Geophysics

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Showing new listings for Tuesday, 30 September 2025

New submissions (showing 4 of 4 entries )

[1] arXiv:2509.24121 [cn-pdf, pdf, other]

Title: Progressive Layer Stripping Analysis for HVSR Interpretation Show Chinese title

Title: 用于HVSR解释的渐进层剥离分析

Mersad Fathizadeh, Clinton M. Wood, Hosna Kianfar

Comments: 15 pages, 6 figures, includes Python code (hvstrip-progressive) available at GitHub: https://github.com/mersadfathizadeh1995/hvstrip-progressive

Subjects: Geophysics (physics.geo-ph)

The horizontal-to-vertical spectral ratio (HVSR) technique is widely used to determine site fundamental periods from ambient noise recordings, but relating the observed peak to a specific impedance contrast within layered soils remains challenging. This paper presents an enhanced implementation of hvstrip-progressive, a Python package for forward HVSR modelling under the diffuse-field assumption and systematic progressive layer stripping. The package computes theoretical HVSR curves from shear-wave velocity (Vs) profiles, iteratively removes the deepest finite layer and promotes the next layer to half-space, and tracks how the fundamental frequency and amplitude change with each step. Compared with previous implementations, the software now supports adaptive frequency scanning, rigorous model validation, and publication-quality visualizations. Using a synthetic seven-layer soil profile, we show that the fundamental peak shifts from 6.99 Hz to 23.45 Hz as layers are stripped and that the maximum impedance contrast of 1.46 at 17 m depth controls the resonance. The transparent workflow, reproducible outputs and open-source distribution make hvstrip-progressive a practical tool for seismic site characterization and microzonation studies. Show Chinese abstract

水平-垂直频谱比（HVSR）技术被广泛用于从环境噪声记录中确定场地基本周期，但将观察到的峰值与分层土壤中的特定阻抗对比联系起来仍然具有挑战性。 本文介绍了一种增强的hvstrip-progressive实现，这是一个用于在扩散场假设下进行正向HVSR建模和系统渐进层剥离的Python包。 该软件包从剪切波速度（Vs）剖面计算理论HVSR曲线，迭代地移除最深的有限层，并将下一层提升为半空间，并跟踪基本频率和振幅随每一步的变化。 与之前的实现相比，该软件现在支持自适应频率扫描、严格的模型验证和高质量的可视化输出。 使用一个合成的七层土壤剖面，我们展示了当逐层剥离时，基本峰值从6.99赫兹移动到23.45赫兹，并且17米深度处的最大阻抗对比1.46控制了共振。 透明的工作流程、可重复的输出和开源分发使hvstrip-progressive成为地震场地特征分析和微区划研究的实用工具。

[2] arXiv:2509.24813 [cn-pdf, pdf, other]

Title: Submarine Cable Deep-Ocean Observation of Mega-Thrust Earthquake and Tsunami with 44,000 100-m Spaced Sensors Show Chinese title

Title: 海底光缆深海观测大型逆冲地震和海啸的44,000个100米间距传感器

Mikael Mazur, Nicolas K. Fontaine, Roland Ryf, Martin Karrenbach, Keith L. McLaughlin, Berry J. Sperry, Anuar G. Butler, Valey Kamalov, Lauren Dallachiesa, Ellsworth Burrows, David Winter, Haoshuo Chen, Jeewan Naik, Kishore Padmaraju, Ajay Mistry, David T. Neilson

Comments: 3 pages, 2 figures. ECOC2025 Post-deadline paper Th.03.02.5

Journal-ref: European Conference on Optical Commuinications 2025

Subjects: Geophysics (physics.geo-ph)

We detect the recent M8.8 mega-earthquake in Eastern Russia, on a 4400km long active telecom cable in the Pacific Ocean. The resolution achieved 100m represents the highest spatial resolution, the largest number of ocean-bottom sensors, and the first fiber-optic deep-ocean observation of a tsunami wave. Show Chinese abstract

