Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 1
The effect of heterogeneity (dissimilar materials) and geometry constituting an interface is an important problem in earthquake source mechanics. These two parameters in the fault interface are responsible for complex rupture propagation and instabilities compared to the homogeneous planar interface. Here, a boundary integral spectral method (BISM) is proposed to capture the in-plane rupture propagation in the non-planar bi-material interface. The conventional traction BISM suffers from the disadvantages of hyper singularity and regularisation is needed (Sato et al., 2020; Romanet et al., 2020; Tada and Yamashita, 1997). So, we are utilising the representation equation arising from the displacement formulation devised by Kostrov (1966). It uses the elastodynamic space-time convolution of Green’s function and traction component at the interface. These displacement boundary integral equations (BIEs) are the inverse equivalent of traction BIEs. When applied to an interface between heterogeneous planar elastic half-spaces, these displacement BIEs have yielded simple and closed-form convolution kernels (Ranjith 2015; Ranjith 2022). Displacement BIEs of this kind have not been utilised to analyse fracture simulation for non-planar bi-material interfaces until now. We assume the small slope assumption (Romanet et al., 2024) in our formulation to get the required displacement BIEs. Also, we expand the displacement BIEs of a non-planar bi-material interface to the leading order to obtain the non-planarity effects. Finally, we present a general spectral boundary integral formulation for a non-planar bi-material interface independent of specific geometry and traction distribution in a small fault slope regime.
How to cite: Kumar, S. and Kunnath, R.: Boundary integral spectral formulation for in-plane rupture propagation at non-planar bi-material interfaces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8250, https://doi.org/10.5194/egusphere-egu25-8250, 2025.
Local heterogeneities on a steadily propagating crack front create persistent disturbance along the crack front. These propagating modes are termed as crack front waves. There have been numerous investigations in the literature of the crack front wave associated with a Mode I crack (for e.g., Ramanathan and Fisher, 1997, Morrissey and Rice, 1998, Norris and Abrahams, 2007, Kolvin and Adda-Bedia, 2024). It has been shown that the Mode I crack front wave travels with a speed slightly less than the Rayleigh wave. However, similar investigation of the Mode II rupture has got minimal attention. Although, Willis (2004) demonstrated that for a Poisson solid, Mode II crack front waves do not exist for crack speeds less than 0.715, explicit results on the speed of the crack front waves, when they exist, have not been reported in the literature. The focus of the present work is on a numerical investigation using a recently developed spectral boundary integral equation method (Gupta and Ranjith, 2024) to obtain the speed of the Mode II crack front waves. Further, the perturbation formulae for Mode II crack, developed by Movchan and Willis (1995) are exploited to validate the numerical results on the crack front wave speeds.
How to cite: Mouli, Y. S. C. and Kunnath, R.: Crack front waves under Mode II rupture dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14737, https://doi.org/10.5194/egusphere-egu25-14737, 2025.
With the continuous development of deep learning technologies, fault prediction techniques based on various neural networks have been evolving. The deep learning modules based on U-Net residual networks have shown significant advantages in both learning efficiency and effectiveness. In this paper, we propose a deep learning model that integrates a 3D U-Net residual architecture, Convolutional Block Attention Module (CBAM), and Multi-scale Enhanced Global Attention (MEGA) module for automatic seismic fault detection and segmentation. This model can effectively handle complex 3D seismic data, fully exploiting both spatial and channel information, significantly improving the prediction accuracy for small faults, while only slightly increasing the computational cost.
Firstly, the model uses the 3D U-Net as the backbone framework, where the residual blocks (BasicRes) extract features through multiple convolution layers. The CBAM module is incorporated to apply attention weighting, enhancing the model's ability to focus on critical information. The CBAM module combines channel attention and spatial attention, effectively adjusting the importance of feature maps from different dimensions, enabling the model to identify potential fault features in complex seismic data.
Secondly, the MEGA module is introduced into the model, which further improves the model's feature representation ability by fusing multi-scale features and applying a global attention mechanism. By weighting global information, the MEGA module helps the model better capture key seismic fault features during feature fusion. This design allows the model to focus not only on local details but also to fully utilize the global contextual information in 3D data, thereby enhancing the accuracy of fault detection.
