Engineering Geology最新文献_第7页

Full-waveform CNN–transformer neural network for regional coseismic landslide susceptibility modeling: A case study of the 2022 Luding earthquake, China 全波形cnn -变压器神经网络区域同震滑坡易感性模拟——以2022年泸定地震为例

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-07 DOI: 10.1016/j.enggeo.2025.108520

Xiaolong Zhang , Shuai Huang , Binghai Gao

Earthquake-induced landslides are among the most common and destructive geological hazards in mountainous regions, posing significant threats to infrastructure, safety, and property. Traditional landslide susceptibility models primarily rely on simplified seismic intensity metrics, such as Peak Ground Acceleration, Velocity, or Arias intensity, which fail to capture the full time-frequency structure and duration effects of seismic motion, limiting both predictive accuracy and explainability. To address these limitations, this study proposes a novel approach for regional coseismic landslide susceptibility modeling that integrates full-waveform seismic data reconstruction with a hybrid Convolutional Neural Network (CNN)–Transformer deep learning model. The method involves a waveform reconstruction process for regions with sparse seismic data, utilizing waveform standardization, spectral decomposition, spatial interpolation, and group velocity constraints to synthesize three-component ground motion time histories with a frequency bandwidth of up to 25 Hz. A CNN-Transformer hybrid model is then employed to jointly analyze the reconstructed seismic waveforms and static environmental factors, such as topographic slope and lithology, enabling high-resolution spatial predictions of coseismic landslide susceptibility. Using the 2022 Luding earthquake as a case study, experimental results show that the integrated model significantly outperforms traditional models, achieving an AUC of 0.982 and an F1-score of 0.957, compared to 0.756 and 0.805 for the traditional model. Gradient-based explainability analysis reveals that the model focuses on the mainshock period within ±10 s of peak ground displacement (PGD) in regions with consistent predictions, while in areas with divergent predictions, it relies on tail waves, multi-phase shaking, and sustained seismic motion features, which are often missed by peak-based metrics. This study advances landslide susceptibility modeling by integrating full-waveform seismic data with static environmental factors, providing a more accurate and explainable framework for predicting coseismic landslide susceptibility. The approach offers significant potential for improving engineering applications and enabling cross-regional deployment in future seismic hazard assessments.

地震引发的山体滑坡是山区最常见和最具破坏性的地质灾害之一，对基础设施、安全和财产构成重大威胁。传统的滑坡敏感性模型主要依赖于简化的地震强度指标，如峰值地面加速度、速度或阿里亚斯强度，这些指标无法捕捉地震运动的全时频结构和持续时间效应，从而限制了预测的准确性和可解释性。为了解决这些局限性，本研究提出了一种新的区域同震滑坡易感性建模方法，该方法将全波形地震数据重建与混合卷积神经网络(CNN) -Transformer深度学习模型相结合。该方法包括对地震数据稀疏区域的波形重建过程，利用波形标准化、频谱分解、空间插值和群速度约束来合成频率带宽高达25 Hz的三分量地震动时程。然后利用CNN-Transformer混合模型，对重建的地震波形与地形坡度、岩性等静态环境因子进行联合分析，实现同震滑坡易感性的高分辨率空间预测。以2022年泸定地震为例，实验结果表明，综合模型的AUC为0.982，f1得分为0.957，显著优于传统模型，传统模型的AUC为0.756，f1得分为0.805。基于梯度的可解释性分析表明，在预测结果一致的地区，该模型主要关注地表峰值位移（PGD）±10 s内的主震周期，而在预测结果不一致的地区，该模型主要依赖于尾波、多相震动和持续地震运动特征，而这些特征往往是基于峰值的指标所忽略的。本研究通过将全波形地震数据与静态环境因素相结合，推进了滑坡易感性建模，为同震滑坡易感性预测提供了一个更准确、可解释的框架。该方法为改进工程应用和在未来地震灾害评估中实现跨区域部署提供了巨大潜力。

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引用次数: 0

From hydro-meteorological thresholds towards an operational warning model for landslides at regional scale: A real-case application 从水文气象阈值到区域滑坡的业务预警模型：一个实际应用

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-07 DOI: 10.1016/j.enggeo.2026.108542

Sen Zhang , Gaetano Pecoraro , Da Huang , Jianbing Peng , Bei Zhang , Michele Calvello

