arXiv - PHYS - Geophysics最新文献_第8页

DCMSA: Multi-Head Self-Attention Mechanism Based on Deformable Convolution For Seismic Data Denoising DCMSA：基于可变形卷积的地震数据去噪多头自适应机制

arXiv - PHYS - Geophysics Pub Date : 2024-08-13 DOI: arxiv-2408.06963

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

引用次数: 0

High-resolution closed-loop seismic inversion network in time-frequency phase mixed domain 时频相位混合域高分辨率闭环地震反演网络

arXiv - PHYS - Geophysics Pub Date : 2024-08-09 DOI: arxiv-2408.04932

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

{"title":"High-resolution closed-loop seismic inversion network in time-frequency phase mixed domain","authors":"Yingtian Liu, Yong Li, Junheng Peng, Huating Li, Mingwei Wang","doi":"arxiv-2408.04932","DOIUrl":"https://doi.org/arxiv-2408.04932","url":null,"abstract":"Thin layers and reservoirs may be concealed in areas of low seismic\u0000reflection amplitude, making them difficult to recognize. Deep learning (DL)\u0000techniques provide new opportunities for accurate impedance prediction by\u0000establishing a nonlinear mapping between seismic data and impedance. However,\u0000existing methods primarily use time domain seismic data, which limits the\u0000capture of frequency bands, thus leading to insufficient resolution of the\u0000inversion results. To address these problems, we introduce a new\u0000time-frequency-phase (TFP) mixed-domain closed-loop seismic inversion network\u0000(TFP-CSIN) to improve the identification of thin layers and reservoirs. First,\u0000the inversion network and closed-loop network are constructed by using\u0000bidirectional gated recurrent units (Bi-GRU) and convolutional neural network\u0000(CNN) architectures, enabling bidirectional mapping between seismic data and\u0000impedance data. Next, to comprehensive learning across the entire frequency\u0000spectrum, the Fourier transform is used to capture frequency information and\u0000establish frequency domain constraints. At the same time, the phase domain\u0000constraint is introduced through Hilbert transformation, which improves the\u0000method's ability to recognize the weak reflection region features. Both\u0000experiments on the synthetic data show that TFP-CSIN outperforms the\u0000traditional supervised learning method and time domain semi-supervised learning\u0000methods in seismic inversion. The field data further verify that the proposed\u0000method improves the identification ability of weak reflection areas and thin\u0000layers.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New versus past silica crush curve experiments: application to Dimorphos benchmarking impact simulations 新的与过去的二氧化硅挤压曲线实验：应用于 Dimorphos 基准冲击模拟

arXiv - PHYS - Geophysics Pub Date : 2024-08-07 DOI: arxiv-2408.04014

Uri Malamud, Christoph M. Schafer, Irina Luciana San Sebastian, Maximilian Timpe, Karl Alexander Essink, Christopher Kreuzig, Gerwin Meier, Jürgen Blum, Hagai B. Perets, Christoph Burger

{"title":"New versus past silica crush curve experiments: application to Dimorphos benchmarking impact simulations","authors":"Uri Malamud, Christoph M. Schafer, Irina Luciana San Sebastian, Maximilian Timpe, Karl Alexander Essink, Christopher Kreuzig, Gerwin Meier, Jürgen Blum, Hagai B. Perets, Christoph Burger","doi":"arxiv-2408.04014","DOIUrl":"https://doi.org/arxiv-2408.04014","url":null,"abstract":"Crush curves are of fundamental importance to numerical modeling of small and\u0000porous astrophysical bodies. The empirical literature often measures them for\u0000silica grains, and different studies have used various methods, sizes,\u0000textures, and pressure conditions. Here we review past studies and supplement\u0000further experiments in order to develop a full and overarching understanding of\u0000the silica crush curve behavior. We suggest a new power-law function that can\u0000be used in impact simulations of analog materials similar to micro-granular\u0000silica. We perform a benchmarking study to compare this new crush curve to the\u0000parametric quadratic crush curve often used in other studies, based on the\u0000study case of the DART impact onto the asteroid Dimorphos. We find that the\u0000typical quadratic crush curve parameters do not closely follow the silica\u0000crushing experiments, and as a consequence they under (over) estimate\u0000compression close (far) from the impact site. The new crush curve presented\u0000here, applicable to pressures between a few hundred Pa and up to 1.1 GPa, might\u0000therefore be more precise. Additionally, it is not calibrated by case-specific\u0000parameters, and can be used universally for comet- or asteroid-like bodies,\u0000given an assumed composition similar to micro-granular silica.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparison of Fireball Luminous Efficiency Models using Acoustic Records 利用声学记录比较火球发光效率模型

