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Sensors
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Acquisition and Processing of Seismic Signals
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Acquisition and Processing of Seismic Signals

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2515

Special Issue Editors

Prof. Dr. Juan Jose Galiana-Merino

E-Mail Website
Guest Editor
Department of Physics, Systems Engineering and Signal Theory, University of Alicante, Crta. San Vicente del Raspeig, s/n, 03080 Alicante, Spain
Interests: seismology; seismic data acquisition; signal processing; near surface geophysics; wavelets
Special Issues, Collections and Topics in MDPI journals
Dr. Boualem Youcef Nassim Benabdeloued

E-Mail Website
Guest Editor
Department of Physics, Systems Engineering and Signal Theory, University of Alicante, Ctra. San Vicente del Raspeig, s/n, 03080 Alicante, Spain
Interests: geophysical study of a large landslide; affecting the urban area of albuñuelas (s spain)

Special Issue Information

Dear Colleagues,

We are inviting original research works covering novel seismic data acquisition systems (including sensors, digitizers, and sensor networks). It includes distributed acoustic sensing (DAS), infrasound sensors, MEMS sensors, broadband and nodal arrays, etc.

Data processing of seismic signals is also crucial, especially for seismic networks, which continuously monitor and work with huge volumes of data. Additionally, new signal processing methods applied to operational earthquake forecasting (OEF), combining seismic and non-seismic signals, and earthquake early warning (EEW) systems are also included. In this sense, new methodologies based on energy analysis, artificial neural networks, maximum likelihood methods, fuzzy logic theory, polarization analysis, hidden Markov models, autoregressive techniques, higher order statistics, wavelet transform, or template matching, among others, are continuously being investigated.

Prof. Dr. Juan Jose Galiana-Merino
Dr. Boualem Youcef Nassim Benabdeloued
Guest Editors

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 data acquisition
  • seismic sensors
  • seismic recorders
  • seismic networks
  • real-time seismic signal processing
  • wavelet processing
  • methodologies based on artificial intelligence

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Published Papers (2 papers)

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Research

11 pages, 1505 KB  
Article
Accelerated Full Waveform Inversion by Deep Compressed Learning
by Maayan Gelboim, Amir Adler and Mauricio Araya-Polo
Sensors 2026, 26(6), 1832; https://doi.org/10.3390/s26061832 - 13 Mar 2026
Viewed by 523
Abstract
We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength case is in the teraflop [...] Read more.
We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as a computational cost mitigation approach. Given modern seismic acquisition systems, the data (as an input for FWI) required for an industrial-strength case is in the teraflop level of storage; therefore, solving complex subsurface cases or exploring multiple scenarios with FWI becomes prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning seismic acquisition layouts from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning, an autoencoder computes latent representations of the shot gathers, followed by K-means clustering of the latent representations to further select the most relevant shot gathers for FWI. This approach can effectively be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, and these results pave the way to accelerating FWI in large scale 3D inversion. Full article
(This article belongs to the Special Issue Acquisition and Processing of Seismic Signals)
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19 pages, 14443 KB  
Article
Robust Phase Association and Simultaneous Arrival Picking for Downhole Microseismic Data Using Constrained Dynamic Time Warping
by Tuo Wang, Limin Li, Shanshi Wen, Yiran Lv, Zhichao Yu and Chuan He
Sensors 2026, 26(1), 114; https://doi.org/10.3390/s26010114 - 24 Dec 2025
Cited by 1 | Viewed by 635
Abstract
Accurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable propagation paths, and ambient noise, poses significant challenges to conventional automatic picking methods, [...] Read more.
Accurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable propagation paths, and ambient noise, poses significant challenges to conventional automatic picking methods, even when the signal-to-noise ratio (SNR) is moderate to high. Specifically, P-wave coda energy can obscure S-wave onsets analysis, and shear wave splitting can generate ambiguous arrivals. In this study, we propose a novel multi-channel arrival picking framework based on Constrained Dynamic Time Warping (CDTW) for phase identification and simultaneous P- and S-wave arrival estimation. The DTW algorithm aligns microseismic signals that may be out of sync due to differences in timing or wave velocity by warping the time axis to minimize cumulative distance. Time delay constraints are imposed to ensure physically plausible alignments and improve computational efficiency. Furthermore, we introduce a Multivariate CDTW approach to jointly process the three-component (3C) data, leveraging inter-component and inter-receiver arrival consistency across the entire downhole array. The method is validated against the Short-Term Average/Long-Term Average (STA/LTA) and waveform cross-correlation techniques using field data from a shale gas hydraulic fracturing. Results demonstrate that the proposed algorithm significantly enhances arrival time accuracy and inter-receiver consistency, particularly in scenarios involving P-wave coda interference and shear wave splitting. Full article
(This article belongs to the Special Issue Acquisition and Processing of Seismic Signals)
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