Landslide prediction is essential for developing a landslide early warning system. Recently, hydro-meteorological thresholds combining rainfall and hydrological variables have demonstrated their effectiveness in enhancing the predictive capability of landslide occurrences. However, most territorial landslide early warning systems operational worldwide are primarily developed using only rainfall thresholds, totally neglecting the hydrological process that contributes to landslide initiation. In this study, we propose a three-step procedure aimed at developing a hydro-meteorological warning model intended for operational use employing multiple hydro-meteorological thresholds derived from a probabilistic analysis, using soil saturation and precipitation data retrieved from the ERA5-Land product. The model developed herein was tested in one of the warning zones defined by civil protection for the management of geo-hydrological risk in Campania region, Italy. Performance indicators derived adopting the “event, duration matrix, performance” (EDuMaP) method highlight that the hydro-meteorological warning model developed in this study—using real-time forecasts from the Integrated Forecasting System - High-Resolution (IFS-HRES) product—outperforms the current implemented warning model, which depends exclusively on precipitation forecasts. Specifically, the inclusion of soil saturation into the warning model leads to a significant reduction of false alarms. The results achieved herein demonstrate that hydro-meteorological thresholds can be effectively employed within landslide early warning systems for real-world applications at regional scale.

滑坡预测是建立滑坡预警系统的基础。近年来，结合降雨和水文变量的水文气象阈值在提高滑坡灾害预测能力方面的有效性得到了验证。然而，世界范围内运行的大多数区域滑坡预警系统主要是利用降雨阈值开发的，完全忽视了有助于滑坡启动的水文过程。在这项研究中，我们提出了一个三步程序，旨在利用从ERA5-Land产品中检索的土壤饱和度和降水数据，利用概率分析得出的多个水文气象阈值，开发一个用于业务使用的水文气象预警模型。本文开发的模型在意大利坎帕尼亚地区的一个预警区进行了测试，该预警区是由民防部门定义的，用于管理地质水文风险。采用“事件、持续时间矩阵、性能”（EDuMaP）方法得出的绩效指标突出表明，本研究开发的水文气象预警模型——使用高分辨率综合预报系统（IFS-HRES）产品的实时预报——优于目前实施的仅依赖降水预报的预警模型。具体来说，将土壤饱和度纳入预警模型可以显著减少误报。研究结果表明，水文气象阈值可以有效地应用于区域尺度的滑坡预警系统中。

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引用次数: 0

Reconstruction and upscaling of local rock mass joint networks based on SinGAN 基于SinGAN的局部岩体节理网络重构与升级

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-07 DOI: 10.1016/j.enggeo.2026.108546

Jun Xiang , Qinjie Zhang , Xiling Liu , Xing Zhao , Tubing Yin , Zhiguo Li

Accurate identification and modeling of rock mass joint networks are crucial for assessing the quality and stability of the rock mass. However, traditional methods are often limited by sparse data and predefined statistical assumptions, making it difficult to capture the multi-scale self-similar characteristics of complex joint systems. To address this challenge, we propose a self-similarity upscaling approach for rock mass joints based on the SinGAN model. Leveraging its pyramid-like multi-scale generator, the method learns self-similar statistical features from a single joint image and enables accurate transmission of multi-scale structural information. Three field-acquired rock joint outcrop images were processed into binary images and used for model training. Model performance was evaluated based on joint intensity, orientation, and length distribution. The results show that SinGAN-generated images exhibit strong consistency with the originals, effectively preserving the variability of joint intensity, dominant orientation clusters, and the log-normal distribution of joint length. Integrating the upscaled images with a simplified GSI-based rock mass classification revealed a systematic decline in grading scores with increasing scale, consistent with the mechanical response of natural rock masses. Compared with traditional methods, the proposed approach leverages a data-driven framework to achieve unsupervised learning of the self-similarity statistical features of rock mass joint networks, significantly enhancing both the efficiency and accuracy of joint modeling in complex geological settings, and bridging the gap between laboratory-scale observations and field-scale predictions. This study highlights the potential of generative adversarial networks for quantitative multi-scale geological modeling and provides reliable data support for engineering design and geohazard risk assessment.