arXiv - PHYS - Geophysics Pub Date : 2024-08-07 DOI: arxiv-2408.04078

Luke McFadden, Peter Brown, Denis Vida

{"title":"A Comparison of Fireball Luminous Efficiency Models using Acoustic Records","authors":"Luke McFadden, Peter Brown, Denis Vida","doi":"arxiv-2408.04078","DOIUrl":"https://doi.org/arxiv-2408.04078","url":null,"abstract":"The total energy of a fireball is commonly obtained from optical measurements\u0000with an assumed value for luminous efficiency. Acoustic energy measurements\u0000offer an independent means of energy estimation. Here we combine optical and\u0000acoustic methods to validate the luminous efficiency model of Boroviv{c}ka et\u0000al. (2020). Our goal is to compare these models with acoustic measurements of\u0000meteoroid energy deposition. Employing theoretical blast scaling laws following\u0000the approach of McFadden et al. (2021), we determine explosive yields for both\u0000fireball fragmentation events and cylindrical shocks for four different bright\u0000fireballs. We model fireballs using the MetSim software (Vida et al., 2023) and\u0000find that the Boroviv{c}ka et al. (2020) model produces agreement better than\u0000a factor of two for our three chondritic fireball case studies. The major\u0000exception is an iron meteorite-producing fireball where the luminous efficiency\u0000is an order of magnitude higher than model predictions calibrated with stony\u0000fireballs. We suggest that large disparities between optical and acoustic\u0000energies could be a signature of iron fireballs and hence useful as a\u0000discriminant of that population.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Certain aspects of prestack deconvolution 预叠加解卷积的某些方面

arXiv - PHYS - Geophysics Pub Date : 2024-08-06 DOI: arxiv-2408.03089

Jagmeet Singh

引用次数: 0

Next-order balanced model captures submesoscale physics and statistics 下一阶平衡模型捕捉次主题尺度物理和统计信息

arXiv - PHYS - Geophysics Pub Date : 2024-08-06 DOI: arxiv-2408.03422

Ryan Shìjié Dù, K. Shafer Smith, Oliver Bühler

{"title":"Next-order balanced model captures submesoscale physics and statistics","authors":"Ryan Shìjié Dù, K. Shafer Smith, Oliver Bühler","doi":"arxiv-2408.03422","DOIUrl":"https://doi.org/arxiv-2408.03422","url":null,"abstract":"Using nonlinear simulations in two settings, we demonstrate that\u0000QG$^mathrm{+1}$, a potential-vorticity (PV) based next-order-in-Rossby\u0000balanced model, captures several aspects of ocean submesoscale physics. In\u0000forced-dissipative 3D simulations under baroclinically unstable Eady-type\u0000background states, the statistical equilibrium turbulence exhibits long\u0000cyclonic tails and a plethora of rapidly-intensifying ageostrophic fronts.\u0000Despite that the model requires setting an explicit, small value for the fixed\u0000scaling Rossby number, the emergent flows are nevertheless characterized by\u0000$O(f)$ vorticity and convergence, as observed in upper-ocean submesoscale\u0000flows. Simulations of QG$^mathrm{+1}$ under the classic strain-induced\u0000frontogenesis set-up show realistic frontal asymmetry and a finite time\u0000blow-up, quantitatively comparable to simulations of the semigeostrophic\u0000equations. The inversions in the QG$^mathrm{+1}$ model are straightforward\u0000elliptic problems, allowing for the reconstruction of all flow fields from the\u0000PV and surface buoyancy, while avoiding the semigeostrophic coordinate\u0000transformation. Together, these results suggest QG$^mathrm{+1}$ as a useful\u0000tool for studying upper-ocean submesoscale dynamics.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network 利用基于注意力的双支双反网络进行声阻抗预测

