neural analysis of seismic data: applications to the monitoring of mt. vesuvius
Article Quality & Performance Metrics
Readership in this journal
EmergingRanked #144 of 205 articles by views in desalination
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 205 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
Abstract
The computing techniques currently available for the seismic monitoring allow advanced analysis. However, the correct event classification remains a critical aspect for the reliability of real time automatic analysis. Among the existing methods, neural networks may be considered efficient tools for detection and discrimination, and may be integrated into intelligent systems for the automatic classification of seismic events. In this work we apply an unsupervised technique for analysis and classification of seismic signals recorded in the Mt. Vesuvius area in order to improve the automatic event detection. The examined dataset contains about 1500 records divided into four typologies of events: earthquakes, landslides, artificial explosions, and “other” (any other signals not included in the previous classes). First, the Linear Predictive Coding (LPC) and a waveform parametrization have been applied to achieve a significant and compact data encoding. Then, the clustering is obtained using a Self-Organizing Map (SOM) neural network which does not require an a-priori classification of the seismic signals, groups those with similar structures, providing a simple framework for understanding the relationships between them. The resulting SOM map is separated into different areas, each one containing the events of a defined type. This means that the SOM discriminates well the four classes of seismic signals. Moreover, the system will classify a new input pattern depending on its position on the SOM map. The proposed approach can be an efficient instrument for the real time automatic analysis of seismic data, especially in the case of possible volcanic unrest.
| Reference Key |
esposito2013annalsneural
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | |
| Journal | desalination |
| Year | 2013 |
| DOI |
10.4401/ag-6452
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
ZincirX productsfrom the team behind Journament
-
PatentYazar
Patent drafts for the EPO, USPTO and TürkPatent
From inventor notes to a DOCX draft: description, claims, abstract. The machine drafts, the attorney signs.
See how it works (opens in a new tab) -
Projenator
Have a project idea? Score and plan it with AI
AI scores your idea in five dimensions and builds a phased plan. Your first project is free, no card needed.
Start your first project (opens in a new tab)
Cookies
We use strictly necessary cookies to run the site and keep you signed in. With your permission we would also use Google Analytics to see how the site is used, and Google AdSense to show ads on journal and article pages. Both stay off unless you accept, and you can change your mind at any time. How we use cookies · KVKK notice (Türkiye)
Cookie settings
Comments
No comments yet. Be the first to comment on this article.