岩体节理网络的准确识别和建模对于评价岩体的质量和稳定性至关重要。然而，传统方法往往受到稀疏数据和预定义统计假设的限制，难以捕捉复杂关节系统的多尺度自相似特征。为了解决这一挑战，我们提出了一种基于SinGAN模型的岩体节理自相似升级方法。该方法利用其金字塔状的多尺度生成器，从单个关节图像中学习自相似的统计特征，从而实现多尺度结构信息的准确传输。将三幅野外采集的岩石节理露头图像处理成二值图像，用于模型训练。根据关节强度、方向和长度分布来评估模型的性能。结果表明，singan生成的图像与原始图像具有较强的一致性，有效地保留了关节强度、优势方向簇和关节长度的对数正态分布的可变性。将升级后的图像与基于简化gsi的岩体分类相结合，发现分级分数随着尺度的增加而系统性下降，这与天然岩体的力学响应一致。与传统方法相比，该方法利用数据驱动框架实现了岩体节理网络自相似统计特征的无监督学习，显著提高了复杂地质环境下节理建模的效率和准确性，弥合了实验室尺度观测与现场尺度预测之间的差距。该研究强调了生成对抗网络在定量多尺度地质建模中的潜力，并为工程设计和地质灾害风险评估提供了可靠的数据支持。

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引用次数: 0

Impacts of plant roots on debris-flow bed erosion in laboratory experiments 植物根系对室内泥石流床侵蚀的影响

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-06 DOI: 10.1016/j.enggeo.2025.108513

Anna J. van den Broek, Dagmar T. Mennes, Maarten G. Kleinhans, Lonneke Roelofs, Jana Eichel, Daniel Draebing, Tjalling de Haas

Debris flows often increase in size due to bed erosion and entrainment, enhancing their hazardous potential. However, the effects of plant rooting on debris-flow erosion on ubiquitous vegetated slopes remain unknown, which hinders debris-flow hazard assessment. Here, we investigated the effects of roots on debris-flow bed erosion using scaled experiments in a 5 m long, 0.3 m wide laboratory flume with an erodible bed. Roots of fast-growing Sorghum bicolor (Sudan grass) seedlings were used as proxies for tree roots to quantify the effect of varying rooting characteristics on erosion. Our results indicate that erosion decreases non-linearly with increasing Root Length Density (

R L D

) and Root Area Ratio (

R A R

). Increases in either parameter enhance root–soil contact, thereby improving soil stability and reducing erosion. Among the two,

R L D

, and thus the combined effect of root length and root density, appears most influential, as

R A R

does not capture the three-dimensional structure of the root system. Our experimental results suggest that increasing root-soil contact at the debris-flow bed reduces erosion, decreasing or even preventing debris-flow volume growth. These findings imply that alterations in vegetation characteristics, such as those resulting from forest fires or reforestation, affect debris-flow erosion and open up possibilities for biogeomorphic scale experiments for slope processes.

由于河床侵蚀和夹带，泥石流的规模往往会增大，从而增加了其潜在的危险性。然而，在普遍存在的植被斜坡上，植物根系对泥石流侵蚀的影响尚不清楚，这阻碍了泥石流危害评估。在这里，我们研究了根对泥石流河床侵蚀的影响，在一个长5米、宽0.3米的实验室水槽中进行了规模实验，并带有可侵蚀的河床。以速生高粱（苏丹草）幼苗根系为代表，量化不同根系特征对侵蚀的影响。结果表明：土壤侵蚀随根长密度（RLD）和根面积比（RAR）的增加呈非线性减少；任何一个参数的增加都能增强根与土壤的接触，从而提高土壤稳定性，减少侵蚀。在这两者中，RLD以及根长度和根密度的综合效应似乎影响最大，因为RAR不能捕捉根系的三维结构。我们的实验结果表明，增加泥石流床根部与土壤的接触可以减少侵蚀，减少甚至阻止泥石流体积的增长。这些发现表明，植被特征的改变，如森林火灾或重新造林所造成的变化，会影响泥石流侵蚀，并为斜坡过程的生物地貌尺度实验开辟了可能性。

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引用次数: 0

Regional-scale inventory and initial analysis of liquefaction triggered by the 2025 Mw 7.7 Mandalay earthquake, Myanmar 2025年缅甸曼德勒7.7兆瓦地震引发的区域尺度液化的库存和初步分析

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-05 DOI: 10.1016/j.enggeo.2026.108543

Sotiris Valkaniotis , George Papathanassiou , Janusz Wasowski , Maria Taftsoglou , Ranjan Kumar Dahal

On March 28, 2025, a Mw 7.7 earthquake struck central Myanmar, rupturing a ∼ 500 km segment of the strike-slip Sagaing Fault. The earthquake produced widespread structural damage and co-seismic ground failures, including extensive liquefaction and lateral spreading. Although field observations and remote assessments have reported numerous liquefaction occurrences, no comprehensive regional-scale inventory was produced. This study presents the first systematic mapping and analysis of earthquake-induced liquefaction associated with the 2025 Mandalay event, using medium resolution (10 m) Copernicus Sentinel-2 satellite imagery acquired in the immediate aftermath of the earthquake.