我们检测到俄罗斯东部最近发生的M8.8大地震，在太平洋一条4400公里长的活动电信光缆上。 达到100米的分辨率代表了最高的空间分辨率，最多的海底传感器数量，以及首次对海啸波的光纤深海观测。

[3] arXiv:2509.24872 [cn-pdf, pdf, other]

Title: U-SWIFT: A Unified Surface Wave Inversion Framework with Transformer via Normalization of Dispersion Curves Show Chinese title

Title: U-SWIFT：通过色散曲线归一化的一体化表面波反演框架与Transformer

Tianjian Cheng, Hongrui Xu, Jiayu Feng, Xiongyu Hu, Chaofan Yao

Comments: 27 pages, 10 figures, 4 tables. Under review at a peer-reviewed journal

Subjects: Geophysics (physics.geo-ph)

Deep learning is an increasingly popular approach for inverting surface wave dispersion curves to obtain Vs profiles. However, its generalizability is constrained by the depth and velocity scales of training data. We propose a unified deep learning framework that overcomes this limitation via normalization of dispersion curves. By leveraging the scaling properties of dispersion curves, our approach enables a single, pre-trained model to predict Vs profiles across diverse scales, from shallow subsurface (e.g., < 10 m depth) to crustal levels. The framework incorporates a novel transformer-based model to handle variable-length dispersion curves and removes tedious manual parameterization. Results from synthetic and field data demonstrate that it delivers rapid and robust inversions with uncertainty estimates. This work provides an efficient inversion approach applicable to a wide spectrum of applications, from near-surface engineering to crustal imaging. The framework establishes a paradigm for developing scale-invariant deep learning models in geophysical inversion. Show Chinese abstract

深度学习是一种越来越流行的方法，用于反演表面波频散曲线以获得Vs剖面。 然而，其泛化能力受到训练数据的深度和速度尺度的限制。 我们提出了一种统一的深度学习框架，通过频散曲线的归一化来克服这一限制。 通过利用频散曲线的缩放特性，我们的方法使一个预训练模型能够预测不同尺度的Vs剖面，从浅层地表（例如，< 10 米深度）到地壳层次。 该框架结合了一个基于变压器的新模型，以处理可变长度的频散曲线，并消除了繁琐的手动参数化。 合成和实地数据的结果表明，它能够快速且稳健地进行反演，并提供不确定性估计。 这项工作提供了一种适用于广泛应用的高效反演方法，从近地表工程到地壳成像。 该框架为在地球物理反演中开发尺度不变的深度学习模型建立了一个范例。

[4] arXiv:2509.25096 [cn-pdf, pdf, html, other]

Title: Estimating high-resolution albedo for urban applications Show Chinese title

Title: 估计城市应用的高分辨率反照率

David Fork, Elizabeth Jane Wesley, Salil Banerjee, Vishal Batchu, Aniruddh Chennapragada, Kevin Crossan, Bryce Cronkite-Ratcliff, Ellie Delich, Tristan Goulden, Mansi Kansal, Jonas Kemp, Eric Mackres, Yael Mayer, Becca Milman, John C. Platt, Shruthi Prabhakara, Gautam Prasad, Shravya Shetty, Charlotte Stanton, Wayne Sun, Lucy R. Hutyra

Subjects: Geophysics (physics.geo-ph)

Implementation of cool roofs is a high-impact pathway for mitigating heat at both global and city scales. However, while albedo estimates derived from Sentinel-2 are free and globally-available, the 10 m resolution is insufficient to resolve individual roofs. We present methods for increasing the resolution of Sentinel-2 albedo using high-resolution satellite imagery to produce albedo inferences at a 30-cm scale. Validating against high-resolution aerial albedo measurements over Boulder, CO we find improved precision and accuracy relative to Sentinel-2 with an RMSE of 0.04. Applying these methods to 12 global cities, we evaluate the impacts of three cool roof implementation scenarios. We find that cities can see up to a 0.5{\deg}C cooling effect from full scale implementation of cool roofs and prioritizing the largest buildings for implementation is a highly effective policy pathway. While Sentinel-2 produces accurate estimates of albedo change at larger scales, high-resolution inferences are required for prioritizing buildings based on their solar radiation management potential. This research demonstrates a scalable implementation of targeted cool roof interventions in neighborhoods with the greatest potential for heat mitigation by enabling actionable, building-level insights. Show Chinese abstract

