# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 2 Methodology Validation
### Data Analysis
[43] developed an Adaptable Seismic Data Format (ASDF) to store any number of synthetic, processed, or unaltered waveforms in a single file, includingcomprehensive meta-information (event or station information) in the same file. [PERSON] et al. [58] analyzed the main file structures for storing and processing seismic data in the cloud and proposed a solution that can improve real-time performance using classic standards (for example, SEG-Y) and modern formats (for example, SEG-Y and ASDF). It decreased seismic processing and helped to efficiently convert to and from SEG-Y. * Early warning trends. [PERSON] et al. [60] presented an application of the virtual seismologist (VS) algorithm for earthquake early warning (EEW). A VS algorithm was used to estimate magnitudes and ground motion in the Swiss Seismological Service and other European networks. [PERSON] et al. [61] proposed an algorithm optimization tool for earthquake detection and localization based on convolutional neural networks (ConvNetQuake) for reviewing the exponential growth of the volume of seismic data. This allowed rapid earthquake detection and location for EEW. [PERSON] et al. [62] proposed a real-time EEW event detection algorithm (SWEDA) that detects seismic wave types, using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. [PERSON] et al. [69] proposed an EEW model through recurrent neural networks (RNNs) for earthquake detection with a real-time response. The data set included 128 earthquake events collected in the Taiwan zone with 1797 seismic waveforms cut from the earthquake events. Two types of architectures were used, a common model to detect the P-wave and the S-wave characteristics, and a developed model was used to detect three targets: (1) a vector related to a number of input features, (2) LSTM cells to build the hidden layers as storage to preserve the state instance, and (3) the output layer to calculate the final probability for each category of the target events. [PERSON] et al. [64] applied compressive sensing (CS) to achieve high-efficiency data observations through seismic waveform sparseness, random sampling of observations, and data recovery of seismic waveform data. The model used two conditions: a sparse representation of data in a transform domain, and incoherence between the sampling method and sparse transform. Moreover, other authors such as [PERSON] et al. [65] used CS for digitizing signals and used more general and random test functions processed via measurements. This allowed for faster capture, sampling, and processing rates, with lower power consumption, especially in cases of larger and higher-dimensional seismic data sets. [PERSON] et al. [67] presented a review of the current information availability at seismic monitoring systems. The proposed solutions at software and network infrastructure use data recovery mechanisms through traffic control points in primary nodes and redundancy in data transmission networks to increase information availability. [PERSON] et al. [72] presented an overview of IoT models, deep learning and machine learning studies for EEW, and geophysical applications. The study suggested combination techniques for high-resolution seismic imaging based on deep learning algorithms. [PERSON] et al. [68] proposed a system of real-time earthquake detection by monitoring millions of queries related to earthquakes from an online search engine (China). The testing set was set up with the results of the MID detector (multi-internal derivative-based detection algorithm) and labelled with earthquake catalogs. [PERSON] et al. [70] developed a KD tree application for large databases to reduce EEW delays identified in the processing time and estimated real-time earthquake parameters. An offline test was made using a database with feature sets of the waveform, and it was compared with real observed seismic parameters.