Results 61 to 70 of about 4,442 (191)

A Convolutional Neural Network to Spiking Neural Network Conversion Framework for Seismic Denoising

IEEE Access
This study investigates the application of Spiking Neural Network (SNN) in seismic signal denoising by developing a Convolutional Neural Network (CNN) to SNN conversion framework. We focus on two challenges: optimal spike encoding strategy adaptation for
Shuna Chen, Zhege Liu, Ziyu Qin, Xinyi Liu, Yajuan Xue, Junxing Cao   +5 more
doaj   +1 more source

Hybrid Deterministic-Stochastic Methods for Data Fitting

, 2011
Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements.
Kumar S., Mark Schmidt, Michael P. Friedlander, Nedic A., Sang E., Vishwanathan S. V. N.   +5 more
core   +4 more sources

FocoNet: Transformer‐Based Focal‐Mechanism Determination

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Traditional focal‐mechanism determination primarily relies on fitting the first‐motion polarities with grid‐search algorithms. We developed a machine‐learning model, FocoNet, to include more seismic information into focal mechanism determination.
Xiaohan Song, Men‐Andrin Meier, William L. Ellsworth, Gregory C. Beroza   +3 more
wiley   +1 more source

Research on Seismic Signal Denoising Model Based on DnCNN Network

Applied Sciences
Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined ...
Li Duan, Jianxian Cai, Li Wang, Yan Shi
doaj   +1 more source

GeoFWI: A Large Velocity Model Data Set for Benchmarking Full Waveform Inversion Using Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Full waveform inversion (FWI) plays an increasingly important role in the field of seismic imaging due to its strong ability to estimate subsurface properties. Specifically, data‐driven FWI (DDFWI) establishes a straightforward mapping relationship between seismic data and the corresponding velocity model, yielding promising results.
Chao Li, Yiran Shen, Sergey Fomel, Umair Bin Waheed, Alexandros Savvaidis, Yangkang Chen   +5 more
wiley   +1 more source

From Signal to Safety: A Data-Driven Dual Denoising Model for Reliable Assessment of Blasting Vibration Impacts

Buildings
With the acceleration of urban renewal, directional blasting has become a common method for building demolition. Analyzing the time–frequency characteristics of blast-induced seismic waves allows for the assessment of risks to surrounding structures ...
Miao Sun, Jing Wu, Junkai Yang, Li Wu, Yani Lu, Hang Zhou   +5 more
doaj   +1 more source

Research on Precise Identification of Rock Strength Based on Bolt Drilling Parameters

Energy Science &Engineering, Volume 14, Issue 3, Page 1353-1371, March 2026.
Drilling detection test platform. ABSTRACT During roadway excavation, the presence of weak interlayers and fractured rock masses significantly affects roof stability. To achieve timely and effective roadway support, it is crucial to identify and predict different rock types based on drilling signals from roof bolters.
Qiang Zhu, Sheng Biao Wang, Da Jiang Wen, Chong‐Yu Song, Kang Zhou   +4 more
wiley   +1 more source

Celebrating the Physics in Geophysics

, 2005
As 2005, the International Year of Physics, comes to an end, two physicists working primarily in geophysical research reflect on how geophysics is not an applied physics.
Anderson, Anthony B. Davis, Bak, Didier Sornette, Oreskes   +4 more
core   +1 more source

Deglitching Martian Seismic Data: Application to Marsquake Detection

Earth and Space Science, Volume 13, Issue 3, March 2026.
Abstract NASA's InSight mission investigates the interior structure of Mars. The data is characterized by multiple non‐seismic signals with varying attributes, including high‐energy instrumental noise, known as glitches, which frequently exhibit large linear polarization.
Jair Zampieri, Juan I. Sabbione, Danilo R. Velis   +2 more
wiley   +1 more source

Distributed Acoustic Sensing Denoising Using a Self‐supervised Conditional Diffusion Model

Geophysical Prospecting, Volume 74, Issue 3, March 2026.
ABSTRACT Distributed acoustic sensing (DAS) data are characterized by a low signal‐to‐noise ratio due to the complex noise present in its challenging operational environment. To enhance the quality of the DAS data, we propose a self‐supervised diffusion model to attenuate the DAS noise.
Omar M. Saad, Tariq Alkhalifah
wiley   +1 more source