首页 > 最新文献

Computers & Geosciences最新文献

英文 中文
Automatic variogram calculation and modeling 自动变异图计算和建模
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-20 DOI: 10.1016/j.cageo.2024.105774
Luis Davila Saavedra , Clayton V. Deutsch
The variogram is one of the most used tools in geostatistics. It represents a key step for the results of estimation and simulation. This paper presents a methodology of the experimental variogram points calculation and subsequent modeling, including some practical considerations. The proposed methodology infers the variogram parameters directly from the dataset to require minimum user input. Autovar is a program that implements the described methodology, giving an initial variogram model for disseminated and tabular deposits.
变异图是地质统计学中最常用的工具之一。它是估算和模拟结果的关键步骤。本文介绍了实验变异图点计算和后续建模的方法,包括一些实际考虑因素。所提出的方法可直接从数据集中推导出变异图参数,从而将用户输入量降至最低。Autovar 是一个实现所述方法的程序,它给出了散布式和表格式矿床的初始变分法模型。
{"title":"Automatic variogram calculation and modeling","authors":"Luis Davila Saavedra ,&nbsp;Clayton V. Deutsch","doi":"10.1016/j.cageo.2024.105774","DOIUrl":"10.1016/j.cageo.2024.105774","url":null,"abstract":"<div><div>The variogram is one of the most used tools in geostatistics. It represents a key step for the results of estimation and simulation. This paper presents a methodology of the experimental variogram points calculation and subsequent modeling, including some practical considerations. The proposed methodology infers the variogram parameters directly from the dataset to require minimum user input. Autovar is a program that implements the described methodology, giving an initial variogram model for disseminated and tabular deposits.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105774"},"PeriodicalIF":4.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SaltFormer: A hybrid CNN-Transformer network for automatic salt dome detection SaltFormer:用于自动检测盐穹的混合 CNN-Transformer 网络
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105772
Yang Li , Suping Peng , Dengke He
Salt dome interpretation of seismic data is a crucial task in the exploration and development of oil and gas. Conventional techniques, such as multi-attribute analysis, are laborious, time-consuming, and susceptible to subjective biases in their results. To achieve a more automated and precise identification of salt dome, we developed a hybrid network for salt dome detection. In order to optimally exploit both local and global features, a hierarchical Vision Transformer is employed as an encoder for feature extraction. Concurrently, the concurrent spatial and channel squeeze & excitation attention module is utilized to improve detection accuracy in the decoder. Furthermore, we leveraged the complementarity of information between multiple tasks to enhance the model’s generalization performance. Using the competition data from the Kaggle platform provided by TGS-NOPEC Geophysics Company, automatic segmentation of salt domes was completed with a detection accuracy of 85.20%. A series of experiments were conducted using state-of-the-art models and the SaltFormer model, which was found to have higher detection accuracy compared to other networks. Finally, the test conducted with seismic field data from the Netherlands offshore F3 block in the North Sea demonstrate that the novel method is highly effective in detecting salt domes in seismic data.
地震数据的盐穹解译是勘探和开发油气的一项重要任务。多属性分析等传统技术费力、费时,而且结果容易出现主观偏差。为了实现更加自动化和精确的盐穹顶识别,我们开发了一种用于盐穹顶检测的混合网络。为了充分利用局部和全局特征,我们采用了分层视觉变换器作为特征提取的编码器。同时,利用并发空间和信道挤压& 激励注意模块来提高解码器的检测精度。此外,我们还利用多个任务之间的信息互补性来提高模型的泛化性能。利用 TGS-NOPEC 地球物理公司提供的 Kaggle 平台竞赛数据,完成了盐穹顶的自动分割,检测准确率达到 85.20%。使用最先进的模型和 SaltFormer 模型进行了一系列实验,发现与其他网络相比,SaltFormer 的检测准确率更高。最后,利用荷兰北海近海 F3 区块的地震现场数据进行的测试表明,这种新方法在检测地震数据中的盐穹顶方面非常有效。
{"title":"SaltFormer: A hybrid CNN-Transformer network for automatic salt dome detection","authors":"Yang Li ,&nbsp;Suping Peng ,&nbsp;Dengke He","doi":"10.1016/j.cageo.2024.105772","DOIUrl":"10.1016/j.cageo.2024.105772","url":null,"abstract":"<div><div>Salt dome interpretation of seismic data is a crucial task in the exploration and development of oil and gas. Conventional techniques, such as multi-attribute analysis, are laborious, time-consuming, and susceptible to subjective biases in their results. To achieve a more automated and precise identification of salt dome, we developed a hybrid network for salt dome detection. In order to optimally exploit both local and global features, a hierarchical Vision Transformer is employed as an encoder for feature extraction. Concurrently, the concurrent spatial and channel squeeze &amp; excitation attention module is utilized to improve detection accuracy in the decoder. Furthermore, we leveraged the complementarity of information between multiple tasks to enhance the model’s generalization performance. Using the competition data from the Kaggle platform provided by TGS-NOPEC Geophysics Company, automatic segmentation of salt domes was completed with a detection accuracy of 85.20%. A series of experiments were conducted using state-of-the-art models and the SaltFormer model, which was found to have higher detection accuracy compared to other networks. Finally, the test conducted with seismic field data from the Netherlands offshore F3 block in the North Sea demonstrate that the novel method is highly effective in detecting salt domes in seismic data.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105772"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MagTFs: A tool for estimating multiple magnetic transfer functions to constrain Earth’s electrical conductivity structure 磁传递函数:估算多重磁传递函数的工具,用于约束地球的导电结构
