are ever-growing needs for high-performance automatic seismic data processing methods and high-quality, standardized professional datasets. To address this issue, we recently updated the 'DiTing' dataset, one of the world's largest seismological AI datasets with ~2.7 million traces and corresponding labels, with 1,089,920 three-component waveforms from 264,298 natural earthquakes in mainland China and adjacent areas, and 958,076 Pg, 780603 Sg, 152752 Pn, 25956 Sn earthquake phase arrival tags, in addition to 249,477 Pg, 41610 Pn first motion polarity tags from 2020 to 2023. We also collected 15375 non-natural earthquake waveforms in mainland China from 2009 to 2023 and a manually labeled noise dataset containing various typical noise signals from the China Seismological Network. With the support of the 'DiTing' dataset, we developed and trained several deep learning models referred as 'DiTingTools' for automatic seismic data processing. In the continuous waveform detection and evaluation of more than 1,000 stations over a year across China, 'DiTingTools' has achieved an average recall rate of 80% for event detection, mean square error ±0.2s for P phase picking, and ±0.4s for S, the average identification accuracy rate of Pg first motion polarity reached 86.7% (U) and 87.9% (D), and 75.1% (U) and 73.1% (D) for Pn first motion polarity, the average magnitude prediction error of a single station is mainly concentrated at ±0.5. The remarkable generalization capabilities of 'DiTingTools' were demonstrated through its application on the China Seismic Network. Specifically, 'DiTingPicker', a model within 'DiTingTools' designed for earthquake detection and phase picking, was employed to analyze the M 6.8 earthquake that struck Luding County, Sichuan Province, in 2022. This tool was instrumental in automatically processing data to examine the main shock and intricate fault structures of the aftershocks. The effectiveness of 'DiTingTools' in earthquake prevention and disaster reduction was further validated through these practical applications.

This work introduces a highly efficient multitask parallel Artificial Intelligence model designed for weak seismic signal detection and phase picking, leveraging the capabilities of the conventional AI-powered Transformer architecture. By integrating a multi-part data extraction strategy, a multi-GPU parallel processing framework, and a multi-layer network schedule, we significantly enhance the accuracy of detecting P- and S-phases while optimizing the model's efficiency. The accuracy attained for the P and S phases was 92% and 76% when employing only a segment of the dataset. When we incorporated the entire dataset, the precision improved to 97% for P phases and 87% for S phases. Notably, our model demonstrates higher accuracy compared to existing deep-learning and traditional detection algorithms. When applied to extensive seismic phase observation data collected from 2020 to 2023 in mainland China, our model consistently demonstrated high accuracy, confirming its generalizability across various spatiotemporal contexts. It also exhibited exceptional sensitivity to subtle changes in waveform data, highlighting its promising potential for detecting smaller seismic events with even greater resolution in future applications.

Regional earthquake correlation represents a fundamental yet open issue in seismicity and geodynamics. This study addresses the synchronicity of earthquake occurrences through statistical correlations among the active fault zones along the eastern margin of the Qinghai-Tibetan Plateau by means of a big data analysis approach. Utilizing the latest earthquake catalog (1970-August 2024), in association with the 17 major fault zones, we introduce an innovative method for catalog segmentation and apply Z-tests, Pearson correlation coefficients, and correlation network analysis to examine fault seismic activity relationships. The results reveal a seismic spatiotemporal migration pattern characterized by reduced seismicity near prominent earthquake epicenters, with subsequent large earthquakes occurring on non-adjacent faults and increased activity around epicenters. Through correlation networks, we identify three major seismic activity clusters: the eastern boundary of the Bayan Har Block, the eastern boundary of the Sichuan-Yunnan Rhombic Block, and the Sanjiang Lateral Collision Zone, aligning with traditional tectonic block divisions, validating the seismic activity similarity clustering method and filling the statistical gap in conventional classifications. Surprisingly, some faults belong to neither the same seismic belt nor the same geological block, exhibiting a regional-scale remote triggering pattern of earthquake nucleation or structural evolution under the concept of a complex fault system. Additionally, we show that while parallel or conjugate fault orientations do not necessarily lead to synchronized seismic activity, faults exhibiting synchronized activity typically have parallel or conjugate geometries. The revealed seismic activity patterns contribute to improving earthquake forecasting and offer a fresh perspective on regional tectonic evolution and seismic dynamics.