在全球和城市尺度上，实施冷却屋顶是减轻热量的高影响力途径。 然而，尽管来自Sentinel-2的反照率估计是免费且全球可用的，但10米的分辨率不足以分辨单个屋顶。 我们提出了使用高分辨率卫星图像提高Sentinel-2反照率分辨率的方法，以在30厘米尺度上生成反照率推断。 在科罗拉多州博尔德市的高分辨率航空反照率测量值进行验证，我们发现相对于Sentinel-2，精度和准确性有所提高，均方根误差为0.04。 将这些方法应用于12个全球城市，我们评估了三种冷却屋顶实施场景的影响。 我们发现，城市可以通过全面实施冷却屋顶看到高达0.5{\deg }摄氏度的降温效果，优先实施最大建筑是一种非常有效的政策途径。 虽然Sentinel-2在较大尺度上能够准确估计反照率变化，但为了根据其太阳能辐射管理潜力对建筑进行优先排序，需要高分辨率的推断。 这项研究展示了在具有最大热量缓解潜力的社区中可扩展的目标冷却屋顶干预措施的实施，通过提供可操作的建筑级洞察来实现这一点。

Cross submissions (showing 2 of 2 entries )

[5] arXiv:2509.23546 (cross-list from physics.data-an) [cn-pdf, pdf, html, other]

Title: Generalizations of Langbein's Formula under Non-Stationarity, Mixed Populations, and Over- or Under-Dispersion in the Number of Exceedances Show Chinese title

Title: 非平稳性、混合种群以及超出次数的过度或不足离散情况下的Langbein公式推广

Francesco Dell'Aira, Antonino Cancelliere, Claudio I. Meier

Comments: Prepared for submission to Stochastic Environmental Research and Risk Assessment (Springer). 27 pages, 2 Figures

Subjects: Data Analysis, Statistics and Probability (physics.data-an) ; Geophysics (physics.geo-ph)

Since its publication in 1949, Langbein's formula has been applied ubiquitously in both research documents and national guidelines concerning frequency analyses (FAs) of hydrologic extremes. Given a time series of independent peak-over-threshold (POT) events and the corresponding annual maxima (AM) series-defined as the subset of extremes representing the largest event in each year-the formula provides a theoretical relationship between the return period T derived from the AM series and the average recurrence interval ARI from the POT series, for any fixed event magnitude. Despite the minimal assumptions required-specifically, that exceedance counts follow a homogeneous Poisson process-there are real-world situations where the validity of the formula may be compromised. Typical cases include non-stationary processes, mixed-event populations, and over- or under-dispersion in exceedance counts. In this work, we extend Langbein's formula to account for these three cases. We demonstrate that, with appropriate adaptations to the definitions of T and ARI, the traditional functional form of Langbein's relationship remains valid for non-stationary processes and mixed populations. However, accounting for dispersion effects in exceedance counts requires a generalization of Langbein's relationship, of which the traditional version represents a limiting case. Show Chinese abstract

自1949年发表以来，Langbein公式已被广泛应用于研究文献和关于水文极端事件频率分析（FAs）的国家指南中。 给定一个独立的超过阈值（POT）事件的时间序列以及相应的年最大值（AM）序列——定义为代表每年最大事件的极端值子集——该公式提供了从AM序列得出的重现期T与从POT序列得出的平均重现间隔ARI之间的理论关系，对于任何固定的事件强度。 尽管所需的假设最少——即超过次数遵循同质泊松过程——但在某些现实情况中，公式的有效性可能会受到损害。 典型的例子包括非平稳过程、混合事件群体以及超过次数的过度或不足分散。 在本研究中，我们将Langbein公式扩展以考虑这三种情况。 我们证明，通过适当调整T和ARI的定义，Langbein关系的传统函数形式对于非平稳过程和混合群体仍然有效。 然而，考虑超过次数中的分散效应需要对Langbein关系进行推广，传统版本是其中的一个极限情况。