After validation, the model achieved promising results in seismic fault detection tasks, automatically identifying and segmenting fault structures in seismic data. The accuracy was improved from 80% with the original 3D U-Net residual network to 85%-87%. This provides strong support for applications such as seismic exploration and subsurface imaging.
Keywords: Seismic Fault Detection, 3D U-Net, Convolutional Block Attention Module (CBAM), Multi-scale Enhanced Global Attention (MEGA), Deep Learning
How to cite: wang, Y.: Application of Optimized 3D U-Net Residual Network with CBAM and MEGA Modules in Seismic Fault Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1772, https://doi.org/10.5194/egusphere-egu25-1772, 2025.
Seismic attribute analysis technology has been widely used in the prediction of fluvial reservoir sand body, but the traditional seismic attribute fusion technology based on linear model has low prediction accuracy and limited application range. This study focused on the non-linear fitting between seismic attributes and reservoir thickness, and used a variety of machine learning technologies to predict the fluvidal reservoir in Chengdao area of Dongying Sag (China).The channel sand body in Chengdao area is deep buried, thin in thickness, fast in velocity and affected by gray matter, so it is difficult to predict, which greatly restricts the oil and gas exploration in this area. In this study, on the basis of fine well earthquake calibration, several seismic attributes such as amplitude, frequency, phase, waveform and correlation are extracted and correlation analysis is done to remove redundant attributes. Then model training and parameter set optimization are carried out, thickness prediction is carried out with verification set, and vertical resolution is improved by logging reconstruction and waveform indication inversion. The results show that compared with the conventional support vector machine and back propagation neural network, the prediction accuracy of echo state network optimized by Sparrow algorithm is greatly improved. Based on the comprehensive prediction method of fluvial reservoir, three large channels developed in the lower part of Chengdao area and several small channels developed in the upper part of Chengdao area are effectively described. The research method can be used for reference to the similar complicated river facies prediction.
How to cite: Huang, Z. and Zhang, J.: Study and Case Application of Fluvial Reservoir Prediction Based on the Fusion of Seismic Attribute Analysis and Machine Learning Technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3391, https://doi.org/10.5194/egusphere-egu25-3391, 2025.
Seismic inversion in geophysics is a method that uses certain prior information, such as known geological laws and well logging and drilling data, to infer the physical parameters of underground media, such as wave impedance, velocity, and density, from seismic observation data, and thereby obtain the spatial structure and physical properties of underground strata. Seismic inversion is a highly complex problem with multiple solutions, and with the advancement of collection equipment, the volume of geophysical observation data is increasing at an astonishing rate. This presents new challenges for the accuracy and speed of seismic data inversion methods. There is an urgent need to develop intelligent and efficient inversion technologies for seismic inversion.
Deep learning networks have powerful nonlinear fitting capabilities and can be used to solve complex nonlinear problems, such as seismic inversion. However, the predictive ability of deep learning networks largely depends on the quantity of training data. In the early stages of oil and gas exploration and development, the amount of well logging label data available for training is very limited, which poses a challenge for the application of deep learning in seismic inversion. Semi-supervised learning seismic inversion methods consider both data mismatch issues and well logging data mismatch issues, and can better adapt to inversion problems in real-world scenarios. Unlike supervised learning approaches, semi-supervised learning does not require a large amount of labeled data, thus it can better handle situations of data scarcity or mismatch.
This paper utilizes a semi-supervised learning workflow to perform inversion on post-stack seismic data and has conducted experimental validation on the Marmousi 2 model. The experimental results show that, compared to supervised learning networks, the semi-supervised learning network still exhibits good predictive performance with a limited amount of data, demonstrating better stability in the presence of noise and geological variations, and effectively learns the mapping relationship between seismic data and artificial intelligence. Furthermore, as the amount of training data increases, the performance of the network also improves, confirming the importance of data quantity for training deep learning networks. The application results of the network on actual data indicate that the network has broad application prospects and feasibility. However, since the network is based on a channel-by-channel inversion method, there is still a lack of representation in terms of lateral continuity, which requires further exploration and improvement in subsequent research.
How to cite: zou, C., zhang, J., tang, B., and huang, Z.: Post stack inversion of seismic data based on Semi-supervised learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2973, https://doi.org/10.5194/egusphere-egu25-2973, 2025.
