# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Data Acquisition and Processing
Solutions based on the relevance vector machine (RVM) method are applied to seismic signals to decrease filtering effects and gaps in seismic waveforms, as shown by aliasing [41]. This allowed greater effectiveness instead of applying common algorithms with the compressive sensing (CS) framework for data reconstruction that did not consider uncertainty quantification or feature learning. Other methods used were Hankel sparse low-rank approximation [47], where the attenuation of seismic signal noise in data processing was probed as the main factor that affects the accuracy and quality of final seismic images and weakens complex traces. Other studies have developed ad hoc software tools to improve seismic data analysis and data processing. With the adoption of different formats (SEED, SAC, ASCII, Y-Nanometric, GSE, and others), these solutions should be adaptable to other seismology applications [40; 42]. Furthermore, these solutions helped to cover functionalities for waveform analysis, such as genetic algorithm, least-square fitting, auto-picking, fast Fourier transforms, location, attenuation, focal mechanisms, waveform, modeling techniques for rapid estimation of earthquake source parameters, and others. Other authors [51] used deep convolutional neural networks to convert geological project files to a data format suitable for deep learning with processing, analysis, model evaluation, and comparative results. The main disadvantage is a large amount of data for training and automatic seismic interpretation. Furthermore, [44; 56] have proposed a framework for processing seismological data in the ObsPy Python library for seismology geared toward processing, and specifically on-time series analysis with file compatibility and conversion formats. Moreover, ObsPy can save fragments of data separately, which reduces the overlaps produced by clock time lag during data recording, as well as the gaps caused by interruptions in data transfer [45]. The functionality of the ObsPy library has also been mentioned in the processing of data and metadata for seismic signals, as well as the graphical user interface [46]. However, the ObsPy functions are used to complement post-acquisition and analysis, and it is not focused on data analysis in real time. Regarding methods to reduce seismic data processing time, unsupervised machine learning processes were designed [48]. For this purpose, seismic data were converted into 3D dispersion images. Methods used were Gaussian mixture model (GMM) clustering for dispersion image points and background noise points. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm allowed discrimination modes of dispersion energy points. Noise interference was reduced with a particle filter to smooth the picked dispersion curves. Other solutions were focused on open-source software development using deep learning models [49]. The goal was to improve the accuracy of the seismic waveform arrival detection system and to reduce the manual selection procedure for seismic phase arrival times. The method applied for automatic seismic wave and phase detection was based on the PhaseNet neural network. Regarding seismic data reconstruction, several processes point to convolutional neural networks, based on computer vision and imaging processing to identify bad or missing traces, which can cause problems for seismic data processing. However, ML techniques and (RNN) algorithms perform seismic trace interpolation. As an alternative solution [50], the neural network (RNN) algorithm model was used to identify sequences of time-series data through deep bidirectional long short-term memory (LSTM), and test models with and without skip connections. However, the proposed model was only tested on a simple experimental combination of input and output traces for prediction and interpolation.