[6] arXiv:2509.24788 (cross-list from cs.LG) [cn-pdf, pdf, html, other]

Title: Assessing the risk of future Dunkelflaute events for Germany using generative deep learning Show Chinese title

Title: 使用生成式深度学习评估德国未来黑暗时刻事件的风险

Felix Strnad, Jonathan Schmidt, Fabian Mockert, Philipp Hennig, Nicole Ludwig

Subjects: Machine Learning (cs.LG) ; Geophysics (physics.geo-ph)

The European electricity power grid is transitioning towards renewable energy sources, characterized by an increasing share of off- and onshore wind and solar power. However, the weather dependency of these energy sources poses a challenge to grid stability, with so-called Dunkelflaute events -- periods of low wind and solar power generation -- being of particular concern due to their potential to cause electricity supply shortages. In this study, we investigate the impact of these events on the German electricity production in the years and decades to come. For this purpose, we adapt a recently developed generative deep learning framework to downscale climate simulations from the CMIP6 ensemble. We first compare their statistics to the historical record taken from ERA5 data. Next, we use these downscaled simulations to assess plausible future occurrences of Dunkelflaute events in Germany under the optimistic low (SSP2-4.5) and high (SSP5-8.5) emission scenarios. Our analysis indicates that both the frequency and duration of Dunkelflaute events in Germany in the ensemble mean are projected to remain largely unchanged compared to the historical period. This suggests that, under the considered climate scenarios, the associated risk is expected to remain stable throughout the century. Show Chinese abstract

欧洲电力电网正在向可再生能源转型，其特点是海上和陆上风电以及太阳能发电的比例不断增加。 然而，这些能源的天气依赖性对电网稳定性构成了挑战，尤其是所谓的黑暗风暴事件——低风力和太阳能发电时期——由于可能导致电力供应短缺而特别令人关注。 在本研究中，我们探讨了这些事件对未来几年和几十年德国电力生产的影响。 为此，我们将一种最近开发的生成深度学习框架适应于从CMIP6集合中降尺度气候模拟。 我们首先将其统计结果与来自ERA5数据的历史记录进行比较。 接下来，我们使用这些降尺度的模拟来评估在乐观低排放（SSP2-4.5）和高排放（SSP5-8.5）情景下，德国未来可能出现的黑暗风暴事件的合理性。 我们的分析表明，与历史时期相比，集合平均情况下德国黑暗风暴事件的频率和持续时间预计基本保持不变。 这表明，在所考虑的气候情景下，相关风险在整个世纪内预计将保持稳定。

Replacement submissions (showing 3 of 3 entries )

[7] arXiv:2503.06490 (replaced) [cn-pdf, pdf, other]

Title: Strong noise attenuation of seismic data based on Nash equilibrium Show Chinese title

Title: 基于纳什均衡的地震数据强噪声衰减

Mingwei Wang, Yingtian Liu, Junheng Peng, Yong Li, Huating Li

Subjects: Geophysics (physics.geo-ph)