Summary The left-lateral Xianshuihe fault is a seismically active fault system located at the eastern boundary of the Tibetan Plateau. We analyzed the Sentinel InSAR data from 2014 to 2021 to study the temporal and spatial variation of fault creep along the Xianshuihe fault. We applied the InSAR stacking method and the coherence-based SBAS method to derive the Line-Of-Sight (LOS) velocity map and time-series from both the ascending and descending orbits. We studied both the secular component and the time-dependent component of surface deformation from InSAR. We compare the InSAR-derived velocity maps with the GPS-derived velocity field and found that these two independent measurements are consistent. A 200 km long creeping section is identified along the central segment of the Xianshuihe fault. The surface creep rate is measured to be ranging from 0 to 6 mm yr−1. We combined the elastic dislocation model and the InSAR velocity maps to invert for the geodetic fault slip rate and the aseismic slip distribution in the upper crust. The secular fault creep model shows that most of the Xianshuihe fault is creeping at depth. The time-dependent fault creep model indicates that the maximum aseismic slip rate from Bamei to Kangding accelerated from 30 mm yr−1 to 40 mm yr−1 and then decayed to 5 mm yr−1 from 2014 to 2021. The fully creeping segment of the Xianshuihe fault seems to become a partially locked segment in a short time period (a couple of years). We suspect that the acceleration of fault creep from 2017 to 2019 is linked to dynamic triggering by passing seismic waves or fluid migration. Finally, we compare the temporal variation of fault creep with previous studies and discuss the earthquake hazard implications.

The North China Craton (NCC) is one of the oldest cratons in the world. It is important to understand the mechanism of NCC destruction. The lithosphere effective elastic thickness (Te) with its anisotropy can provide new insights into those mechanisms, as the spatial distribution patterns of Te and its anisotropy serve as distinctive indicators of past tectonic processes that are difficult to characterize through conventional approaches. The Te and load ratio (F) around the NCC region are estimated using a joint inversion method that integrated both admittance and coherence techniques. The Te anisotropy is estimated using fan wavelet coherence method. The derived Te and F are used to compute flexural isostatic anomalies (FIA) using the theoretical Bouguer admittance method. We find the Te, F, Te anisotropy, and FIA vary a lot from east to west in the NCC area. The Ordos Basin in the western North China Craton (WNCC) has high Te, low F, and weak Te anisotropy, suggesting that the lithosphere of Ordos Basin have high strength and in a state of stable. The Trans-North China Orogen (TNCO) is located in transition zone of Te, F, Te anisotropy, and FIA, indicating that the TNCO could be the front of collision between Eurasia Plate and western Pacific Plate. The eastern North China Craton (ENCC) lithosphere has been largely modified by the deep tectonic activities due to the subduction of western Pacific Plate.

Given the complexity of earthquake forecast and the current limitations in the application of artificial intelligence (AI), we propose a conceptual framework for a novel AI system, HuiShangGPT, intended to act as an expert in discussion on the trend of seismicity. This system, still in the conceptual stage, aims to integrate AI into the empirical approaches traditionally used in earthquake forecasting. The proposed HuiShangGPT system would not only assist in the comprehensive analysis of seismic data but also contribute to the expert panel discussions, enhancing the decision-making process. We outline the envisioned functionalities and potential benefits of such a system, while acknowledging the technical and practical challenges that need to be addressed for its future implementation.

Since the 1975 MS7.3 Haicheng earthquake, spatio-temporal variations in the gravity field have attracted much attention as potential earthquake precursors. Recent technical advances in terrestrial gravity observation, along with the construction of a high-precision mobile gravity network covering Chinese mainland, have positioned temporal gravity variations (GVs) as an important tool for clarifying the signal characteristics and dynamic mechanisms of crustal sources. Reportedly, crustal mass transfer, which is affected by stress state and structural environment, alters the characteristics of the regional gravity field, thus serving as an indicator for locations of moderate to strong earthquakes and a seismology-independent predictor for regions at risk for strong earthquakes. Therefore, quantitatively tracking time-varying gravity is of paramount importance to enhance the effectiveness of earthquake prediction. In this study, we divided the areas effectively covered by the terrestrial mobile gravity network in the Sichuan-Yunnan region into small grids based on the latest observational data (since 2018) from the network. Next, we calculated the 1- and 3-year GVs and gravity gradient indicators (amplitude of analytic signal, AAS; total horizontal derivative, THD; and amplitude of vertical gradient, AVG) to quantitatively characterize variations in regional time-varying gravity field. Next, we assessed the effectiveness of gravity field variations in predicting earthquakes in the Sichuan-Yunnan region using Molchan diagrams constructed for gravity signals of 13 earthquakes (M ≥ 5.0; occurred between 2021 and 2024) within the terrestrial mobile gravity network. The results reveal a certain correspondence between gravity field variations and the locations of moderate and strong earthquakes in the Sichuan-Yunnan region. Furthermore, the 3-year AAS and AVG outperform the 3-year THD in predicting subsequent seismic events. Notably, the AAS and AVG showed large probability gains prior to the MS6.8 Luding earthquake, indicating their potential for earthquake prediction.

