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Advances in Seismic Sensing and Monitoring
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Advances in Seismic Sensing and Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 3392

Special Issue Editors

Dr. Qiancheng Liu

E-Mail Website
Guest Editor
Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
Interests: seismic imaging; seismic tomography; inverse problems
Special Issues, Collections and Topics in MDPI journals
Dr. Giovanni Leucci

E-Mail Website
Guest Editor
Istituto di Scienze del Patrimonio Culturale, Institute of Cultural Heritage Sciences and Sites (ISPC), Consiglio Nazionale delle Ricerche, National Research Council (CNR), 73100 Lecce, Italy
Interests: applied geophysics; ground-penetrating radar; electrical resistivity tomography; seismic tomography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, seismic imaging and tomography techniques have undergone significant advancements, enhancing our ability to probe the Earth's subsurface and understand its complex structure. These methods, coupled with sophisticated inverse problem solving techniques, now allow for more accurate and detailed mapping of geological features and processes.

This Special Issue of Sensors explores the latest developments in seismic sensing and monitoring, focusing on how cutting-edge technologies are revolutionizing our approach to seismic imaging and tomography. From advanced algorithms to novel sensor designs, we highlight the innovations that are pushing the boundaries of what is possible in seismic imaging. Join us as we delve into the exciting world of seismic sensing and its transformative impact on Earth science research and practical applications.

Dr. Qiancheng Liu
Dr. Giovanni Leucci
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • seismic sensing
  • seismic monitoring
  • seismic imaging and tomography techniques

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Published Papers (2 papers)

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Research

21 pages, 4706 KB  
Article
Near-Real-Time Integration of Multi-Source Seismic Data
by José Melgarejo-Hernández, Paula García-Tapia-Mateo, Juan Morales-García and Jose-Norberto Mazón
Sensors 2026, 26(2), 451; https://doi.org/10.3390/s26020451 - 9 Jan 2026
Cited by 3 | Viewed by 826
Abstract
The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish [...] Read more.
The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article
(This article belongs to the Special Issue Advances in Seismic Sensing and Monitoring)
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21 pages, 28441 KB  
Article
Seismic Risk Classification of Building Clusters Using MST Clustering and UAV Remote Sensing
by Xianteng Wang, Xue Li, Zhumei Liu, Zihao Wu, Yike Xie and Zijie Han
Sensors 2025, 25(3), 744; https://doi.org/10.3390/s25030744 - 26 Jan 2025
Cited by 2 | Viewed by 1839
Abstract
The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity [...] Read more.
The fundamental attribute that is essential for the seismic capacity assessment of houses is the building structure type. Conventionally, remote sensing assessment of the seismic capacity for houses has been based on the image features of individual houses, instead of the spatial similarity between them. To enhance the classification accuracy of house structure types, this work proposes a minimum spanning tree (MST) house clustering structure type classification method based on the spatial similarity of houses. First, the method employs the geometric characteristics of residential buildings to calculate the Gestalt factor that characterizes the visual distance. Subsequently, a Delaunay triangular mesh is constructed to create a proximity map between the houses, with the MST generated using visual distance as the weighting factor. Then, the spatial proximity similarity of house clusters is obtained through pruning. Finally, a support vector machine is employed to categorize the architectural structure of the housing complex, viz., simple houses, brick–concrete houses, and frame houses. This classification is based on the geometric, textural, height, and spatial distribution characteristics of the houses. We have conducted a remote sensing classification experiment of house structure types with Zhushan County, Hubei Province as the study area. The results show that the MST clustering method improves the classification accuracy of brick–concrete houses to 95.4% and the classification accuracy of simple houses to 93.4%. Compared to the single-family-based classification method of building structure types, the classification accuracy of frame-structure buildings is improved to 87%. The Kappa coefficient increased to 0.89. This study significantly improves the classification accuracy of building structure types by introducing spatial similarity. Furthermore, it shows the potential for spatial similarity in classifying building structure types. Full article
(This article belongs to the Special Issue Advances in Seismic Sensing and Monitoring)
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