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105769
Zhengyong Ren , Zijun Zuo , Hongbo Yao , Chaojian Chen , Linan Xu , Jingtian Tang , Keke Zhang
Time-varying magnetic signals measured by geomagnetic observatories and satellites carry information about the Earth’s deep electrical conductivity structure and external current sources in the ionosphere and magnetosphere. Estimating magnetic transfer functions (TFs), which reflect the Earth’s internal conductivity structure, is a primary task in interpreting geomagnetic data from observatories and satellites. However, available TFs estimation tools either focus on a single source (ionosphere currents or magnetosphere currents) or are not publicly accessible. Therefore, we developed a flexible TFs estimation tool, named MagTFs, to achieve robust and precise estimation of magnetic TFs from the time series of magnetic field data acquired through land or satellite-based observations. This tool can handle magnetic data originating from time-varying currents in both the ionosphere and magnetosphere. We tested its performance on four kinds of data sets, and the good agreements with published results underscore the tool’s maturity and versatility in accurately estimating multi-source TFs. As a contribution to the scientific community, we have released MagTFs as an open-source tool, facilitating broader utilization and collaborative advancements.
地磁观测站和卫星测量到的时变磁信号携带着有关地球深层导电结构以及电离层和磁层中外部电流源的信息。估算反映地球内部传导结构的磁传递函数(TFs)是解释来自观测站和卫星的地磁数据的首要任务。然而,现有的磁传递函数估算工具要么只关注单一来源(电离层电流或磁层电流),要么无法公开获取。因此,我们开发了一种灵活的 TFs 估算工具,命名为 MagTFs,以便从陆基或卫星观测获得的磁场数据时间序列中稳健而精确地估算磁场 TFs。该工具可处理来自电离层和磁层中时变电流的磁数据。我们在四种数据集上测试了该工具的性能,结果与已发表的结果吻合良好,凸显了该工具在准确估算多源 TF 方面的成熟性和多功能性。作为对科学界的贡献,我们已将 MagTFs 作为开源工具发布,以促进更广泛的利用和合作进步。
{"title":"MagTFs: A tool for estimating multiple magnetic transfer functions to constrain Earth’s electrical conductivity structure","authors":"Zhengyong Ren ,&nbsp;Zijun Zuo ,&nbsp;Hongbo Yao ,&nbsp;Chaojian Chen ,&nbsp;Linan Xu ,&nbsp;Jingtian Tang ,&nbsp;Keke Zhang","doi":"10.1016/j.cageo.2024.105769","DOIUrl":"10.1016/j.cageo.2024.105769","url":null,"abstract":"<div><div>Time-varying magnetic signals measured by geomagnetic observatories and satellites carry information about the Earth’s deep electrical conductivity structure and external current sources in the ionosphere and magnetosphere. Estimating magnetic transfer functions (TFs), which reflect the Earth’s internal conductivity structure, is a primary task in interpreting geomagnetic data from observatories and satellites. However, available TFs estimation tools either focus on a single source (ionosphere currents or magnetosphere currents) or are not publicly accessible. Therefore, we developed a flexible TFs estimation tool, named MagTFs, to achieve robust and precise estimation of magnetic TFs from the time series of magnetic field data acquired through land or satellite-based observations. This tool can handle magnetic data originating from time-varying currents in both the ionosphere and magnetosphere. We tested its performance on four kinds of data sets, and the good agreements with published results underscore the tool’s maturity and versatility in accurately estimating multi-source TFs. As a contribution to the scientific community, we have released MagTFs as an open-source tool, facilitating broader utilization and collaborative advancements.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105769"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An identification for channel mislabel of strong motion records based on Siamese neural network 基于连体神经网络的强运动记录信道误标识别方法
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105780
Baofeng Zhou , Bo Liu , Xiaomin Wang , Yefei Ren , Maosheng Gong
Strong motion records are first-hand data for studying the seismic response of sites or engineering structures, and their objectivity is crucial for the credibility of the results in earthquake engineering and engineering seismology. However, domestic and international earthquake data may be mislabeled between horizontal and vertical channels. This issue is typically addressed by manually comparing the similarity between the three components of strong motion records, which is inherently subjective and inefficient in identification. To achieve the intelligent recognition of massive records, this study used 14,983 sets of ground motion records with significant differences between horizontal and vertical components from the NGA-West2 database. A Siamese neural network preliminarily distinguished the similarity between the acceleration waveform and the three components of the Fourier amplitude spectrum (FAS) of ground motion records. Combined with manual identification, an efficient and accurate method for identifying vertical components in ground motion records was proposed, and applied to verify the channel directions of the strong motion records in Strong Motion Network in China. It was found that 308 sets of records from 170 stations were suspected of mislabeling vertical and horizontal components. This advancement significantly enhances the objectivity of strong motion records. This proposed method holds potential for remote maintenance of strong motion stations, verifying the channels of strong motion instruments, and mitigating the negative impact of channel confusion on research results.