We identified over 18,000 liquefaction sites within an area of more than 80,000 km², with the highest concentrations along the Irrawaddy and Sittang River valleys, in vicinity to the fault rupture. Liquefaction predominantly occurred in Holocene fluvial environments, including meandering channels, floodplains, and abandoned paleochannels, reflecting the influence of geomorphology and sediment characteristics. Over 95 % of the sites were located within 20 km of the fault rupture, confirming that rupture proximity is a more reliable predictor of liquefaction hazard than epicentral distance alone. This is consistent with the very large length of the ruptured fault, off-center location of the mainshock epicenter and the significant (∼3 m on average) surface slip documented throughout much of the rupture.

The scale of liquefaction observed, particularly the extensive lateral spreading and ground deformation along the Irrawaddy River near Mandalay, indicate that this event may represent one of the largest regional liquefaction occurrences during the last few decades. Our results demonstrate the importance of integrating geomorphic, lithological, and seismic parameters into regional-scale assessments of seismic hazard. The liquefaction inventory offers critical insights for post-earthquake risk evaluation in Myanmar and for mitigating future co-lateral seismic hazards in tectonically active fluvial regions.

2025年3月28日，缅甸中部发生7.7级地震，导致实皆断层走滑断裂约500公里。地震造成了广泛的结构破坏和同震地面破坏，包括广泛的液化和横向蔓延。虽然实地观察和远程评估报告了许多液化事件，但没有编制全面的区域范围清单。本研究首次系统地绘制和分析了与2025年曼德勒事件相关的地震诱发液化，使用了在地震发生后立即获得的中分辨率（10米）哥白尼哨兵-2卫星图像。我们在超过8万平方公里的区域内确定了超过1.8万个液化点，在断层破裂附近的伊洛瓦底江（Irrawaddy）和锡塘河谷（Sittang River）一带，液化点的浓度最高。液化主要发生在全新世河流环境中，包括曲流河道、洪泛平原和废弃的古河道，反映了地貌和沉积物特征的影响。超过95%的地点位于断层破裂的20公里范围内，这证实了破裂距离是比单独的震中距离更可靠的液化危险预测指标。这与破裂断层的非常大的长度、主震震中的偏离中心位置以及在大部分破裂中记录的显著（平均约3米）地表滑动相一致。观测到的液化规模，特别是曼德勒附近伊洛瓦底江沿岸广泛的横向扩展和地面变形，表明这次事件可能是过去几十年来最大的区域性液化事件之一。我们的研究结果表明，将地貌、岩性和地震参数整合到区域地震危险性评估中的重要性。液化清单为缅甸地震后风险评估和减轻构造活跃河流地区未来的同侧地震灾害提供了重要见解。

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引用次数: 0

Bio-geotechnical reinforcement of purple soil slopes: The synergistic effects of xanthan gum biopolymer and planting density 紫色土坡的生物土工加固：黄原胶生物聚合物与种植密度的协同效应

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-03 DOI: 10.1016/j.enggeo.2025.108540

Zhongkai Liu , Qian Peng , Qi Yang , Huafeng Deng , Yao Xiao , Daxiang Liu , Yueshu Yang

