Journal Article10.1785/0220220242
Pyseistr: A Python Package for Structural Denoising and Interpolation of Multichannel Seismic Data
YangQuan Chen,Alexandros Savvaidis,S. Fomel,Yunfeng Chen,O.M. Saad,Yapo Abolé Serge Innocent Oboué,Quan Zhang,Wei Chen +7 more
6
TL;DR: Pyseistr as discussed by the authors is a Python package that is designed to make full use of the structural patterns in multichannel seismic data to facilitate the data processing, including slope estimation, structural mean and median filtering and structural reconstruction of missing data.
read more
Abstract:
New sensing techniques like the nodal geophones and distributed acoustic sensing enable a spatial sampling ratio that was unprecedentedly high in earthquake seismology. The much higher sampling of seismic wavefields that is close to the level in exploration seismology calls for a unified processing approach for multichannel seismic data regardless of the research interest, for example, oil and gas oriented or earthquake-study oriented. Here, we present the first Python package for multichannel seismic data that benefits both communities, that is, exploration and earthquake seismology, called Pyseistr. The Pyseistr is a Python package that is designed to make full use of the structural patterns in multichannel seismic data to facilitate the data processing. The Pyseistr package currently includes several fundamental functions like slope estimation, structural mean and median filtering, and structural reconstruction of missing data. The Pyseistr package is continuously developed to include more functions that benefit both exploration and earthquake communities.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
An advanced median filter for improving the signal-to-noise ratio of seismological datasets
Yapo Abolé Serge Innocent Oboué,Yunfeng Chen,Sergey Fomel,Wei Zhong +3 more
- 01 Nov 2023
TL;DR: This study introduces MATamf, an open-source MATLAB package using a novel advanced median filter (AMF) to improve signal-to-noise ratio in seismological datasets by combining multiple denoising operators to simultaneously attenuate various types of noise.
6
U-Net++ based subshallow gas scattered image conditioning: Small-scale case study of seismic data acquired in the Korean East Sea
Juan Lee,Min Je Lee,Han Gyu Park,H. Jun,Yong-June Cho +4 more
Unsupervised 3-D Seismic Erratic Noise Attenuation With Robust Tensor Deep Learning
Feng Qian,Haowei Hua,Yuhang Wen,Shengli Pan,Gulan Zhang,Guangmin Hu +5 more
TL;DR: Unsupervised 3-D seismic erratic noise attenuation with robust tensor deep learning achieves promising results by introducing a robust tensor sparse norm and optimizing the model parameters using an efficiency tensor optimization method.
Detecting local earthquakes via fiber-optic cables in telecommunication conduits under Stanford University campus using deep learning
Fantine Huot,Robert G. Clapp,Biondo Biondi +2 more
TL;DR: Fiber-optic seismic monitoring combined with deep learning enables the detection of small earthquakes in urban environments.
Detecting local earthquakes via fiber-optic cables in telecommunication conduits under Stanford University campus using deep learning
F. Huot,Robert G. Clapp,Biondo Biondi +2 more
- 11 Mar 2022
TL;DR: In this article , a convolutional neural network (CNN) was used for earthquake detection using data acquired over three years by fiber-optic cables in telecommunication conduits under Stanford University campus.
References
ObsPy: A Python Toolbox for Seismology
TL;DR: ObsPy as discussed by the authors is a Python toolbox that simplifies the usage of Python programming for seismologists by providing direct access to the actual time series, allowing the use of powerful numerical array-programming modules like NumPy (http://numpy.thz.edu/manuals/sac/Manual.html), as well as filtering, instrument simulation, triggering, and plotting.
1.4K
Applications of plane-wave destruction filters
TL;DR: This work has shown that finite‐difference plane‐wave destruction filters perform well in applications such as fault detection, data interpolation, and noise attenuation.
Shaping regularization in geophysical-estimation problems
TL;DR: Shaping regularization as discussed by the authors is a general method for imposing constraints by explicit mapping of the estimated model to the space of admissible models, which is integrated in a conjugate-gradient algorithm for iterative least-squares estimation.
Local seismic attributes
TL;DR: This work defines local attributes with the help of regularized inversion and demonstrates their usefulness for measuring local frequencies of seismic signals and local similarity between different data sets.
403
Predictive painting of 3D seismic volumes
TL;DR: Predictive painting as mentioned in this paper is a numerical algorithm that spreads information in 3D volumes according to the local structure of seismic events, thus automatically "painting" the data space.
192