强震记录是研究场地或工程结构地震反应的第一手资料,其客观性对地震工程和工程地震学研究结果的可信度至关重要。然而,国内外的地震数据可能会在水平道和垂直道之间出现标注错误。解决这一问题的方法通常是人工比较强震记录三个部分的相似度,这种方法本身主观性强,识别效率低。为了实现海量记录的智能识别,本研究使用了来自 NGA-West2 数据库的 14,983 组水平和垂直分量差异显著的地面运动记录。暹罗神经网络初步区分了加速度波形与地动记录的傅里叶振幅谱(FAS)三个分量之间的相似性。结合人工识别,提出了一种高效、准确的地动记录垂直分量识别方法,并应用于中国强震网强震记录通道方向的验证。结果发现,170 个台站的 308 组记录存在误标垂直和水平分量的嫌疑。这一进步大大提高了强运动记录的客观性。该方法可用于强运动台站的远程维护,验证强运动仪器的信道,减少信道混乱对研究结果的负面影响。
{"title":"An identification for channel mislabel of strong motion records based on Siamese neural network","authors":"Baofeng Zhou ,&nbsp;Bo Liu ,&nbsp;Xiaomin Wang ,&nbsp;Yefei Ren ,&nbsp;Maosheng Gong","doi":"10.1016/j.cageo.2024.105780","DOIUrl":"10.1016/j.cageo.2024.105780","url":null,"abstract":"<div><div>Strong motion records are first-hand data for studying the seismic response of sites or engineering structures, and their objectivity is crucial for the credibility of the results in earthquake engineering and engineering seismology. However, domestic and international earthquake data may be mislabeled between horizontal and vertical channels. This issue is typically addressed by manually comparing the similarity between the three components of strong motion records, which is inherently subjective and inefficient in identification. To achieve the intelligent recognition of massive records, this study used 14,983 sets of ground motion records with significant differences between horizontal and vertical components from the NGA-West2 database. A Siamese neural network preliminarily distinguished the similarity between the acceleration waveform and the three components of the Fourier amplitude spectrum (FAS) of ground motion records. Combined with manual identification, an efficient and accurate method for identifying vertical components in ground motion records was proposed, and applied to verify the channel directions of the strong motion records in Strong Motion Network in China. It was found that 308 sets of records from 170 stations were suspected of mislabeling vertical and horizontal components. This advancement significantly enhances the objectivity of strong motion records. This proposed method holds potential for remote maintenance of strong motion stations, verifying the channels of strong motion instruments, and mitigating the negative impact of channel confusion on research results.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105780"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ReUNet: Efficient deep learning for precise ore segmentation in mineral processing ReUNet:用于矿物加工中精确矿石分割的高效深度学习
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105773
Chanjuan Wang , Huilan Luo , Jiyuan Wang , Daniel Groom
Efficient ore segmentation plays a pivotal role in advancing mineral processing technologies. With the rise of computer vision, deep learning models like UNet have increasingly outperformed traditional methods in automatic segmentation tasks. Despite these advancements, the substantial computational demands of such models have hindered their widespread adoption in practical production environments. To overcome this limitation, we developed ReUNet, a lightweight and efficient model tailored for mineral image segmentation. ReUNet optimizes computational efficiency by selectively focusing on critical spatial and channel information, boasting only 1.7 million parameters and 24.88 GFLOPS. It delivers superior segmentation performance across three public datasets (CuV1, FeMV1, and Pellets) and achieves the most accurate average particle size estimation, closely matching the true values. Our findings underscore ReUNet’s potential as a highly effective tool for mineral image analysis, offering both precision and efficiency in processing mineral images.
