# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 2 Methodology Validation
### Data Analysis
The compression algorithm was developed with the Lempel-Ziv-Markov chain algorithm, providing experimental results. [PERSON] et al. [36] proposed a seismic data compression model through the convolutional neural network (CNN). The main goal was to contribute to memory optimization in transmission equipment, and to preserve seismic information for rebuilding. * Data acquisition and processing. [PERSON] et al. [39] described the most common seismic data formats; common conversion programs; standards for exchange and data storage; as well as the format structure of SEED, SAC, GSE, CSS, SEISAN, miniSEED, ASCII, ESSTF, and conversion methods. [PERSON] et al. [26] made a summary of the Standard for Exchange of Earthquake Data (SEED format), as well as the structure and advantages of dataless SEED, which was the most common format used to share metadata. [PERSON] et al. [42] developed an ad hoc application for seismic waveform analysis in a specific organization. [PERSON] et al. [40] proposed a processing workflow to improve the quality of the final processed data. The goal was to significantly decrease noise, and to recover missing signals of seismic broadband sensors. [PERSON] et al. [51] proposed a workflow for automatic fault recognition in seismic data using deep convolutional neural networks (DCNNs). It required conversion of geological project files to other formats. [PERSON] et al. [56] published the ObsPy Python library developed for seismological packages and workflows, through the integration and re-purposing of established legacy codes, using the data processing time, conversion formats, and modern workflows composed in Python. The study was proposed because some seismological tools face several hurdles to generalize into scientific Python system, such as special file formats, unknown terminology, and no suitable replacement for a non-trivial piece of software. [PERSON] et al. [46] proposed ObspyDMT Python, a software tool used for the query, retrieval, processing, and management of seismological data sets. It allowed some repetitive and complex diary seismological tasks such as data storage, preprocessing of information, instrument correction, and quality control routines. Other previous studies (e.g., [PERSON] et al. [44] and [PERSON] et al. [45]) also proposed the Python toolbox for seismology and SAC file conversion, unifying a wide variety of computing platforms, file formats, methods for accessing seismological data through information preprocessing standards, as well as libraries to access and process seismological waveform data and metadata. For data processing, seismic signal deconvolution methods were applied to improve filtering effects or attenuation at the source of seismic waves, and [PERSON] et al. [41] proposed a method to reconstruct seismic data using a relevance vector machine (RVM). Experiments were conducted on synthetic and field data sets. [PERSON] et al. [47] proposed a method to reduce random seismic noise and seismic signal attenuation using Hankel sparse low-rank approximation. Their sampling strategy was used through acquisition parameters to simulate synthetic data composed of 76 traces. A 25 Hz Ricker wavelet generated the seismic section, and the seismic noise was contaminated with white Gaussian random noise. The test results of noise attenuation were compared with the NLM, OptSLR, DRR, and OptWSST methods using land field data and synthetic seismic data. [PERSON] et al. [48] proposed an automatic picking method for multi-mode surface-wave dispersion curves with unsupervised machine learning to reduce time on human-machine interaction, improve efficiency, and increase accuracy of data processing. Seismic data were changed to 3D dispersion images through GMM clustering, DBSCAN algorithms, and filters for dispersion curves. Results were analyzed on synthetic tests and field data. [PERSON] et al. [49] developed open-source software for automatic phase detection of seismic waves using a deep learning model.