Seismic attributes calculated by conventional methods are susceptible to noise. Conventional filtering reduces the noise in the cost of losing the spectral bandwidth. The challenge of having a high-resolution and robust signal processing tool motivated us to propose a sparse time-frequency decomposition while is stabilized for random noise. The procedure initiates by using Sparsity-based adaptive S-transform to regularize abrupt variations in frequency content of the nonstationary signals. Then, considering the fact that a higher amplitude of a frequency component results in a higher signal to noise ratio, an adaptive filter is applied to the time-frequency spectrum which is sparcified previously. The proposed zero adaptive filter enhances the high amplitude frequency components while suppresses the lower ones. The performance of the proposed method is compared to the sparse S-transform and the robust window Hilbert transform in estimation of instantaneous attributes by applying on synthetic and real data sets. Seismic attributes estimated by the proposed method is superior to the conventional ones in terms of its robustness and high resolution image. The proposed approach has a vast application in interpretation and identification of geological structures.
Seismotectonics and Slip Behavior of a Submarine Plate Boundary Fault from Seismicity Repeaters and Tomography using a high-resolution earthquake catalog from machine learning
Incorporating wave physical priors into diffusion models: A novel approach to seismic resolution enhancement
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；School of Petroleum, China University of Petroleum-Beijing at Karamay（中国石油大学（北京）克拉玛依校区石油学院）；School of Geography and Tourism, Zhengzhou Normal University（郑州师范学院地理与旅游学院）
Seismic data interpolation based on U-net with texture loss
School of Mathematics and Physics, China University of Geosciences（中国地质大学数学与物理学院）；Department of Computer Science, Central China Normal University（华中师范大学计算机学院）
The application of high-resolution 3D seismic data to model the distribution of mechanical and hydrogeological properties of a potential host rock for the deep storage of radioactive waste in France
Matrix imaging as a tool for high-resolution monitoring of deep volcanic plumbing systems with seismic noise
Institut Langevin, ESPCI Paris, PSL University, CNRS（朗之万研究所，巴黎高等物理化工学院，巴黎文理研究大学，法国国家科学研究中心）；Institut de Physique du Globe de Paris, Université Paris Cité, CNRS（巴黎地球物理研究所，巴黎西岱大学，法国国家科学研究中心）
Multiscale Fusion for Seismic Geometric Attribute Enhancement
在这个摘要中，我们提出了一个多尺度融合技术，以提高地震几何属性，如倾角和曲率，这是非常敏感的噪声存在于地震数据。对于给定的地震剖面，首先，我们构建一个高斯金字塔，使我们能够生成不同分辨率（尺度）的地震属性。然后，在不同的尺度上的所有属性融合在一起，形成建议的多尺度增强属性。新西兰Great South Basin三维地震数据集的应用表明，与现有方法和算法相比，该方法能够提高一阶倾角和二阶曲率属性的分辨率和噪声鲁棒性。这种改进表明了我们的多尺度融合技术在提高多道地震属性（如相干性、挠曲性和灰度共生矩阵）质量方面的巨大潜力。
Toward Forecasting Volcanic Eruptions using Seismic Noise
在喷发期间，岩浆加压产生微妙的变化，火山机构的弹性性能。我们使用记录在Piton de la Fournaise火山上的环境地震噪声的再现性特性来测量小于0.1%的相对地震速度变化，时间分辨率为一天。我们的研究结果表明，五个研究的火山爆发之前，明显可检测的地震速度下降的岩浆注入区。这些前兆反映了岩浆加压引起的沉积扩张，可以作为改进火山喷发预测的有用指标。
TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Earth and Space Sciences, University of Science and Technology of China（中国科学技术大学地球和空间科学学院）；Department of Earth and Planetary Sciences, Harvard University（哈佛大学地球与行星科学系）；Institute of Earthquake Forecasting, China Earthquake Administration（中国地震局地震预报研究所）
Seismic P-wave attenuation estimation based on frequency-dependent AVO using Kramers-Kronig relations for gas reservoir prediction
Chengdu University of Technology（成都理工大学）；State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation（油气藏地质及开发工程国家重点实验室）；Key Laboratory of Earth Exploration & Information Techniques of Ministry of Education（教育部地球探测与信息技术重点实验室）
Accurate Interpolation of Ambient Noise Empirical Green's Functions by Denoising Diffusion Probabilistic Model and Implicit Neural Representation
University of Science and Technology of China（中国科学技术大学）；Mengcheng National Geophysical Observatory, University of Science and Technology of China（蒙城国家地球物理观测台，中国科学技术大学）；Anhui Provincial Key Laboratory of Subsurface Exploration and Earthquake Hazard Risk Prevention（安徽省地下探测与地震灾害风险预防重点实验室）
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；China National Petroleum Corp Bureau of Geophysical Prospecting Inc, Geophysical Technology Research Center（中国石油集团地球物理勘探局有限公司地球物理技术研究中心）