Bio-geotechnical engineering in the Three Gorges Reservoir Area (TGRA) faces prominent stability challenges during the vegetation establishment period. Xanthan gum (XG) demonstrates potential for enhancing early-stage stability of root-soil composites, but the interdependent effects between XG content and planting density remain unclear. This study systematically investigates the synergistic effects between XG content (0 %, 0.6 % and 1.2 % by dry soil mass) and Cynodon dactylon planting density (18 and 36 g/m²) on purple soil stability through multi-mode tests. Results demonstrate that increased XG content significantly enhances soil cohesion and aggregate stability, while the 1.2 % XG content markedly inhibits plant germination and growth. Notably, mechanistic analysis reveals that under the combined effects of evaporation and soil layer thickness, high XG content induces surface cementation through upward capillary migration of XG molecules and soil cations, followed by crystallization and cross-linking upon dehydration. This process promotes the formation of a white cementation layer, which subsequently leads to preferential cracking, and seeds are consequently forced to germinate from within these cracks. Furthermore, in thicker soil layers, high XG content contributes to prolonged moisture retention and induces localized anaerobic conditions. This anaerobic environment enhances the activity of anaerobic microorganisms, leading to the formation of black metal sulfide deposits. The higher planting density (36 g/m²) can mitigate these effects by improving soil aeration and drainage through root development. Finally, Entropy-weighted TOPSIS evaluation identifies 0.6 % XG with 36 g/m² planting density as the recommended combination, effectively balancing immediate soil reinforcement with sustainable vegetation establishment. Compared to untreated purple soil, this optimized treatment achieves a 95.67 % increase in disintegration resistance index, a 196.64 % increase in cohesion, and a 31.09 % reduction in surface crack ratio. The findings could provide theoretical guidance for bio-geotechnical engineering design in TGRA and similar regions, offering references for biopolymer-vegetation interaction studies.

三峡库区生物岩土工程在植被建立期面临着突出的稳定性挑战。黄原胶（XG）具有提高根土复合材料早期稳定性的潜力，但XG含量与种植密度之间的相互作用尚不清楚。本研究通过多模式试验系统研究了干土XG含量（0、0.6%和1.2%）和长尾草种植密度（18和36 g/m2）对紫色土稳定性的协同效应。结果表明，增加XG含量可显著提高土壤黏聚力和团聚体稳定性，而1.2%的XG含量可显著抑制植物的萌发和生长。值得注意的是，机理分析表明，在蒸发和土层厚度的共同作用下，高XG含量通过XG分子和土壤阳离子向上的毛细迁移诱导表面胶结，脱水后发生结晶和交联。这个过程促进了白色胶结层的形成，随后导致优先开裂，种子因此被迫从这些裂缝中发芽。此外，在较厚的土层中，高XG含量有助于延长水分保持时间并诱导局部厌氧条件。这种厌氧环境增强了厌氧微生物的活性，导致黑色金属硫化物沉积物的形成。较高的种植密度（36 g/m2）可以通过根系发育改善土壤的通气和排水，从而缓解这些影响。最后，熵权TOPSIS评价确定了0.6% XG和36 g/m2种植密度作为推荐组合，有效地平衡了立即土壤加固和可持续植被的建立。与未处理紫土相比，优化处理后的紫土抗崩解指数提高了95.67%，黏聚力提高了196.64%，表面裂缝率降低了31.09%。研究结果可为TGRA及类似地区的生物岩土工程设计提供理论指导，为生物聚合物与植被相互作用的研究提供参考。

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引用次数: 0

Seismic site characterization using satellite-derived terrain morphometry and geological data: A machine learning approach for predominant frequency prediction 利用卫星衍生的地形形态测量学和地质数据进行地震现场表征：一种用于主要频率预测的机器学习方法

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-02 DOI: 10.1016/j.enggeo.2025.108541

Harish Thakur, P. Anbazhagan

<div><div>Predominant frequency (<em>fo</em>) characterization across large seismically active regions remains challenging due to limited field measurements and cost constraints. Existing <em>fo</em> mapping approaches rely exclusively on spatial interpolation methods (kriging, inverse distance weighting, natural neighbor) that redistribute measured values without incorporating terrain morphometry, geological context, or subsurface parameters as predictors. This study develops a DEM-based machine learning methodology for regional-scale <em>fo</em> prediction in the Himalayan region and Indo-Gangetic Plains, addressing critical data scarcity in earthquake-prone developing countries. We compiled 4400 <em>fo</em> measurements from 26 published HVSR studies using systematic georeferencing procedures to ensure spatial consistency. The methodology employs a two-stage regression kriging framework: (1) stacked ensemble machine learning models trained on 20 predictor variables using GLO-30 DEM morphometric parameters (elevation, slope, curvature indices), geological classifications, and bedrock depth information to capture nonlinear terrain-frequency relationships; and (2) ordinary kriging of model residuals to account for spatial correlation patterns. Cross-validation partitioning ensures unbiased residuals, while Bayesian optimization determines optimal hyperparameters for base model selection. Feature importance analysis reveals that valley bottom identification (MRVBF), geological formation characteristics, and bedrock depth provide primary predictive capability (Shapley values ∼0.15–0.18), demonstrating that terrain morphometry and subsurface parameters effectively control <em>fo</em> variation at regional scales. The stacked ensemble achieves R<sup>2</sup> = 0.516 and RMSE = 0.634 log units, with variogram analysis revealing spatial correlation extending 7.3 km and structured variance accounting for 52 % of model residuals. High-resolution <em>fo</em> maps (50 m grid) generated for Delhi, Kathmandu, and Dhaka differentiate site response zones: low frequencies (<1.0 Hz) in deep sedimentary basins versus high frequencies (>3.0 Hz) in bedrock-controlled areas.</div><div>This work represents the first regional-scale application of DEM-derived terrain morphometry for direct <em>fo</em> prediction, utilizing a much larger compiled dataset for this purpose than previous basin-scale studies. Unlike previous studies that employed purely interpolation techniques without predictive parameters, this hybrid framework integrates physical predictors (terrain morphometry, geology, bedrock depth) with spatial modelling to produce more robust <em>fo</em> maps. Results demonstrate that incorporating satellite-derived morphometric and geological parameters—readily available globally—significantly enhances prediction reliability beyond interpolation-only approaches. This cost-effective methodology enables preliminary seismic hazard assessment in data-sparse mounta