高效的矿石分割在推动矿物加工技术发展方面发挥着举足轻重的作用。随着计算机视觉技术的兴起,UNet 等深度学习模型在自动分割任务中的表现越来越优于传统方法。尽管取得了这些进步,但此类模型的大量计算需求阻碍了它们在实际生产环境中的广泛应用。为了克服这一限制,我们开发了 ReUNet,一种专为矿物图像分割定制的轻量级高效模型。ReUNet 通过选择性地关注关键的空间和通道信息来优化计算效率,仅有 170 万个参数和 24.88 GFLOPS。它在三个公共数据集(CuV1、FeMV1 和 Pellets)中提供了卓越的分割性能,并实现了最准确的平均粒度估计,与真实值非常接近。我们的研究结果凸显了 ReUNet 作为矿物图像分析高效工具的潜力,它在处理矿物图像方面既精确又高效。
{"title":"ReUNet: Efficient deep learning for precise ore segmentation in mineral processing","authors":"Chanjuan Wang ,&nbsp;Huilan Luo ,&nbsp;Jiyuan Wang ,&nbsp;Daniel Groom","doi":"10.1016/j.cageo.2024.105773","DOIUrl":"10.1016/j.cageo.2024.105773","url":null,"abstract":"<div><div>Efficient ore segmentation plays a pivotal role in advancing mineral processing technologies. With the rise of computer vision, deep learning models like UNet have increasingly outperformed traditional methods in automatic segmentation tasks. Despite these advancements, the substantial computational demands of such models have hindered their widespread adoption in practical production environments. To overcome this limitation, we developed ReUNet, a lightweight and efficient model tailored for mineral image segmentation. ReUNet optimizes computational efficiency by selectively focusing on critical spatial and channel information, boasting only 1.7 million parameters and 24.88 GFLOPS. It delivers superior segmentation performance across three public datasets (CuV1, FeMV1, and Pellets) and achieves the most accurate average particle size estimation, closely matching the true values. Our findings underscore ReUNet’s potential as a highly effective tool for mineral image analysis, offering both precision and efficiency in processing mineral images.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105773"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spectral whitening based seismic data preprocessing technique to improve the quality of surface wave's velocity spectra 基于频谱白化的地震数据预处理技术,提高面波速度谱的质量
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-19 DOI: 10.1016/j.cageo.2024.105784
Tarun Naskar , Mrinal Bhaumik , Sayan Mukherjee , Sai Vivek Adari
A high-quality surface wave velocity spectrum, also known as a dispersion image, is paramount for any MASW survey to accurately predict subsurface earth properties. The presence of diversified noise during field acquisition and dissimilar attenuation due to mechanical and radial damping makes it challenging for any wavefield transformation technique to produce a detailed and precise velocity spectrum. Standard surface wave data preprocessing techniques, such as trace normalization and bandpass filtering, along with postprocessing techniques like frequency-wise amplitude normalization, fail to address all these issues appropriately. In this paper, we present a spectral whitening-based data preprocessing technique that can adequately eradicate most of the shortcomings associated with different wavefield transformation techniques. Instead of normalizing each trace, it normalizes the amplitude of every frequency present in the seismogram. The spectral whitening can regain the relative amplitude losses due to both radial and mechanical damping, thus improving the signal-to-noise ratio. Along with diversified field data including Love and Rayleigh wave surveys, a synthetic dataset is used to demonstrate the efficacy of the proposed technique. Furthermore, field noise is added to random traces to test the ability of the proposed technique to filter asymmetric noise. Overall, the spectral whitening procedure significantly improves the quality of the velocity spectrum and produces a sharper dispersion image with well-separated modes. The work presented here enhances our ability to interpret surface wave velocity spectra precisely and helps explore accurate properties of the subsurface earth. It can help avoid the need for repeated field tests in cases of extremely noisy data, thereby significantly reducing costs and saving time.
