# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Data Transmission Networks
\begin{table}
\begin{tabular}{p{113.8 pt} p{341.4 pt}} \hline \hline
**Section** & **Data Transmission Networks** \\ \hline
**Article** & A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11,A12,A13,A14,A15,A16 \\ \hline
**Focus** & EEW algorithm, testing and evaluation. Data delivery latency improvements. Compression algorithm for seismic data transmission. Structure of SEED format. Seismic network description and infrastructure. Energy consumption and local storage improvement. Routing protocol LWSN, WSN, seismic application \\ \hline
**Mechanism** & Virtual seismologist VS(SC3). OnSite algorithm on data-loggers, avoid collisions, compression algorithms comparison, TDMA protocols comparison, minSEED, and dataless standard. Quality parameters: real time, availability, robustness. Software SEISAN, MATLAB, RINEX data format using TQC software. (QLFR) protocol to prolong lifetime. \\ \hline
**Protocols** & TCP/IP, DART and NET, NSFNET. TDMA-based MAC protocols, depth-based routing (DBR), QLFR using Q-learning algorithms, protocols focused on beam routing (FBR), common encoding types STEIM 1 and STEIM 2 compression. Routing protocols WSN, IEEE.(802.11-802.11.4-802.15.4), ArchLink protocol (SeisComp3), RefTek RTPD server, vector-based forwarding (VBF), SeedLink protocol in data transmission. \\ \hline
**Formats** & QUakeML, SEEDLink, SEG-Y, mSEED. Algorithm: Deflate, Stein2 compression, BZip2. Common channel-naming conventions and focal mechanisms: FMHASH software. GUI of jggle software (ISTI/AQMS), routing protocol design can be exchanged with data packet transmission. Frame format of TDMA protocol, TDMA scheduling algorithms, Matlab, C++, Java, OPDMAC, Miczaz. \\ \hline
**Topologies** & Seismic networks for EEW, SCSN seismic networks, SEG-Y, MiniSEED data format and distribution, analog and digital sensor networks (UHF, digital radio links, VNS, WiFi/Hyperfan radio bridges). Regional GNSS, Osits(Aspecodagis) data-loggers, linux rankruform system. Low-power satellite-timed seismic data acquisition, mobile underwater sensor networks, WSN protocols, tools and simulators, overview of TDMA-based MAC protocol designed for large-scale cluster-based wireless sensor networks. \\ \hline
**Application Area (Scope)** & Systems for seismic early and post-event warning in a regional area. Results from seismic networks using Seiscomp3. Tuning the data-logger parameters, deploying software upgrades. Storage space optimization for data acquisition, decreasing transmission costs, and improving transmission efficiency. Dataless SEED uses: station metadata, instrument response information, and physical location. Seismic network and collaboration between regional networks. Time stamps, non-volatile memories, low-power consumption electronics, and dynamic voltage control techniques. TDMA scheduling algorithms: scheduled entity, network topology information, and entities to produce and maintain the schedules. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data Transmission Networks—Results. toward data centers in case of strong earthquakes. Regarding the compatibility of formats for data transmission, [26] has mentioned the advantages of using Dataless SEED to share metadata. Furthermore, the recent Extensible Markup Language (XML) FDSN format helps with metadata usage and compatibility with other formats. Regarding data compression in data transmission, [PERSON], [PERSON] et al. [25] used the Lempel-Ziv-Markov chain algorithm (LZMA) and the deflate algorithm to decrease the size of SEG-Y files, and, in the experimental results, showed that the improved algorithm could provide better compression and compatibility than algorithms of standards established by the Geophysical Exploration Society (SEG) and the Steim2 compression algorithm proposed by the Standard for the Exchange of Earthquake Data (SEED). [PERSON] et al. [36] used a convolutional neural network (CNN) focused on a low-compression ratio (CRs) and a signal-to-noise ratio (SNR) to improve high and moderate seismic data CRs. Their CNN model used Keras with TensorFlow backend and Google COLAB GPU.