由于有限的现场测量和成本限制，大型地震活跃区域的主要频率（fo）表征仍然具有挑战性。现有的测绘方法完全依赖于空间插值方法（克里格法、逆距离加权法、自然邻域法），这些方法重新分配测量值，而没有将地形形态、地质背景或地下参数作为预测因素。本研究开发了一种基于dem的机器学习方法，用于喜马拉雅地区和印度恒河平原的区域尺度预测，解决了地震多发发展中国家的关键数据短缺问题。我们使用系统的地理参考程序，从26项已发表的HVSR研究中收集了4400个测量值，以确保空间一致性。该方法采用两阶段回归克里格框架：(1)利用gloo -30 DEM形态参数（高程、坡度、曲率指数）、地质分类和基岩深度信息，训练20个预测变量的堆叠集成机器学习模型，捕捉非线性地形-频率关系；(2)对模型残差进行普通克里格，以解释空间相关模式。交叉验证分区确保残差无偏，贝叶斯优化确定最优超参数，用于基础模型选择。特征重要性分析表明，谷底识别（MRVBF）、地质构造特征和基岩深度提供了主要的预测能力（Shapley值~0.15 ~ 0.18），表明地形形态和地下参数在区域尺度上有效控制了变化。叠加集合的R2 = 0.516,RMSE = 0.634 log units，方差分析显示空间相关延伸7.3 km，结构方差占模型残差的52. %。为德里、加德满都和达卡制作的高分辨率地图（50 m网格）区分了场地响应区域：深沉积盆地的低频（<1.0 Hz）与基岩控制区的高频（>3.0 Hz）。

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引用次数: 0

Forecasting CO2 injection-induced fault reactivation: A hybrid approach and its application to the Illinois Basin–Decatur Project 预测二氧化碳注入引起的断层再活化：一种混合方法及其在伊利诺斯盆地-迪凯特项目中的应用

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-02 DOI: 10.1016/j.enggeo.2025.108536

Yao Zhang , Qi Li , Jianhua Zhang , Haodong Cui , Yiyan Zhong , Yongsheng Tan , Meng Jing , Xiaying Li

Geological CO₂ storage (GCS) is an effective method for reducing carbon emissions. However, as more such projects are deployed in the future, the associated risks of injection-induced fault reactivation require comprehensive assessment to ensure long-term and effective CO₂ storage. This study presents an integrated assessment of the Illinois Basin–Decatur Project (IBDP) through a hybrid approach combining physics-based modeling and probabilistic forecasting. A coupled CO₂-geomechanical model is developed to simulate CO₂ injection at CCS1 well. The fault reactivation risks have been systematically evaluated by analyzing near-well and far-field fault slip tendency indices, spatiotemporal evolution of Coulomb failure stress (CFS), seismogenic index (Σ), and magnitude probability distributions. Results demonstrate that while permeability-controlled pore pressure diffusion dominates fault reactivation for both near-well and far-well faults, poroelastic stresses may induce localized fault slip and provide stabilization during shut-in periods. The reactivation state is significantly controlled by fault geometry. Higher initial injection rates substantially facilitate fault instability compared to constant or gradually increasing injection schemes. Based on field data, the applicability of the seismogenic index for carbon storage sites has been validated. The low seismogenic index (Σ ≈ −4) for this site confirms limited seismic potential, and the probability of seismic magnitude below 2.27 exceeds 50%. Probabilistic modeling further indicates that a controlled injection rate ramp-up preferentially induces seismicity with low magnitudes. The proposed hybrid forecasting approach enables a more comprehensive evaluation of fault reactivation risks at carbon storage sites.

