Results 101 to 110 of about 1,182 (196)

Deep Learning-Based Blind Denoising for Distributed Acoustic Sensing Seismic Data With Self-Supervised and Transfer Learning

Photonic Sensors
A distributed acoustic sensing (DAS) technology, extensively utilized in the seabed geological exploration, ocean current analysis, and marine seismic monitoring, faces challenges due to the presence of various noise types in sensing signals, which ...
Tianrui LI, Zhengyong LIU, Qi SUI, Chao LU, Jun HAN, Shaoyi CHEN, Zhaohui LI   +6 more
doaj   +1 more source

Evaluating Scalograms for Seismic Event Denoising

, 2021
Phillip Lewis, Antonio Gonzales, Patrick Hammond   +2 more
openaire   +2 more sources

High Resolution Seismic Waveform Generation Using Denoising Diffusion

Journal of Geophysical Research: Machine Learning and Computation
Abstract Accurate prediction and synthesis of seismic waveforms are crucial for seismic‐hazard assessment and earthquake‐resistant infrastructure design. Existing prediction methods, such as ground‐motion models and physics‐based wavefield simulations, often fail to capture the full complexity of seismic ...
Kadek Hendrawan Palgunadi, Andreas Bergmeister, Andrea Bosisio, Laura A. Ermert, Maria Koroni, Nathanaël Perraudin, Simon Dirmeier, Men‐Andrin Meier   +7 more
openaire   +2 more sources

Introduction to NumPy and Its Application in Seismic Data Denoising [PDF]

<p>This page contains all the PPT, .IPYNB, pdf and .NPY files for the workshop organized under Digital Scholarship Days 2024. The workshop discusses introduction to NumPy, high resolution seismic data used in hydrocarbon exploration, ML-based ...
Dhanapal, Syadhisy
core   +1 more source

Self-Supervised Seismic Random Noise Suppression With Higher-Quality Training Data Based on Similarity Differences

IEEE Access
Suppressing random noise and improving the signal-to-noise ratio of seismic data holds immense significance for subsequent high-precision processing. As one of the most widely used denoising methods, self-learning-based algorithms typically partition the
Jian Gao, Zhenchun Li, Min Zhang, Wanyue Gao, Yixuan Gao   +4 more
doaj   +1 more source

Research on High-Density Discrete Seismic Signal Denoising Processing Method Based on the SFOA-VMD Algorithm

Geosciences
With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths
Xiaoji Wang, Kai Lin, Guangzhao Guo, Xiaotao Wen, Dan Chen   +4 more
doaj   +1 more source

Application of a Fractional Laplacian-Based Adaptive Progressive Denoising Method to Improve Ambient Noise Crosscorrelation Functions

Fractal and Fractional
Extracting high-quality surface wave dispersion curves from crosscorrelation functions (CCFs) of ambient noise data is critical for seismic velocity inversion and subsurface structure interpretation. However, the non-uniform spatial distribution of noise
Kunpeng Yu, Jidong Yang, Shanshan Zhang, Jianping Huang, Weiqi Wang, Tiantao Shan   +5 more
doaj   +1 more source

A Real Benchmark Swell Noise Dataset for Performing Seismic Data Denoising via Deep Learning [PDF]

The recent development of deep learning (DL) methods for computer vision has been driven by the creation of open benchmark datasets on which new algorithms can be tested and compared with reproducible results.
Evsukoff, Alexandre G., Barros, Pablo M., Arboleda, Giovanny A. M., Bulcão, André, Valente, Lessandro de S. S., de Melo, Isabelle R. V., Netto, Sergio L., Sardinha, Roosevelt de L., Aveleda, Albino   +8 more
core   +1 more source

Seismological data acquisition and signal processing using wavelets [PDF]

, 2009
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete ...
Hloupis, Georgios P
core  

Neural Networks for Seismic Data Denoising: Attention Mechanisms and Diffusion Models [PDF]

Seismic data, which is crucial for understanding the Earth’s subsurface structure, is frequently compromised by incoherent and coherent noise, complicating accurate geological imaging and thus making noise suppression one of the most important processing
Knispel, Stefan
core