arXiv - PHYS - Geophysics Pub Date : 2024-08-05 DOI: arxiv-2408.02524

Wen Feng, Yong Li, Yingtian Liu, Huating Li

{"title":"Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network","authors":"Wen Feng, Yong Li, Yingtian Liu, Huating Li","doi":"arxiv-2408.02524","DOIUrl":"https://doi.org/arxiv-2408.02524","url":null,"abstract":"Seismic impedance inversion is a widely used technique for reservoir\u0000characterization. Accurate, high-resolution seismic impedance data form the\u0000foundation for subsequent reservoir interpretation. Deep learning methods have\u0000demonstrated significant potential in seismic impedance inversion. Traditional\u0000single semi-supervised networks, which directly input original seismic logging\u0000data, struggle to capture high-frequency weak signals. This limitation leads to\u0000low-resolution inversion results with inadequate accuracy and stability.\u0000Moreover, seismic wavelet uncertainty further constrains the application of\u0000these methods to real seismic data. To address these challenges, we propose\u0000ADDIN-I: an Attention-based Dual-branch Double-Inversion Network for Impedance\u0000prediction. ADDIN-I's dual-branch architecture overcomes the limitations of\u0000single-branch semi-supervised networks and improves the extraction of\u0000high-frequency weak signal features in sequence modeling. The network\u0000incorporates an attention mechanism to further enhance its feature extraction\u0000capabilities. To adapt the method for real seismic data applications, a deep\u0000learning forward operator is employed to fit the wavelet adaptively. ADDIN-I\u0000demonstrates excellent performance in both synthetic and real data\u0000applications.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Noise Suppression for CRP Gathers Based on Self2Self with Dropout 基于 "自我对自我"（Self2Self）和 "丢弃"（Dropout）的 CRP 采集的噪声抑制

arXiv - PHYS - Geophysics Pub Date : 2024-08-05 DOI: arxiv-2408.02187

Fei Li, Zhenbin Xia, Dawei Liu, Xiaokai Wang, Wenchao Chen, Juan Chen, Leiming Xu

{"title":"Noise Suppression for CRP Gathers Based on Self2Self with Dropout","authors":"Fei Li, Zhenbin Xia, Dawei Liu, Xiaokai Wang, Wenchao Chen, Juan Chen, Leiming Xu","doi":"arxiv-2408.02187","DOIUrl":"https://doi.org/arxiv-2408.02187","url":null,"abstract":"Noise suppression in seismic data processing is a crucial research focus for\u0000enhancing subsequent imaging and reservoir prediction. Deep learning has shown\u0000promise in computer vision and holds significant potential for seismic data\u0000processing. However, supervised learning, which relies on clean labels to train\u0000network prediction models, faces challenges due to the unavailability of clean\u0000labels for seismic exploration data. In contrast, self-supervised learning\u0000substitutes traditional supervised learning with surrogate tasks by different\u0000auxiliary means, exploiting internal input data information. Inspired by\u0000Self2Self with Dropout, this paper presents a self-supervised learning-based\u0000noise suppression method called Self-Supervised Deep Convolutional Networks\u0000(SSDCN), specifically designed for Common Reflection Point (CRP) gathers. We\u0000utilize pairs of Bernoulli-sampled instances of the input noisy image as\u0000surrogate tasks to leverage its inherent structure. Furthermore, SSDCN\u0000incorporates geological knowledge through the normal moveout correction\u0000technique, which capitalizes on the approximately horizontal behavior and\u0000strong self-similarity observed in useful signal events within CRP gathers. By\u0000exploiting the discrepancy in self-similarity between the useful signals and\u0000noise in CRP gathers, SSDCN effectively extracts self-similarity features\u0000during training iterations, prioritizing the extraction of useful signals to\u0000achieve noise suppression. Experimental results on synthetic and actual CRP\u0000gathers demonstrate that SSDCN achieves high-fidelity noise suppression.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards improved understanding of spontaneous imbibition into dry porous media using pore-scale direct numerical simulations 利用孔隙尺度直接数值模拟加深对干燥多孔介质自发浸润的理解