This paper proposes a deep learning model based on 3D Convolutional Neural Networks (CNN) and a custom attention mechanism (ESSAttn) for seismic fault interpretation from 3D seismic data. The model combines the advantages of self-attention mechanisms and convolutional neural networks to enhance the ability to capture and represent features in three-dimensional seismic data. The core innovation of the model lies in the introduction of the ESSAttn layer, which applies a non-traditional normalization process to the input feature queries, keys, and values, thereby strengthening the relationships between features, especially in high-dimensional seismic data. Unlike traditional attention mechanisms, the ESSAttn layer normalizes feature vectors by squaring them and integrates features across depth, width, height, and channel dimensions, significantly improving the effectiveness of attention computation.
The model's role in seismic fault interpretation is reflected in several aspects. First, the 3D convolutional layers automatically extract spatial features from seismic data, accurately capturing the location and shape of faults. Second, the ESSAttn layer enhances critical region features and focuses attention on important areas such as fault zones, reducing the interference from background noise and significantly improving fault detection accuracy. Finally, by using a weighted binary cross-entropy loss function, the model can prioritize fault regions when handling imbalanced data, improving sensitivity to weak fault signals.
The network architecture consists of three main parts: encoding, attention enhancement, and decoding. Initially, two 3D convolutional layers and max-pooling layers are used for feature extraction and down-sampling, followed by the ESSAttn layer to enhance the extracted features. The decoding part restores spatial resolution through upsampling and convolution layers, ultimately outputting the fault prediction results. The model is trained using the Adam optimizer, with a learning rate set to 1e-4.
Experimental results show that the model performs well in seismic fault interpretation tasks, effectively extracting and enhancing fault-related features. It is particularly suitable for automatic fault identification and localization in complex geological environments. The model's automation of feature extraction and enhancement reduces manual intervention, increases analysis efficiency, and demonstrates strong adaptability to large-scale 3D seismic datasets. Furthermore, the model architecture was visualized and saved using visualization tools for easier analysis and presentation.
Keywords: 3D Convolutional Neural Networks, ESSAttn, Attention Mechanism, Fault Interpretation, Weighted Cross-Entropy, 3D Seismic Data, Deep Learning
How to cite: zhang, Y.: "Deep Learning Application for Seismic Fault Interpretation Based on 3D Convolutional Neural Networks and ESSAttn Attention Mechanism", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4961, https://doi.org/10.5194/egusphere-egu25-4961, 2025.
Southeastern Tibet, a segment of the eastern Himalayan Syntaxis, is a significantly deformed area resulting from multistage subduction and the ongoing collision of the Indian and Asian tectonic plates. The region has a clockwise material movement around the indenting corner of the Indian plate, evident on the surface as strike-slip faults aligned with the Himalayan Arc. Numerous scientific studies have focused on the east-west extension and tectonic history of southeastern Tibet; however, the scientific enquiries regarding the depth constraints of the crustal flow process—specifically, whether it is confined to the middle crust or extends to the lower crust beneath southeastern Tibet—remain unresolved. This study employs ambient noise tomography to examine a 3-D high-resolution crustal velocity model for the region, which is crucial for unravelling the mechanisms that regulate crustal deformation and evolution in active orogenic systems. To do this, we examined ambient noise data from 48 seismic stations of the XE network, operational from 2003 to 2004. We obtained Rayleigh wave phase velocities ranging from 4 to 60 seconds and subsequently inverted them to develop a 3-D shear wave velocity model of the region extending to depths of 50 km. Our results reveal persistent low shear wave velocity zones at depths of 15–25 km (within the mid-crust), notably observed between the Indus Tsangpo suture and the Bangong-Nujiang Suture. We contend that the detected low-velocity zones are only linked to mid-crustal channel flow, a mechanism presumably essential for comprehending crustal deformation. Our findings provide significant constraints on the depth localisation of crustal channel flow and the interaction of tectonic forces in southern Tibet, enhancing the overall comprehension of Eastern Syntaxial tectonics.
How to cite: Mandi, A., Kumar, G., Bishoyi, N., and Tiwari, A. K.: 3-D Crustal Shear Wave Velocity Tomography Using Seismic Ambient Noise Data in Southeast Tibet, Close to Namcha Barwa Mountain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1178, https://doi.org/10.5194/egusphere-egu25-1178, 2025.
