# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 4 Discussion  
The results presented in Section 3, after applying the methodology described in Section 2, have allowed us to identify the current mechanisms, techniques, methods, standards, and trends that support the availability of information in acquisition and processing systems. We can more precisely answer the research questions proposed in Section 2.1 based on different topics recognized in our results. In addition, this section elaborates on related works and contributions in this paper. Research Question Analysis
* Other studies have focused on developing specific systems to guarantee the availability of information in a seismological network's rebuilding waveform with buffered data. \begin{table}
\begin{tabular}{l l} \hline \hline
**Section** & **Early Warning Trends** \\ \hline
**Article** & A39,A40,A41,A42,A43,A44,A45,A46,A47,A48,A49,A50,A51 \\ \hline \multirow{4}{*}{**Focus**} & Virtual seismologist (VS) algorithm. Convolutional neural network for earthquake detection and location (ConvNet). Seismic wave event detection algorithm (SWEDA). Software algorithms, automated information system. \\  & Compressive sensing (CS), reconstruction of seismic data challenges. \\ \hline \multirow{4}{*}{**Methods used in experimental analysis**} & \multirow{4}{*}{**Mechanism**} & EEW Bayesian approach. Efficient algorithm to reliably detect and locate earthquakes. CS challenges: (1) developing sparse realistic models for seismic data considering the noise recorded in seismic stations; (2) accurately implementing non-uniform sampling, but the acquisition geometry and time basis for both sources and receivers has to be known, as well as other aspects related to the signal-to-noise ratio (S/N). \\ \cline{2-3}  & \multirow{4}{*}{**Protocols**} & Seedlink protocol adopted for data transmission. Data exchange protocols: SeisComP, SEEDlink, Earthworm package SW InterNAQS nanometrics. \\ \cline{2-3}  & \multirow{4}{*}{**Fomats**} & Metadata formats: XML, CSV, and XSL. Waveforms of seismic vibrations: mini-SEED formats and SAC. Calibration data of recorders: SEED formats, RESP, and PZ. Catalogs of recorded seismic events: QuakeML formats. \\ \cline{2-3}  & \multirow{4}{*}{**Topologies**} & Embedded suite of SeisComp3 modules. Software application for Reftek monitoring network and RTD utilities. Accelerometer mesh for seismic prediction in rise buildings. Training sets compared with earthquake catalogs. Simulation of EEW models using recurrent neural networks. \\ \hline \multirow{4}{*}{**Application Area (Scope)**} & Monitoring in real time by seismological organizations, based on the SeisComp3 system. The ConvNetQuake neural network achieves probabilistic event detection and location using a single signal. Good performance for earthquake detection and location depends on the size of the training set. (CS) applied in sparse structure signal sparsity, randomized sampling, optimization-based signal recovery, and perspectives on applications to seismic data acquisition and processing, in contrast to the Shannon-Nyquist sampling theorem. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Early Warning Trends—Results. Nevertheless, they do not represent solutions applicable to other networks, in response to RQ1. Moreover, these solutions require a network infrastructure that allows physical redundancy and redirection of data traffic through an alternative route, but at high costs and greater complexity. * Global Seismographic Networks allow information management from affiliated seismological networks. They also provide reliability, as well as quality of service in the acquisition, processing, data storage, and analysis of information for early warnings. However, they focus on providing transmission media that guarantee the stability of data arrival for specific stations only, which will be part of the global monitoring network.