地质CO2封存是减少碳排放的有效方法。然而，随着未来更多此类项目的部署，需要对注入引起的断层重新激活的相关风险进行全面评估，以确保长期有效的二氧化碳储存。本研究通过结合物理建模和概率预测的混合方法，对伊利诺斯盆地-迪凯特项目（IBDP）进行了综合评估。建立了模拟CCS1井CO2注入过程的CO2-地质力学耦合模型。通过分析近井和远场断层滑动趋势指数、库仑破坏应力（CFS）时空演化、发震指数（Σ）和震级概率分布，系统评价了断层再激活风险。结果表明，虽然渗透率控制的孔隙压力扩散主导着近井和远井断层的恢复，但孔隙弹性应力可能会导致局部断层滑动，并在关井期间提供稳定。再激活状态在很大程度上受断层几何形状的控制。与恒定或逐渐增加的注入方案相比，较高的初始注入速率大大促进了断层的不稳定性。根据实测资料，验证了孕震指数在碳库选址上的适用性。本区孕震指数（Σ≈−4）较低，地震潜力有限，发生2.27级以下地震的概率超过50%。概率模型进一步表明，控制注入速度的增加优先诱发低震级的地震活动。提出的混合预测方法能够更全面地评估碳储存地点的断层再激活风险。

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引用次数: 0

Earthquake-triggered landslide susceptibility modeling based on fault geometry 基于断层几何的地震诱发滑坡易感性建模

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-02 DOI: 10.1016/j.enggeo.2025.108539

Yigen Qin, Dongli Zhang, Wenjun Zheng, Xinyuan Chen, Xin Sun, Zhikang Gong

The geometric configurations and kinematic behavior of seismogenic faults fundamentally govern the spatial distribution of earthquake-triggered landslides, where quantifying their mechanisms dominating occurrence probability poses a core challenge for advancing predictive accuracy. Focusing on the 2008 Wenchuan earthquake and its main rupture zone (Beichuan–Yingxiu Fault), a thrust–strike–slip structure exhibiting along-strike dip variations ranging from ∼43° in the south to near-vertical in the north, this study establishes a probabilistic framework integrating fault geometry with five controlling factors, including distance to fault, peak ground acceleration (PGA), slope, local relief, and lithology, using a comprehensive EQTL inventory. Using multivariate nonlinear regression (MNR) and random forest (RF) modeling, we elucidate the fault dip's regulatory role in earthquake-triggered mechanisms. Key findings indicate that low-dip faults (43°–53°) drive concentrated hanging-wall landslides, characterized by PGA saturation > 0.5 g, high sensitivity to slopes >30°, and abrupt acceleration beyond 500 m relief; conversely, high-dip faults (59°–89°) exhibit bilaterally symmetric distributions with PGA triggering at 0.2 g, pronounced slope sensitivity >20°, and peak probability at 500–1000 m relief followed by post-peak decline. Factor importance analysis confirms distance to fault and PGA as primary predictors; however, their influence is dynamically modulated by dip angle. The RF model outperforms MNR (AUC >0.90 versus MNR's high-dip AUC = 0.728). Blind testing with the 2013 Lushan earthquake (thrust-type) and 2017 Jiuzhaigou earthquake (strike-slip) confirms the model captures hanging-wall concentration and bilateral symmetric distributions, demonstrating cross-fault adaptability.