arXiv - PHYS - Geophysics Pub Date : 2024-08-05 DOI: arxiv-2408.02831

Luka Malenica, Zhidong Zhang, Ueli Angst

{"title":"Towards improved understanding of spontaneous imbibition into dry porous media using pore-scale direct numerical simulations","authors":"Luka Malenica, Zhidong Zhang, Ueli Angst","doi":"arxiv-2408.02831","DOIUrl":"https://doi.org/arxiv-2408.02831","url":null,"abstract":"Traditional approaches to mathematically describe spontaneous imbibition are\u0000usually based on either macro-scale models, such as Richards equation, or\u0000simplified pore-scale models, such as the bundle of capillary tubes (BCTM) or\u0000pore-network modeling (PNM). It is well known that such models cannot provide\u0000full microscopic details of the multiphase flow processes and that many\u0000pore-scale mechanisms still lack proper mathematical descriptions. To improve\u0000the predictive capabilities of traditional models, a fundamental understanding\u0000of pore-scale dynamics is needed. The focus of this paper is obtaining detailed\u0000insight and consistent explanation of particular processes during\u0000capillary-controlled water imbibition into dry porous media. We use\u0000two-dimensional model geometries and perform fully dynamic volume-of-fluid\u0000based direct numerical simulations of air-water multiphase flow at the\u0000pore-scale, to study processes that generally are not considered in traditional\u0000models. More specifically, we investigate differences between converging and\u0000diverging geometries, dynamic pressure and meniscus reconfiguration during\u0000pore-filling events, and the influence of inertia and pore size on imbibition\u0000dynamics and the occurrence of capillary barriers. Furthermore, we perform a\u0000detailed comparison between non-interacting and interacting BCTM and study the\u0000impact of the narrow contractions on imbibition dynamics and the trapping of\u0000the non-wetting phase. Obtained knowledge can be used to improve predictive\u0000models, which are broadly relevant considering the importance of spontaneous\u0000imbibition in many different natural and industrial processes.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization 利用多目标灰狼优化法联合反演直流电阻率和 MT 数据

arXiv - PHYS - Geophysics Pub Date : 2024-08-05 DOI: arxiv-2408.02414

Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh

{"title":"Joint Inversion of DC Resistivity and MT Data using Multi-Objective Grey Wolf Optimization","authors":"Rohan Sharma, Divakar Vashisth, Kuldeep Sarkar, Upendra Kumar Singh","doi":"arxiv-2408.02414","DOIUrl":"https://doi.org/arxiv-2408.02414","url":null,"abstract":"Joint inversion of geophysical datasets is instrumental in subsurface\u0000characterization and has garnered significant popularity, leveraging\u0000information from multiple geophysical methods. In this study, we implemented\u0000the joint inversion of DC resistivity with MT data using the Multi-Objective\u0000Grey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used\u0000Grey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal\u0000non-dominated solutions, eliminating the need for weighting parameters in the\u0000objective functions. This set of non-dominated predictions also facilitates the\u0000understanding of uncertainty in the predicted model parameters. Through a field\u0000case study in the region around Broken Hill in South Central Australia, the\u0000paper showcases MOGWO's capabilities in joint inversion, providing confident\u0000estimates of the model parameters (resistivity profiles), as indicated by a\u0000narrow spread in the suite of solutions. The obtained results are comparable to\u0000well established methodologies and highlight the efficacy of MOGWO as a\u0000reliable tool in geophysical exploration.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0