高质量的面波速度频谱(也称为频散图像)对于任何 MASW 勘测准确预测地下土层属性都至关重要。由于现场采集过程中存在多种噪声,以及机械和径向阻尼导致的不同衰减,任何波场转换技术都很难生成详细而精确的速度频谱。标准的面波数据预处理技术,如轨迹归一化和带通滤波,以及后处理技术,如频率范围内的振幅归一化,都无法适当地解决所有这些问题。在本文中,我们提出了一种基于频谱白化的数据预处理技术,它能充分消除与不同波场变换技术相关的大部分缺点。它不是对每个地震道进行归一化处理,而是对地震图中每个频率的振幅进行归一化处理。频谱白化可以恢复由于径向阻尼和机械阻尼造成的相对振幅损失,从而提高信噪比。除了包括洛夫波和瑞利波勘测在内的各种现场数据外,还使用了一个合成数据集来证明所建议技术的有效性。此外,还在随机迹线中添加了现场噪声,以测试建议技术过滤非对称噪声的能力。总体而言,频谱白化程序大大提高了速度频谱的质量,并生成了具有良好分离模式的更清晰的频散图像。本文介绍的工作增强了我们精确解释面波速度频谱的能力,有助于探索地下地球的精确特性。在数据噪声极高的情况下,它有助于避免重复现场测试,从而大大降低了成本,节省了时间。
{"title":"Spectral whitening based seismic data preprocessing technique to improve the quality of surface wave's velocity spectra","authors":"Tarun Naskar ,&nbsp;Mrinal Bhaumik ,&nbsp;Sayan Mukherjee ,&nbsp;Sai Vivek Adari","doi":"10.1016/j.cageo.2024.105784","DOIUrl":"10.1016/j.cageo.2024.105784","url":null,"abstract":"<div><div>A high-quality surface wave velocity spectrum, also known as a dispersion image, is paramount for any MASW survey to accurately predict subsurface earth properties. The presence of diversified noise during field acquisition and dissimilar attenuation due to mechanical and radial damping makes it challenging for any wavefield transformation technique to produce a detailed and precise velocity spectrum. Standard surface wave data preprocessing techniques, such as trace normalization and bandpass filtering, along with postprocessing techniques like frequency-wise amplitude normalization, fail to address all these issues appropriately. In this paper, we present a spectral whitening-based data preprocessing technique that can adequately eradicate most of the shortcomings associated with different wavefield transformation techniques. Instead of normalizing each trace, it normalizes the amplitude of every frequency present in the seismogram. The spectral whitening can regain the relative amplitude losses due to both radial and mechanical damping, thus improving the signal-to-noise ratio. Along with diversified field data including Love and Rayleigh wave surveys, a synthetic dataset is used to demonstrate the efficacy of the proposed technique. Furthermore, field noise is added to random traces to test the ability of the proposed technique to filter asymmetric noise. Overall, the spectral whitening procedure significantly improves the quality of the velocity spectrum and produces a sharper dispersion image with well-separated modes. The work presented here enhances our ability to interpret surface wave velocity spectra precisely and helps explore accurate properties of the subsurface earth. It can help avoid the need for repeated field tests in cases of extremely noisy data, thereby significantly reducing costs and saving time.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105784"},"PeriodicalIF":4.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Heterogeneous layer effects on mining-induced dynamic ruptures 异质层对采矿引起的动态断裂的影响
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-17 DOI: 10.1016/j.cageo.2024.105776
Yatao Li
The risk of dynamic disasters increases with the trend toward deeper mining, highlighting an urgent need to better understand induced seismicity. To address this need, we developed custom code to implement the open-source software PyLith in the study of induced seismicity for the first time. We examined the effects of heterogeneous geological conditions on dynamic ruptures induced by deep mining operations. Our focus was on the dynamic ruptures and their effects on the nearby working face, analyzing parameters such as peak slip rates and rupture velocities. Our results show that rupture duration ranges from 255 ms to 676 ms and peak slip rates vary between 1.3 m/s and 5.0 m/s, with rupture velocities decreasing from 1.29 km/s to 0.17 km/s as the critical slip distance (Dc) increases. The relationship between peak slip rate and rupture velocity is consistent with Bizzarri's (2012) findings. A linear relationship between the times of peak slip rate (Tpv) and breakdown time (Tb) was observed, with a ratio of 1.0. In examining the induced seismic waves at the working face, we found that heterogeneous models exhibited more irregular slip distributions and higher peak particle acceleration (PPA) and peak particle velocity (PPV) compared to homogeneous models, indicating amplified seismic responses due to material heterogeneity. The study also identified potential risks to the working face's structural integrity, with more pronounced effects observed in hanging wall mining compared to footwall mining. These findings underscore the importance of considering geological heterogeneity in seismic hazard assessments and support the development of more accurate predictive models for mining-induced seismic events. It is important to note that our comparison of heterogeneous and homogeneous modeling is based on the assumption of identical initial traction, focusing on the effects of heterogeneous layers.