Seismic data acquisition is often affected by various types of noise, which degrade data quality and hinder subsequent interpretation. Recovery of seismic data becomes particularly challenging in the presence of strong noise, which significantly impacts both data accuracy and geological analysis. This study proposes a novel single-encoder, multiple-decoder network based on Nash equalization (SEMD-Nash) for effective strong noise attenuation in seismic data. The main contributions of this method are as follows: First, we design a shared encoder-multi-decoder architecture, where an improved encoder extracts key features from the noisy data, and three parallel decoders reconstruct the denoised seismic signal from different perspectives. Second, we develop a multi-objective optimization system that integrates three loss functions-Mean Squared Error (MSE), Perceived Loss, and Structural Similarity Index (SSIM)-to ensure effective signal reconstruction, high-order feature preservation, and structural integrity. Third, we introduce the Nash Equalization Weight Optimizer, which dynamically adjusts the weights of the loss functions, balancing the optimization objectives to improve the models robustness and generalization. Experimental results demonstrate that the proposed method effectively suppresses strong noise while preserving the geological characteristics of the seismic data. Show Chinese abstract

地震数据采集常受到各种类型的噪声影响，这会降低数据质量并阻碍后续解释。 在强噪声存在的情况下，地震数据的恢复变得尤为具有挑战性，这会显著影响数据准确性和地质分析。 本研究提出了一种基于纳什均衡的单编码器多解码器网络（SEMD-Nash），以有效抑制地震数据中的强噪声。 该方法的主要贡献如下：首先，我们设计了一个共享编码器-多解码器架构，其中改进的编码器从噪声数据中提取关键特征，三个并行解码器从不同角度重建去噪的地震信号。 其次，我们开发了一个多目标优化系统，集成了三个损失函数——均方误差（MSE）、感知损失和结构相似性指数（SSIM）——以确保有效的信号重建、高阶特征保留和结构完整性。 第三，我们引入了纳什均衡权重优化器，它动态调整损失函数的权重，平衡优化目标，以提高模型的鲁棒性和泛化能力。 实验结果表明，所提出的方法能够在保留地震数据地质特征的同时有效抑制强噪声。

[8] arXiv:2504.08559 (replaced) [cn-pdf, pdf, html, other]

Title: Multi-MeV electron occurrence and lifetimes in the outer radiation belt and slot region during the maximum of solar cycle 22 Show Chinese title

Title: 太阳活动第22个周期最大期外辐射带和槽区多兆电子伏特电子出现率和寿命

R. T. Desai, J. Perrin, N. P. Meredith, S. A. Glauert, S. Ruparelia, W. R. Johnston

Comments: 15 pages, 7 figures, 1 tables. Accepted for publication in Space Weather on 24 September 2025

Subjects: Space Physics (physics.space-ph) ; Earth and Planetary Astrophysics (astro-ph.EP) ; High Energy Astrophysical Phenomena (astro-ph.HE) ; Solar and Stellar Astrophysics (astro-ph.SR) ; Geophysics (physics.geo-ph) ; Plasma Physics (physics.plasm-ph)

The Combined Release and Radiation Effects Satellite (CRRES) observed the response of the Van Allen radiation belts to peak solar activity within solar cycle 22. This study analyses relativistic and ultra-relativistic electron occurrence and loss timescales within the CRRES High Energy Electron Fluxometer (HEEF) dataset, including during several strong and severe geomagnetic storms that all, remarkably, flooded the slot region with multi-MeV electrons. These allow the first definitive multi-MeV electron lifetimes to be calculated in this region and indicate an elevated risk to satellites in slot region orbits during periods of heightened solar activity. The HEEF outer belt loss timescales are broadly in agreement with those from later solar cycles, but differences include longer-lasting sub-MeV electrons near the inner region of the outer belt and faster-decaying multi-MeV electrons near geosynchronous orbit. These differences are associated with higher levels of geomagnetic activity, a phenomenon that enables the spread in the results to be parameterised accordingly. The timescales generally appear well-bounded by Kp-dependent theoretical predictions, but the variability within the spread is not always well-ordered by geomagnetic activity. This suggests the limitations of using pitch-angle diffusion to account for the decay of elevated electrons following geomagnetic storms, and the need for more sophisticated space weather indices for radiation belt forecasting. Show Chinese abstract

