# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Early Warning Trends  
The articles mentioned below deal with early warning trends and are related to the research questions proposed in this study. Table 7 presents a compilation of the most relevant aspects. Commonly, seismological data centers use open-source earthquake monitoring systems (e.g., Seiscomp3) for seismic data acquisition; this improves the compatibility with traditional analysis methods and these data centers require very accurate real-time results even when the network is not advanced. Some studies have proposed improvements in determining earthquake early warnings [60; 61; 62; 69], optimized algorithms, and recurrent neural networks for magnitude estimation and earthquake detection. One particular solution focused on Seiscomp3 compatibility based on the Bayesian approach and used envelopes of acceleration, velocity, and displacement as the basic data input. Another study contributed to computational performance improvement and memory usage. However, we noted the requirement of a large training set for good earthquake detection and location performance. Other studies considered gradient mapping, auto-calibration, seismic noise filtration (SNF), peak detection sequence, scaling coefficient arrays, STA/LTA arrays, and earthquake probabilistic sequence for algorithm development. In addition, other results showed a disadvantage of traditional methods for earthquake detection waves and empiric criterion, which result in false alarms decreasing the credibility of the system, allowing simulation results as well as the identification of better performance compared to traditional schemes in terms of detection, accuracy, and processing time. Studies on compressive sensing (CS) processing techniques for efficiently acquiring and reconstructing a signal were applied in [64; 74]. When it was sampled significantly below the Nyquist rate, it could retrieve data by random sampling on the signal structure in measurements that appear to be \"noise\" interference. Later, it became possible to reconstruct a coherent signal. The CS method demonstrated efficiency in image resolution, compression, adaptability, and time reduction in seismic data acquisition. Another study [65] proposes CS using random functions to combine Shannon-Nyquist frequency sampling with large-scale optimization on sparse structure signals for signal recovery and processing. The aim is to use larger data sets, speed in processing, lower bandwidth and acquisition time, and energy consumption reduction. Another advantage of CS capability was sampling below the Nyquist rate, sensing by dimensional reduction, preserving measurements, signal recovery, and reconstruction from real data. In addition, CS uses compression algorithms to transform the signals to another domain (DCT and wavelet transform). However, CS could face challenges in seismic acquisition related to the representation of variable seismic noise in different monitoring sites, sampling precision, efficiency in signal reconstruction algorithms, and signal-noise ratio. Other authors presented a review of the methods for data processing, storage, and availability of information used in volcanic seismic monitoring centers, as well as trends to decrease data gaps identified due to limitations of the acquisition systems and transmission networks, leading to possible errors in the quantification and interpretation of seismic monitoring in real time [67]. Furthermore, a review of IoT models, deep learning, and machine learning algorithms for EEW and geophysical applications summarized methods for seismic imaging [72]. Furthermore, convolutional neural networks could be applied to seismic wave simulation, velocity prediction, and density profiles used in earthquake detection, EEW, seismic tomography, and earthquake geodesy. Regarding EEW delays, researchers have developed techniques to reduce the delay time between earthquake detection and the final information delivered to the civil population.