Evidence map›Paper›PMID 37300062›Full record

ReviewSensors (Basel, Switzerland)2023

Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors.

Bingwei Tian, Wenrui Liu, Haozhou Mo, Wang Li, Yuting Wang, Basanta Raj Adhikari

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bingwei TianInstitute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu 610207, China.ORCID 0000-0001-9031-3307
Wenrui LiuSichuan University-Pittsburgh Institute, Sichuan University, Chengdu 610065, China.
Haozhou MoSichuan University-Pittsburgh Institute, Sichuan University, Chengdu 610065, China.
Wang LiSichuan University-Pittsburgh Institute, Sichuan University, Chengdu 610065, China.
Yuting WangInstitute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu 610207, China.
Basanta Raj AdhikariInstitute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu 610207, China.ORCID 0000-0001-8156-0875

Funding

The National Bureau of Foreign Expert DL2022164001L
6 · The paper itself

Abstract

The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, the physical or chemical mechanisms of sensors, and the location of sensor platforms. In this study, we analyzed available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.

Indexed as

Disaster PlanningDisastersEarthquakesearly warningearthquake DRRmonitoringsearch and rescuesensors

Identifiers

PMID37300062
PMCPMC10256000

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.