发震断层的几何形态和运动行为从根本上控制着地震诱发滑坡的空间分布，而对其机制进行量化控制是提高预测精度的核心挑战。以2008年汶川地震及其主断裂带（北川-映秀断裂）为研究对象，建立了断层几何形状与断层距离、峰值地面加速度（PGA）、坡度、局部起伏和岩性等5个控制因素相结合的概率框架。北川-映秀断裂是一个逆冲-走滑构造，其沿走向倾角变化范围从南部的~ 43°到北部的接近垂直。利用多元非线性回归（MNR）和随机森林（RF）模型，阐明了断层倾角在地震触发机制中的调节作用。主要研究结果表明：低倾断层（43°~ 53°）驱动集中上盘滑坡，其特征为PGA饱和度0.5 g，对坡度30°高度敏感，起伏大于500 m时陡增；相反，高倾断层（59°-89°）呈现双边对称分布，PGA在0.2 g触发，明显的坡度敏感性>；20°，峰值概率在500-1000 m起伏处，然后是峰后下降。因子重要性分析证实断层距离和PGA是主要预测因子；然而，它们的影响是由倾角动态调制的。RF模型优于MNR （AUC >0.90，而MNR的高倾角AUC = 0.728）。利用2013年芦山地震（逆冲型）和2017年九寨沟地震（走滑型）进行盲测，证实该模型捕捉到了上盘集中和两侧对称分布，具有跨断层适应性。

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引用次数: 0

Distribution-free estimation for three-dimensional diameter of rock discontinuities within complex high-steep slope based on Bertrand paradox 基于Bertrand悖论的复杂高陡边坡岩体结构面三维直径无分布估计

IF 8.4 1区工程技术 Q1 ENGINEERING, GEOLOGICAL

Engineering Geology

Pub Date : 2026-01-02 DOI: 10.1016/j.enggeo.2025.108538

Sicong Wang , Shengyuan Song , Mingyu Zhao , Ziyue Xu , Yaoyao Jiang , Muye Ma , Haojie Li

Traditional diameter estimation methods rely on the assumption that the midpoint of trace is uniformly distributed along the diameter of discontinuities and require sampling planes to share the same strike. This limitation renders them inapplicable to high-steep rock slopes with complex topography and undulating slope surfaces. In this situation, accurately estimating the average diameter of discontinuities has become a critical issue that requires urgent resolution. This study proposes a novel hypothesis based on Bertrand paradox that the midpoint of traces is uniformly distributed within the discontinuity for the first time. A new implicit probability function model for two-dimensional trace length and three-dimensional diameter was reconstructed, and a discretized numerical method was used to estimate the numerical solution of average diameter. A distribution-free accurate correction method for the average diameter of discontinuities in complex high-steep slopes has been proposed for the first time. The new method effectively suppresses size bias while eliminating orientation bias. Extensive validation through 162 sets of simulated data confirms the robustness of the new method, with mean relative errors consistently maintain below 15 %. Systematically analyzed the impact of four key discontinuity parameters (dip direction, dip angle, diameter size, and diameter distribution type) on estimation errors and proposed selecting principles for estimation parameters. Furthermore, the mapping relationship of distribution between diameter and intersection trace length is further revealed. Finally, the new method was applied to a high-steep slope with a height difference of 959 m. The consistency between the estimated and actual survey results indicates the reliability of the new method in complex high-steep slope engineering. The research outcomes break through the technical bottleneck in accurate characterization of rock mass discontinuity sizes, provide important references for evaluating the shape parameters of non-circular discontinuities, and hold significant implications for stability evaluation of high-steep rock slopes in hard mountain areas.

传统的直径估计方法依赖于迹线中点沿不连续面直径均匀分布的假设，并要求采样平面共用同一走向。这种局限性使其不适用于地形复杂、坡面起伏的高陡岩质边坡。在这种情况下，准确估计不连续面平均直径已成为一个迫切需要解决的关键问题。本文首次提出了基于Bertrand悖论的轨迹中点均匀分布于不连续区内的新假设。重建了二维轨迹长度和三维轨迹直径的隐式概率函数模型，并采用离散化数值方法估计了平均直径的数值解。首次提出了复杂高陡坡面结构面平均直径的无分布精确校正方法。新方法有效地抑制了尺寸偏差，同时消除了取向偏差。通过162组模拟数据的广泛验证证实了新方法的鲁棒性，平均相对误差始终保持在15%以下。系统分析了倾角、倾角、直径大小、直径分布类型等4个关键不连续参数对估计误差的影响，提出了估计参数的选择原则。进一步揭示了直径与交点轨迹长度分布的映射关系。最后，将该方法应用于高差为959 m的高陡边坡。结果表明，该方法在复杂高陡边坡工程中的应用是可靠的。研究成果突破了岩体结构面尺寸精确表征的技术瓶颈，为非圆形结构面形态参数评价提供了重要参考，对硬山区高陡岩质边坡稳定性评价具有重要意义。

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引用次数: 0