In this paper,we computed the fractal dimension of three survey areas within the central and southern sections of the Tan-Lu fault zone using fractal analysis.Subsequently,simulations were conducted to analyze the gravity response under a forward model of equivalent density changes.Additionally,we thoroughly investigated the seismic monitoring capabilities of the gravity network in the central and southern regions of the Tan-Lu fault.Expanding on these analyses.Recent gravity field variations were examined in the mid-southern segment of the Tan-Lu fault zone and its surrounding areas from 2013 to 2023.The results indicate that the observation capabilities of the northern network in the study area outperform those of the southern gravity network,with the northern network demonstrating a more evenly distributed coverage.The optimal gravity anomaly recovery effect for the entire study area is achieved at a resolution of 0.5° × 0.5°.With an equivalent observable signal in the range of 30 × 10-8 m/s2 to 40 × 10-8 m/s2,the spatial resolution of the gravity network's field source is estimated to be approximately 55 km.From 2013 to 2023,a significant positive change has been observed in the gravity field within the study area.The Tan-Lu fault zone plays a crucial role in governing the crustal movement in this region,with the dextral strike-slip movement trend of the fault persisting.Small earthquakes occur more frequently in the southern section of the fault zone,while strong earthquakes are less common.The alignment of gravity field changes with the fault strike indicates ongoing activity in the fault zone without any signs of locking.In the central segment of the Tan-Lu fault zone in the Shandong region,there appears to be a weaker correlation between gravity field changes and fault trends.This discrepancy may suggest that the area is locked,resulting in the accumulation of stress and strain.It is imperative to monitor the continuous evolution of the gravity field in this region to gain insights into potential seismic risks.

The gravitational potential field plays a pivotal role in interdisciplinary geological exploration. Recently, significant progress has been made in inversion techniques for three-dimensional gravity anomaly and its gradient tensor data in the spatial domain. However, in geoscientific research, gravity anomalies derived from models such as the Earth Gravitational Model (EGM) and other characteristic series models are prevalent. These spherical harmonic models have limitations due to their finite order, which can lead to truncation errors when traditional spatial domain inversion methods are applied. To address this problem, this paper presents a novel inversion method for three-dimensional gravity and its gradient tensor data in the wavenumber domain. Unlike the spatial domain inversion, the Green's function matrix in the wavenumber domain is sparse, resulting in substantial improvements in computational efficiency and reduced calculation time. Furthermore, to tackle the issue of multiple solutions often encountered in wavenumber domain inversions, regularization techniques commonly used in the spatial domain have been incorporated. This strategic integration stabilizes the inversion process and enhances the reliability of the results. To validate the effectiveness of the proposed method, rigorous testing using theoretical model data and field data has been performed. The inversion results clearly demonstrate the robustness of this novel approach, making it highly suitable for inverting three-dimensional gravity anomalies and their gradient tensor data.

This study investigates the variations in lithospheric mechanical strength of the Iranian Plateau by integrating flexural analysis theory with gravity anomalies and topographic data, employing Bouguer admittance, correlation analysis, and Bayesian inversion methods. The results show that the effective elastic thickness (Te) of the Iranian Plateau range from 0 to 65 km, exhibiting significant regional variations. High Te values are primarily concentrated in the Zagros Orogenic Belt and the South Caspian Basin, reflecting stronger lithospheric strength in these regions. In contrast, lower Te values are observed in central and eastern areas, particularly around the Lut Block, associated with lithospheric thinning and mantle-derived magmatic activity. Tectonic activity, heat flow, and shear-wave velocity show a close relationship with Te: regions with high heat flow typically exhibit lower Te values, indicating weaker lithospheric strength, while Shear-wave velocity is positively correlated with Te, with higher velocities corresponding to stronger lithospheric mechanical strength. These findings provide essential insights into the crustal structure and lithospheric mechanical properties of the Iranian Plateau and comparable regions.

Gravity anomalies reflect the geophysical response to subsurface density structures. Traditionally, the terrain density is assumed to be a constant when calculating Bouguer gravity anomaly. But deviations from this assumption may induce high-frequency signals to Bouguer gravity anomaly. This study introduces a Bayesian method for computing Bouguer gravity anomaly. It incorporates a smoothness prior for the Bouguer gravity anomaly and estimates near-surface density parameters to minimize the Akaike’s Bayesian Information Criterion (ABIC) value. The effectiveness of this method is validated through theoretical model tests and calculations on two observed gravity profiles in Yunnan. The results indicate that the Bouguer gravity anomaly profiles estimated using the Bayesian approach need no extra filtering, exhibit correlations with the profile’s crustal structure, and effectively reveal subsurface crustal density variations. Moreover, the obtained density variations offer insights into the near-surface rock density of geological time. Specifically, Cenozoic formations have a density of roughly 2.65 to 2.90 g·cm−3, Mesozoic formations 2.61–2.91 g·cm−3, and Paleozoic formations 2.61–2.92 g·cm−3. Magmatic rock regions generally show higher density values. Additionally, these estimated densities show a positive correlation with the global VS30 seismic velocity estimates, suggesting a new geophysical approach for seismic site classification. The findings of this study are significantly valuable for near-surface density estimation and Bouguer gravity anomaly calculations.