The tectonic framework of Bhutan Himalaya documents significant along-strike variability in crustal structure and deformation. To visualize this spatial and depth variability, we compile an extensive dataset of surface-wave phase velocities derived from seismic ambient noise and teleseismic earthquakes recorded by the temporary GANSSER network (2013-2014) in Bhutan, aiming to produce Rayleigh phase-velocity maps over the period range of 4 to 50 seconds. We translate the phase-velocity maps into a 3-D shear-wave velocity model stretching from the surface to a depth of 42 kilometres. The employed methodologies enable imaging of the upper to mid-crustal and lower crustal velocity anomalies with a lateral resolution of approximately 25 km. The obtained tomographic model fills a void in the prior established shear-wave velocity structure of Bhutan, encompassing depths from upper-crustal to lowermost crust. Our findings indicate notable mid-crustal to lower-crustal high phase velocity anomalies in central Bhutan (around 90.5). The presence of this significant anomaly within the mid- to lower crustal layer may indicate localized stress accumulation along the Main Himalayan Thrust (MHT) resulting from the interaction of the dipping and sub-horizontal Moho. This area might act as a stress concentration zone, resulting in increased deformation and enhanced shear-wave velocity in the crust. Minor fluctuations in velocity across latitude may result from variations in the local geometry of MHT (dip or ramp-flat transition). Localised high shear velocity in western Bhutan may indicate a zone of crustal thickening. Northeastern Bhutan exhibits modest shear velocity, possibly because of a flat Moho and the partial creeping behaviour of the MHT.
How to cite: Kumar, G. and Tiwari, A. K.: Multiscale Surface Wave Tomography of the Bhutan Himalayas using Ambient Seismic Noise and Teleseismic Earthquake Data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1021, https://doi.org/10.5194/egusphere-egu25-1021, 2025.
In this study, we used the P-wave receiver functions (PRFs) to investigate the crustal structure of northern Morocco, located at the westernmost edge of the Mediterranean, near to the boundary between the African and Eurasian tectonic plates. This region is an integral part of the complex crustal deformation and tectonic system associated with the Alpine orogeny, characterized by concurrent compressional and extensional processes. These dynamics have led to the development of various structural and tectonic models aimed at explaining the area‘s geological evolution. The significant tectonic activity, evident in frequent seismic events, and complex lithospheric deformation, makes it an ideal location for studying crustal variations, lithospheric interactions, and mineralogical contrasts.
To achieve these objectives, we utilized high-quality seismic broadband data from the TopoIberia and Picasso seismic experiments, provided by the Scientific Institute, as well as from the broadband seismic stations operated by the National Center for Scientific and Technical Research (CNRST). The PRFs were extracted by decomposing teleseismic P-waves to isolate the effects of the local crustal structure. The dataset covers a wide range of regional stations, and the RFs provide detailed insights into crustal thickness, density and velocity contrasts, as well as deep discontinuities. Our preliminary results reveal significant variations in Moho depth, ranging from approximately 22.7 km in the eastern part of the region to 51.7 km in the western part. These variations correlate with changes in Vp/Vs and Poisson’s ratios, indicating mineralogical heterogeneity, with compositions spanning from mafic to felsic. These findings provide new constraints for tectonic models and enhance our understanding of the geodynamic processes involved, particularly the interactions between the crust and the upper mantle. This study not only improves our understanding of active tectonics and crustal composition in northern Morocco but also offers valuable insights for refining evolutionary models of the Western Mediterranean within its complex geodynamic context.
Keywords: Teleseismic event, P-wave, Receiver functions, Seismic Network, Vp/Vs ratio, Poisson ratio, Crustal structure, Mineralogical composition, Seismotectonics, Northern Morocco.
How to cite: Zakarya, H., El Moudnib, L., Badrane, S., Zeckra, M., and Lharti, S.: Continental Crustal Structure Beneath Northern Morocco Deduced from Teleseismic Receiver Function: Constraints into structure variation and compositional properties., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9078, https://doi.org/10.5194/egusphere-egu25-9078, 2025.
