Article
Geosciences, Multidisciplinary
Yumin Zhao, Enhedelihai Alex Nilot, Bei Li, Gang Fang, Wei Luo, Yunyue Elita Li
Summary: By extracting seismic ambient noise from motor vehicle noise, we quantified the linear relationship between frequency and amplitude ratio of paired instantaneous spectra to obtain daily seismic attenuation. After verifying the reliability of the method, it was applied to seismic ambient noise data collected from three urban sites. The estimated attenuation was compared with three environmental variables: rainfall, temperature, and traffic volume. The results showed a strong correlation between estimated seismic attenuation and precipitation, indicating a high sensitivity to changes in soil moisture and groundwater system.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Geochemistry & Geophysics
Yangkang Chen, Alexandros Savvaidis, Sergey Fomel
Summary: Passive seismic denoising is often done using band-pass filters, but this can be problematic when signal and noise have the same frequency. This study presents a new data-driven denoising method based on adaptive sparse transform. The method is flexible and can be applied to any passive seismic monitoring project.
SEISMOLOGICAL RESEARCH LETTERS
(2023)
Article
Geochemistry & Geophysics
Fangyu Li, Fengyuan Sun, Naihao Liu, Rui Xie
Summary: The proposed method is a generalized seismic noise attenuation solution that efficiently suppresses random noise and achieves better recovery of seismic signal components. The resampling mechanism alleviates signal loss without requiring extensive parameter tuning.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Fabrizio Magrini, Lapo Boschi
Summary: This study evaluates the determination of surface-wave attenuation from ambient-noise data through numerical tests. The directionality of noise sources and the attenuation coefficient in the area of interest were identified from both experimental setups and real recordings. The method shows promise in accurately quantifying surface-wave attenuation at relatively high frequencies.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2021)
Article
Geochemistry & Geophysics
Martin van Driel, Savas Ceylan, John F. Clinton, Domenico Giardini, Anna Horleston, Ludovic Margerin, Simon C. Stahler, Maren Bose, Constantinos Charalambous, Taichi Kawamura, Amir Khan, Guenole Orhand-Mainsant, John-R. Scholz, Fabian Euchner, Martin Knapmeyer, Nicholas Schmerr, William T. Pike, Philippe Lognonne, William B. Banerdt
Summary: The seismometer on Mars has detected hundreds of marsquakes, with most being high-frequency events exhibiting resonance and distinct seismic energy arrivals. These events have been classified into three types based on frequency content and energy ratio, and the travel times between arrivals are related to epicentral distance. The amplitude shape is explained by layered models with scattering.
JOURNAL OF GEOPHYSICAL RESEARCH-PLANETS
(2021)
Article
Geosciences, Multidisciplinary
Tomoya Takano, Kiwamu Nishida
Summary: Microcracks in geomaterials lead to changes in elastic moduli under strain, resulting in seismic wave velocity variations that are crucial for understanding crustal dynamic processes. Previous research has not explored the characteristics of seismic velocity variations caused by large-scale tidal deformation. A new method utilizing a state-space model was developed to systematically evaluate tidal response to velocity variations. Large tide-induced seismic velocity changes were observed in the low S-wave velocity region of the shallow crust. Overall, the tidal responses of velocity variations provide new insights into the response mechanisms of the shallow crust to applied strain.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Geochemistry & Geophysics
Bin Liu, Jinghang Yue, Zhiwu Zuo, Xinji Xu, Chao Fu, Senlin Yang, Peng Jiang
Summary: An unsupervised learning method based on the features of seismic data was proposed for denoising, utilizing a deep convolutional neural network. Experimental results demonstrated the effectiveness of the method in suppressing random noise while preserving amplitude.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Chuangji Meng, Jinghuai Gao, Yajun Tian, Zhiqiang Wang
Summary: This study proposes a deep learning framework based on Non-IID noise modeling for seismic random noise attenuation. By using variational inference technique, the framework can adaptively characterize the noise and data distribution in the local area, leading to improved generalization and performance capabilities.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xingye Liu, Qin Li, Cheng Yuan, Jingye Li, Xiaohong Chen, Yangkang Chen
Summary: A high-order directional total variation (HDTV) method for seismic denoising is developed, considering the local structural direction and higher order derivatives to avoid the staircasing effect. Comparative tests with synthetic models and field seismic data sets demonstrate better denoising performance of the proposed method compared to first-order DTV and conventional high-order TV methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Taghi Shirzad, Mahsa Safarkhani, Marcelo S. Assumpcao
Summary: This study proposes a weighted root-mean-square stacking method based on the analysis of synthetic cross-correlation functions to extract seismic signals from ambient seismic noise. The results demonstrate that this method can accurately extract the seismic signals and provide important parameters for the Earth's internal structure.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2022)
Article
Geochemistry & Geophysics
Weilin Huang, Xiaoyu Chuai, Ying Rao, Baoyu Li
Summary: This letter proposes a Shannon entropy-based method to measure seismic local correlation and enhance the correlation of seismic events. The method, derived from the local plane-wave model, is formulated as a Shannon entropy form and calculates probabilities along the coordinate direction of local slope. The application to both synthetic and field data sets demonstrates the superior performance of this method over traditional alternatives.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Yuxing Zhao, Yue Li, Shaoping Lu, Xintong Dong, Ning Wu
Summary: Noise reduction is crucial in seismic data processing due to the complex geological conditions and acquisition environments, which pose challenges with spatial and temporal variability in noise levels. An innovative generic denoising network is introduced in this article, allowing for artificial adjustment of denoising levels to prevent over- or underdenoising.
Article
Geochemistry & Geophysics
Liuqing Yang, Wei Chen, Hang Wang, Yangkang Chen
Summary: The proposed adaptive random noise attenuation framework based on convolutional neural networks transforms the target function to noise learning through residual learning to improve training efficiency and allows for unsupervised noise reduction. The network architecture includes techniques such as residual learning and batch normalization to reduce training parameters and shorten feature learning time, leading to more effective noise reduction and seismic waveform reconstruction compared to classic denoising algorithms.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Fengyuan Sun, Guisheng Liao, Yihuai Lou, Xing Jiang
Summary: Random noise elimination is crucial in seismic data processing, alongside preserving and recovering useful subsurface structure information. This study utilizes S-mean as a nonlinear filter for seismic denoising, obtaining the geometric mean of seismic traces on the SPD matrix manifold. By optimizing the search for S-mean on the SPD manifold, the best correlation with other elements based on S-divergence is achieved, effectively compensating for and maintaining broken correlation features in noisy seismic data, facilitating the description of subsurface structures. Quality and quantitative evidence from synthetic examples and field data applications validate the proposed workflow's effectiveness and validity.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Review
Geosciences, Multidisciplinary
Mathieu Le Breton, Noelie Bontemps, Antoine Guillemot, Laurent Baillet, Eric Larose
Summary: Monitoring landslides is crucial for understanding their dynamics and reducing human casualties. The ambient noise correlation method has shown potential in detecting precursor signals before landslide failures, but challenges remain in detecting velocity changes rapidly and confidently, accounting for environmental influences, and ensuring measurement stability. Seasonal velocity fluctuations are influenced by groundwater levels and soil conditions, posing challenges for early-warning systems. Daily fluctuations and spurious velocity changes unrelated to material dynamics should also be considered for accurate landslide monitoring.
EARTH-SCIENCE REVIEWS
(2021)