随着采矿向深部发展的趋势,动态灾害的风险也随之增加,因此迫切需要更好地了解诱发地震。为了满足这一需求,我们开发了自定义代码,首次将开源软件 PyLith 用于诱发地震的研究。我们研究了异质地质条件对深部采矿作业诱发的动态破裂的影响。我们的重点是动态破裂及其对附近工作面的影响,分析了峰值滑移率和破裂速度等参数。结果表明,随着临界滑移距离(Dc)的增加,破裂持续时间从 255 毫秒到 676 毫秒不等,峰值滑移率从 1.3 米/秒到 5.0 米/秒不等,破裂速度从 1.29 千米/秒下降到 0.17 千米/秒。峰值滑移率与破裂速度之间的关系与 Bizzarri(2012 年)的研究结果一致。峰值滑移速率(Tpv)和破裂时间(Tb)之间呈线性关系,比率为 1.0。在研究工作面诱发的地震波时,我们发现与均质模型相比,异质模型的滑移分布更不规则,峰值颗粒加速度 (PPA) 和峰值颗粒速度 (PPV) 也更高,这表明材料异质会放大地震反应。研究还发现了工作面结构完整性的潜在风险,与底壁采矿相比,悬壁采矿的影响更为明显。这些发现强调了在地震灾害评估中考虑地质异质性的重要性,并支持开发更准确的采矿诱发地震事件预测模型。值得注意的是,我们对异质和均质模型的比较是基于相同初始牵引力的假设,重点是异质层的影响。
{"title":"Heterogeneous layer effects on mining-induced dynamic ruptures","authors":"Yatao Li","doi":"10.1016/j.cageo.2024.105776","DOIUrl":"10.1016/j.cageo.2024.105776","url":null,"abstract":"<div><div>The risk of dynamic disasters increases with the trend toward deeper mining, highlighting an urgent need to better understand induced seismicity. To address this need, we developed custom code to implement the open-source software PyLith in the study of induced seismicity for the first time. We examined the effects of heterogeneous geological conditions on dynamic ruptures induced by deep mining operations. Our focus was on the dynamic ruptures and their effects on the nearby working face, analyzing parameters such as peak slip rates and rupture velocities. Our results show that rupture duration ranges from 255 ms to 676 ms and peak slip rates vary between 1.3 m/s and 5.0 m/s, with rupture velocities decreasing from 1.29 km/s to 0.17 km/s as the critical slip distance (<em>D</em><sub>c</sub>) increases. The relationship between peak slip rate and rupture velocity is consistent with Bizzarri's (2012) findings. A linear relationship between the times of peak slip rate (T<sub>pv</sub>) and breakdown time (T<sub>b</sub>) was observed, with a ratio of 1.0. In examining the induced seismic waves at the working face, we found that heterogeneous models exhibited more irregular slip distributions and higher peak particle acceleration (PPA) and peak particle velocity (PPV) compared to homogeneous models, indicating amplified seismic responses due to material heterogeneity. The study also identified potential risks to the working face's structural integrity, with more pronounced effects observed in hanging wall mining compared to footwall mining. These findings underscore the importance of considering geological heterogeneity in seismic hazard assessments and support the development of more accurate predictive models for mining-induced seismic events. It is important to note that our comparison of heterogeneous and homogeneous modeling is based on the assumption of identical initial traction, focusing on the effects of heterogeneous layers.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105776"},"PeriodicalIF":4.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Robust frequency-domain acoustic waveform inversion using a measurement of smooth radical function derived from compressive sensing 利用测量压缩传感得出的平滑激波函数进行稳健的频域声波反演
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-17 DOI: 10.1016/j.cageo.2024.105778
Chao Lang, Ning Wang, Shi-Li Pang
A smooth radical function derived from compressive sensing is introduced, aiming to measure the misfit in frequency-domain acoustic waveform inversion. The purpose of employing this function is to improve inverse accuracy and reliability. With a novel approximation of L1 norm, the objective function constructed by this measurement can exhibit favorable robustness throughout the inverse iteration. By exploiting the smoothness property, the misfit can be minimized through a cost-effective approach of taking derivatives. The inverse framework of the smooth radical function is derived which indicates comparable computing complexity per iterative step to L2 case, theoretically. The experiential data with outliers are employed for inversion and compared with the traditional optimization-based L1 norm and L2 norm. The obtained results are consistent with theoretical analysis and demonstrate the superiority of the proposed measurement.