Ambient noise surface wave imaging has become a powerful tool for mapping subsurface velocity structures. Recent advancements in seismology, including the widespread deployment of high-density arrays such as nodal seismometers and Distributed Acoustic Sensing (DAS) systems, have facilitated the use of subarray-based methods for surface wave dispersion data extraction, such as phase-shift, F-K, and F-J methods. Alternatively, dispersion data can also be derived from two-station approaches, such as the FTAN method. However, integrating dispersion data extracted from subarrays and two-station methods remains challenging. In this study, we propose a joint inversion framework that combines these two types of surface wave dispersion data to achieve improved constraints on subsurface structures. We demonstrate its accuracy and practical applicability by conducting numerical experiments and applying the method to field data. The proposed approach introduces intrinsic spatial smoothing constraints. It effectively integrates subarray and two-station dispersion measurements, resulting in better imaging of subsurface shear-wave velocity structures compared to using either dataset alone. The versatility and potential of this method highlight its promising applications in a wide range of geophysical scenarios.
How to cite: Luo, S.: Joint inversion of surface wave dispersion data derived from subarrays and two-station methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20181, https://doi.org/10.5194/egusphere-egu25-20181, 2025.
Coal seam floor water hazards, caused by stress changes resulting from coal mining, are a common type of mine water disaster, and their monitoring and prevention are critical for mine safety. The mine resistivity method, a geophysical exploration technique, is widely used for monitoring and detecting such water hazards due to its high sensitivity to water-bearing structures. In practical monitoring, it is necessary to rapidly and accurately invert apparent resistivity data. However, traditional linear inversion methods are prone to local optima, leading to biased results. In contrast, deep learning-based inversion methods utilize data mining to train networks, avoiding reliance on initial models and enabling fast computation of global optimal solutions.
This study constructs a multi-layer convolutional and skip-connected U-Net model to capture resistivity features at different scales. The model is trained and validated using synthetic data to evaluate its inversion accuracy and efficiency in monitoring coal seam floor water hazards. The results show that the U-Net-based inversion method can accurately identify low-resistivity anomalies associated with water hazards in the coal seam floor and quickly achieve the global optimal solution.
The method is further applied to the inversion of resistivity models with complex boundaries to simulate the impact of stress changes caused by coal mining on the formation of floor water hazards. The results demonstrate that this method is several times faster than traditional linear inversion methods, while maintaining high consistency with the actual model. Therefore, this inversion method provides an efficient new tool for monitoring coal seam floor water hazards and holds great promise for advancing technologies in mine water disaster prevention and geological exploration.
How to cite: Wang, H. and Hu, Y.: Research on mine electrical resistivity inversion method based on Deep Learning Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5528, https://doi.org/10.5194/egusphere-egu25-5528, 2025.
Here, we disentangle a complex disturbance deposit sequence attributed to the ~M 7 1873 CE Brookings earthquake from lower Acorn Woman Lake, Oregon, USA, using sedimentological techniques, computed tomography, and micro-X-ray fluorescence. The lower portion of the sequence is derived from schist bedrock and has characteristics similar to a local landslide deposit, but is present in all cores, suggesting that it is the result of high frequency (>5 Hz) ground motions from a crustal earthquake triggered the landslide. In contrast, the upper portion of the sequence is similar to a deposit attributed to the 1700 CE Cascadia subduction earthquake (two-sigma range of 1680-1780 CE): the base has a higher concentration of light-colored, watershed-sourced silt derived from the delta front followed by a long (2-5 cm) organic tail. The soft lake sediments are more likely to amplify the sustained lower frequency accelerations (<5 Hz) of subduction earthquakes, resulting in subaquatic slope failures of the delta front. The upper portion of the 1873 CE deposit, however, has an even higher concentration of watershed-sourced silt as compared to the 1700 CE deposit, which is suspected to be the result of shaking-induced liquefaction of the lake’s large subaerial delta. The tail of both the 1873 CE and 1700 CE deposits is explained as the result of flocculation that occurred during sustained shaking. A preliminary literature search suggests that flocculation may occur during low frequency (<4-5 Hz) water motion that is sustained for an extended period of time (~minutes). The subduction interpretation of the upper portion of the 1873 CE deposit is supported by the observation of a small local tsunami offshore and the presence of a possible seismogenic turbidite attributed to the 1873 CE Brookings earthquake in southern Oregon sediment cores.
These results are important to regional seismic hazards for several reasons. Southern Cascadia crustal earthquakes, not previously recognized as a threat in southern Oregon, have the potential to cause damage to infrastructure, including the Applegate dam and buildings and other structures at Oregon Caves National Monument. They also identify a previously unrecognized recent southern Cascadia subduction earthquake. Finally, the close temporal relationship between these two types of earthquakes, not observed elsewhere in the downcore record, may be early evidence of the transition of the Walker Lane belt into a transform fault as predicted.