联合释放和辐射效应卫星（CRRES）观测了范艾伦辐射带在太阳活动第22周期峰值期间的响应。 本研究分析了CRRES高能电子通量计（HEEF）数据集中的相对论和超相对论电子出现和损失时间尺度，包括在几次强烈的和严重的地磁风暴期间，这些风暴显著地将多兆电子伏特电子注入槽区。 这些数据使得首次计算该区域的多兆电子伏特电子寿命成为可能，并表明在太阳活动增强期间，槽区轨道上的卫星面临更高的风险。 HEEF外辐射带的损失时间尺度与后续太阳周期的观测结果大致一致，但存在一些差异，包括在外辐射带内侧区域的亚兆电子伏特电子持续时间更长，以及在地球同步轨道附近多兆电子伏特电子衰减更快。 这些差异与更高的地磁活动水平有关，这一现象使得结果的分散性能够相应地进行参数化。 时间尺度通常由Kp相关的理论预测所限定，但分散范围内的变化并不总是由地磁活动很好地排序。 这表明使用投掷角扩散来解释地磁风暴后增强电子的衰减存在局限性，并且需要更复杂的空间天气指数来进行辐射带预报。

[9] arXiv:2507.15809 (replaced) [cn-pdf, pdf, html, other]

Title: Diffusion models for multivariate subsurface generation and efficient probabilistic inversion Show Chinese title

Title: 多变量地下生成的扩散模型和高效的概率反演

Roberto Miele, Niklas Linde

Comments: 35 pages, 16 figures, reviewed version. Changes from V1 include: general revision of the text; Ip values are now reported in SI (Pa.s/m); addition of bibliographic references and correction of one reference wrongly reported (Yismaw et al., 2024); standardization of the figures' labels; corrected inaccuracies in Algorithms and improved readability

Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG) ; Geophysics (physics.geo-ph) ; Applications (stat.AP)

Diffusion models offer stable training and state-of-the-art performance for deep generative modeling tasks. Here, we consider their use in the context of multivariate subsurface modeling and probabilistic inversion. We first demonstrate that diffusion models enhance multivariate modeling capabilities compared to variational autoencoders and generative adversarial networks. In diffusion modeling, the generative process involves a comparatively large number of time steps with update rules that can be modified to account for conditioning data. We propose different corrections to the popular Diffusion Posterior Sampling approach by Chung et al. (2023). In particular, we introduce a likelihood approximation accounting for the noise-contamination that is inherent in diffusion modeling. We assess performance in a multivariate geological scenario involving facies and correlated acoustic impedance. Conditional modeling is demonstrated using both local hard data (well logs) and nonlinear geophysics (fullstack seismic data). Our tests show significantly improved statistical robustness, enhanced sampling of the posterior probability density function and reduced computational costs, compared to the original approach. The method can be used with both hard and indirect conditioning data, individually or simultaneously. As the inversion is included within the diffusion process, it is faster than other methods requiring an outer-loop around the generative model, such as Markov chain Monte Carlo. Show Chinese abstract

扩散模型为深度生成建模任务提供了稳定的训练和最先进的性能。 在这里，我们考虑它们在多变量地下建模和概率反演背景下的应用。 我们首先证明，与变分自编码器和生成对抗网络相比，扩散模型增强了多变量建模能力。 在扩散建模中，生成过程涉及相对较多的时间步数，其更新规则可以修改以考虑条件数据。 我们通过Chung等人(2023)提出的流行扩散后验采样方法提出了不同的修正。 特别是，我们引入了一种似然近似，考虑了扩散建模中固有的噪声污染。 我们在一个涉及岩相和相关声学阻抗的多变量地质场景中评估了性能。 使用局部硬数据（井数据）和非线性地球物理数据（全叠地震数据）进行了条件建模。 我们的测试表明，与原始方法相比，统计鲁棒性显著提高，后验概率密度函数的采样效果增强，计算成本降低。 该方法可以单独或同时与硬数据和间接条件数据一起使用。 由于反演包含在扩散过程中，它比其他需要围绕生成模型进行外循环的方法（如马尔可夫链蒙特卡洛）更快。