本文介绍了一种源自压缩传感的平滑基函数,旨在测量频域声波反演中的不匹配度。使用该函数的目的是提高反演精度和可靠性。通过对 L1 准则的新颖近似,利用该测量方法构建的目标函数在整个反演迭代过程中表现出良好的鲁棒性。利用平滑特性,可以通过求导的低成本方法使误差最小化。推导出的平滑基函数逆框架表明,理论上每个迭代步骤的计算复杂度与 L2 情况相当。利用带有异常值的经验数据进行反演,并与传统的基于优化的 L1 准则和 L2 准则进行比较。得到的结果与理论分析一致,证明了所提出的测量方法的优越性。
{"title":"Robust frequency-domain acoustic waveform inversion using a measurement of smooth radical function derived from compressive sensing","authors":"Chao Lang,&nbsp;Ning Wang,&nbsp;Shi-Li Pang","doi":"10.1016/j.cageo.2024.105778","DOIUrl":"10.1016/j.cageo.2024.105778","url":null,"abstract":"<div><div>A smooth radical function derived from compressive sensing is introduced, aiming to measure the misfit in frequency-domain acoustic waveform inversion. The purpose of employing this function is to improve inverse accuracy and reliability. With a novel approximation of L1 norm, the objective function constructed by this measurement can exhibit favorable robustness throughout the inverse iteration. By exploiting the smoothness property, the misfit can be minimized through a cost-effective approach of taking derivatives. The inverse framework of the smooth radical function is derived which indicates comparable computing complexity per iterative step to L2 case, theoretically. The experiential data with outliers are employed for inversion and compared with the traditional optimization-based L1 norm and L2 norm. The obtained results are consistent with theoretical analysis and demonstrate the superiority of the proposed measurement.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105778"},"PeriodicalIF":4.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Augmented formulation for a Bayesian approach for frequency-domain full-waveform inversion to estimate the material properties of a layered half-space 贝叶斯频域全波形反演方法的增强公式,用于估算层状半空间的材料特性
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-16 DOI: 10.1016/j.cageo.2024.105782
Hieu Van Nguyen, Jin Ho Lee
Seismic full-waveform inversion (FWI) facilitates the generation of high-resolution subsurface images using wavefield measurements. Seismic FWI in the frequency domain is preferable because it allows consideration of the multiscale nature of FWI, controls the numerical dispersion of the media, and represents the hysteretic damping of the material. The Bayesian approach can be considered for FWI problems to alleviate the ill-posedness of inverse problems and quantify the uncertainty of the estimated parameters. This study rigorously formulates a Bayesian approach for seismic FWI in the frequency domain, assuming Gaussian probability distributions for the prior information of parameters to be estimated and the likelihood functions of observations. Conventional and augmented formulations are provided. In the augmented formulation, complex dynamic responses in the frequency domain are augmented by their complex conjugates. Rigorous expressions are derived for the posterior covariance matrix of estimated parameters to assess the uncertainty in these parameters. The proposed augmented formulation is demonstrated using various elastic inverse problems to estimate the shear-wave velocities of layered half-spaces. Excellent inverted profiles for the shear-wave velocities are obtained, and their posterior probability distributions are estimated using the Bayesian approach.
地震全波形反演(FWI)有助于利用波场测量生成高分辨率的地下图像。频域地震全波形反演比较可取,因为它可以考虑全波形反演的多尺度性质,控制介质的数值色散,并表示材料的滞后阻尼。对于 FWI 问题,可以考虑采用贝叶斯方法来缓解逆问题的拟合不良性,并量化估计参数的不确定性。本研究假设待估算参数的先验信息和观测值的似然函数为高斯概率分布,严格制定了频域地震 FWI 的贝叶斯方法。研究提供了传统公式和增强公式。在增强公式中,频域中的复杂动态响应由其复杂共轭物增强。为估算参数的后验协方差矩阵导出了严格的表达式,以评估这些参数的不确定性。利用各种弹性反演问题来估算层状半空间的剪切波速度,证明了所提出的增强公式。获得了剪切波速度的出色反演剖面,并利用贝叶斯方法估算了它们的后验概率分布。
{"title":"Augmented formulation for a Bayesian approach for frequency-domain full-waveform inversion to estimate the material properties of a layered half-space","authors":"Hieu Van Nguyen,&nbsp;Jin Ho Lee","doi":"10.1016/j.cageo.2024.105782","DOIUrl":"10.1016/j.cageo.2024.105782","url":null,"abstract":"<div><div>Seismic full-waveform inversion (FWI) facilitates the generation of high-resolution subsurface images using wavefield measurements. Seismic FWI in the frequency domain is preferable because it allows consideration of the multiscale nature of FWI, controls the numerical dispersion of the media, and represents the hysteretic damping of the material. The Bayesian approach can be considered for FWI problems to alleviate the ill-posedness of inverse problems and quantify the uncertainty of the estimated parameters. This study rigorously