How to cite: Morey, A. E., Shapley, M. D., Gavin, D. G., Goldfinger, C., and Nelson, A. R.: A complex deposit sequence from a small, southern Cascadia lake suggests a previously unrecognized subduction earthquake immediately followed a crustal earthquake in 1873 CE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11849, https://doi.org/10.5194/egusphere-egu25-11849, 2025.
How to cite: Motti, A.: Active and capable faults (FAC) and buildings in Norcia, interventions carried out and possibile technicolor and regulatory actions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5076, https://doi.org/10.5194/egusphere-egu25-5076, 2025.
In Greece, almost all accelerometer stations provided accelerometer recordings, more than 400 in total, are characterized by inferred Vs30 values based on combination of surface geology and slope proxy (Stewart et al. 2014). However, only about 15% of them have been characterized by in-situ geophysical and geotechnical methods (invasive or/and non-invasive) were performed at a distance less than 100m from the station. In addition, regarding reference rock stations where shear wave velocity Vs30 is equal or greater than 800m/sec (engineering bedrock), only five (5) of them have been characterized todate, with respective values ranging between 800Vs301183m/s. It is evident that measured site characterization parameters of accelerometer stations in Greece is far from a desired goal, especially regarding those on rock reference sites. In this study multiple/combined non-invasive passive and active seismic techniques are applied in six (6) accelerometer stations throughout Greece, to improve earthquake site characterization metadat of the national accelerometer network, focusing on stations placed on geologic rock conditions. The Vsz (S-wave) and Vpz (P-wave) profiles and thereby Vs30 site class according to the Eurocode-8 are determined. In addition, to form a holistic picture of the site’s characterization, surface geology and topographic properties are provided for the investigated stations. Results of this study aim at contributing on improving site characterization parameters estimated by the Generalized Inversion Technique (source, path, site), as well as in defining Ground Motion Models for rock site conditions.
How to cite: Theodoulidis, N., Hollender, F., Rischette, P., Buscetti, M., Douste-bacque, I., Grendas, I., and Roumelioti, Z.: Characterization of selected “rock” reference stations of the Hellenic Accelerometer Network (HAN), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16647, https://doi.org/10.5194/egusphere-egu25-16647, 2025.
This article mainly studies the characteristics of the earthquake sequence and the post - earthquake trend of the Ms6.4 earthquake in Yangbi, Yunnan，China on May 21, 2021. The research area is located in Yangbi Yi Autonomous County in the western part of Yunnan Province. The earthquake caused severe disasters such as housing destruction, traffic interruption, water conservancy facilities damage and power supply interruption. Through the analysis of the basic parameters of the earthquake, the tectonic stress environment and the seismogenic structure, it is determined that the earthquake is a right - lateral strike - slip rupture, with a focal depth of 8 kilometers, consistent with the direction of the Weixi - Qiaohou and Honghe fault zones. The earthquake sequence type is determined as the main shock - aftershock type (including the foreshock - main shock - aftershock type). Spatially, the source rupture expands unilaterally from the northwest to the southeast, mostly occurring in the upper crust high - speed zone or the high - low speed transition zone. Based on the G - R relationship and other analyses, the earthquake activity cycle in this area has active and quiet periods, and there are certain abnormal change laws before strong aftershocks, such as strain accumulation, calmness or enhancement of earthquakes above magnitude 3.5, and abnormal frequency of earthquakes above magnitude 2. The conclusion is that the earthquake sequence is normal, and the post - earthquake trend shows the characteristics of long - term calmness - breaking calmness - becoming calm again - signal earthquake (main shock). In the next few years, the strain accumulation may reach the peak and release. It is predicted that there may be a larger earthquake accompanied by strong aftershocks in 2025, or enter an active period with a strong aftershock magnitude exceeding 5.9 and lasting for more than half a year. Finally, the earthquake prevention and disaster reduction countermeasures are proposed.
How to cite: Wu, B.: The determination of the seismic sequence characteristics and post - earthquake trend of the Ms6.4 earthquake in Yangbi, Yunnan, China on May 21, 2021, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2519, https://doi.org/10.5194/egusphere-egu25-2519, 2025.
