Remote Sensing and Geological Disasters

Topic Information

Dear Colleagues,

Geological disasters pose significant challenges to human engineering activities, such as the development of underground coal, shale gas, and geothermal resources, as well as the construction of tunnels, bridges, and hydropower stations. These activities often involve complex subsurface environments and stress conditions, necessitating a comprehensive exploration of the underlying principles to address geotechnical engineering problems. The integration of remote sensing technology has become vital for monitoring and analyzing geological disasters, offering real-time data and enhanced predictive capabilities.

By considering various influencing factors and geological characteristics, researchers can explore the complex behaviors in these applications. The advent of network information, big data, and intelligent technologies has provided new methods for studying and mitigating geological disasters. Remote sensing technology, in particular, has emerged as a critical tool for understanding geological hazards and improving the effectiveness of mitigation strategies.

We welcome submissions of cutting-edge reviews, scientific problem analyses, engineering case reports, and other papers related to remote sensing and geological disasters. Additionally, we encourage the submission of papers exploring innovative research methods and new engineering solutions in these fields. Topics of interest include, but are not limited to, the following:

• Remote sensing technology and application in rock mass landslides;
• Integration of geospatial data and engineering geology for disaster risk reduction;
• Advanced remote sensing methods for real-time geohazard assessment;
• Applications of remote sensing in hydrological studies and water resource management related to geological disasters;
• Remote sensing in the analysis of soil and land degradation;
• Thermo-hydro-mechanical coupled model for geological structures;
• Methods and theories for assessing geological stability;
• Slope engineering modeling and landslide disaster prediction methods;
• Application technology of intelligence in geological research;
• Rock structure description and mechanical constitutive equations;
• Failure laws, criteria, and mechanisms of geological materials under high in situ stress;
• Damage, crack initiation, and propagation mechanisms of geological materials under coupled multi-field conditions;
• Mechanical properties of soft and hard geological materials and their mechanisms of deformation. This Topic aims to bridge the gap between remote sensing and geological disaster mitigation, promoting interdisciplinary research and innovative solutions. We look forward to your valuable contributions to this exciting and impactful field.

Dr. Gan Feng
Prof. Dr. Qiao Lyu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
GeoHazards geohazards	-	2.6	2020	20.4 Days	CHF 1000	Submit
Geosciences geosciences	2.4	5.3	2011	26.2 Days	CHF 1800	Submit
Land land	3.2	4.9	2012	17.8 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit

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Published Papers (1 paper)

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All Applied Sciences GeoHazards Geosciences Land Remote Sensing

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15 pages, 10808 KiB  

Open AccessArticle

A Strong Noise Reduction Network for Seismic Records

by Tong Shen, Xuan Jiang, Wenzheng Rong, Lei Xu, Xianguo Tuo and Guili Peng

Appl. Sci. 2024, 14(22), 10262; https://doi.org/10.3390/app142210262 - 7 Nov 2024

Viewed by 391

Abstract

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. [...] Read more.

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

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