formulates a Bayesian approach for seismic FWI in the frequency domain, assuming Gaussian probability distributions for the prior information of parameters to be estimated and the likelihood functions of observations. Conventional and augmented formulations are provided. In the augmented formulation, complex dynamic responses in the frequency domain are augmented by their complex conjugates. Rigorous expressions are derived for the posterior covariance matrix of estimated parameters to assess the uncertainty in these parameters. The proposed augmented formulation is demonstrated using various elastic inverse problems to estimate the shear-wave velocities of layered half-spaces. Excellent inverted profiles for the shear-wave velocities are obtained, and their posterior probability distributions are estimated using the Bayesian approach.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105782"},"PeriodicalIF":4.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ProbShakemap: A Python toolbox propagating source uncertainty to ground motion prediction for urgent computing applications ProbShakemap:为紧急计算应用传播地动预测源不确定性的 Python 工具箱
IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-16 DOI: 10.1016/j.cageo.2024.105748
Angela Stallone , Jacopo Selva , Louise Cordrie , Licia Faenza , Alberto Michelini , Valentino Lauciani
Seismic urgent computing enables early assessment of an earthquake’s impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions about rescue and mitigation activities in the affected areas. Uncertainty quantification for urgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present ProbShakemap, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. ProbShakemap incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS ShakeMap software and the OpenQuake-engine. ShakeMap modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the OpenQuake-engine libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. ProbShakemap provides the user with a set of tools to explore, at each POI, the predictive distribution of ground motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between ProbShakemap and ShakeMap output.
地震紧急计算通过提供基于模拟的快速地震动预报,能够对地震的影响进行早期评估。地方当局和灾害风险管理者可以利用这些信息为灾区的救援和减灾活动提供决策依据。紧急计算应用的不确定性量化是最具挑战性的任务之一。目前的做法考虑了地震动模型 (GMM) 带来的不确定性,但忽略了震源模型带来的不确定性,而震源模型在地震发生后的最初几分钟内只能大致知道。原则上,地震源的不确定性可以通过捕捉震源变异性的地震场景集合物理模拟传播到地动预测中。然而,在时间紧迫的紧急情况下,完全的集合模拟是不可行的。在此,我们介绍一个 Python 工具箱 ProbShakemap,该工具箱可生成多场景集合,并为紧急震源不确定性量化提供基于集合的预测。该工具箱实现了 GMM,可有效地将源头不确定性从场景集合传播到一组兴趣点 (POI) 的地动预测,同时还考虑了模型的不确定性(如果有的话,可通过容纳多个 GMM)及其固有的不确定性。ProbShakemap 融合了两个开源工具箱的功能,这两个工具箱通常用于地震灾害和风险分析:USGS ShakeMap 软件和 OpenQuake-engine。ShakeMap 模块用于自动选择地震灾区可用的 GMMs 集和权重,而 OpenQuake-engine 库则用于通过随机抽样可用的 GMMs 来计算一组点上的地震动。ProbShakemap 为用户提供了一套工具,用于在每个 POI 探索地震动值的预测分布,包括震源不确定性、模型不确定性和 GMMs 固有的可变性。我们提出的方法针对 2016 年 10 月 30 日发生的 Mw 6.5 Norcia 地震和 2023 年 2 月 6 日发生的 Mw 7.8 Pazarcik 地震进行了定量测试。我们还说明了 ProbShakemap 和 ShakeMap 输出之间的差异。
{"title":"ProbShakemap: A Python toolbox propagating source uncertainty to ground motion prediction for urgent computing applications","authors":"Angela Stallone ,&nbsp;Jacopo Selva ,&nbsp;Louise Cordrie ,&nbsp;Licia Faenza ,&nbsp;Alberto Michelini ,&nbsp;Valentino Lauciani","doi":"10.1016/j.cageo.2024.105748","DOIUrl":"10.1016/j.cageo.2024.105748","url":null,"abstract":"<div><div>Seismic urgent computing enables early assessment of an earthquake’s impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions about rescue and mitigation activities in the affected areas. Uncertainty quantification for urgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present <span>ProbShakemap</span>, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. <span>ProbShakemap</span> incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS <span>ShakeMap</span> software and the <span>OpenQuake-engine</span>. <span>ShakeMap</span> modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the <span>OpenQuake-engine</span> libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. <span>ProbShakemap</span> provides the user with a set of tools to explore, at each POI, the predictive distribution of ground motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between <span>ProbShakemap</span> and <span>ShakeMap</span> output.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"195 ","pages":"Article 105748"},"PeriodicalIF":4.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Computers & Geosciences
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1