The exponential growth of the size of seismic data recorded in seismic surveys and real time data monitoring makes seismic data compression strongly demanded. Furthermore, compression will lead to an efficient use of the bandwidth assigned for the communication link between the seismic stations and the main center. In this paper, two convolutional autoencoders (CAEs) are proposed for seismic data compression. The two algorithms are mainly based on the convolutional neural network (CNN), which has the capability to compress the seismic data into feature representations, thereby allowing the decoder to perfectly reconstruct the input seismic data. The results show that the first model is efficient at low compression ratios (CRs), while the second model improves the signal-to-noise ratio (SNR) from about 3 dB to 12 dB compared to the other benchmark algorithms at moderate and high CRs.

Ultra-low frequency (ULF) signals in the geomagnetic records are important indicators for many phenomena; therefore identification of such signals is an important issue. Automatic identification of these ULF signals is not an easy target because of their small magnitudes. Through this study in hand, two algorithms are proposed to automatically detect these micro-pulsations. The first algorithm uses the multi-level components (details) of the discrete wavelet transform (DWT) instead of the original geomagnetic record. The vector of the maximum values of the cross-correlation between the record and an arbitrary chosen ULF pattern in the same frequency range is a good indicator for the existence of these micro-pulsations. The second algorithm is based on convolutional neural network (CNN) framework guided with the multi-resolution-analysis (MRA) of the DWT. Preprocessing the geomagnetic records using the MRA of DWT to produce the fifth and the sixth details to be the input to the deep CNN topology, highly improved the accuracy to approach 91.11%. In addition, deep learning based algorithm showed better results than the DWT based algorithm in light of all the performance metrics.

P-wave picking is one of the important steps for earthquake parameter determination. Although manual inspection of P-wave arrival timing is the most accurate method for detection but, using automated algorithm is a must in order to facilitate this continuous task. In this research article an automated real-time algorithm is introduced to determine P-wave arrival timing of multiple of stations in a sub-network of the Egyptian National Seismic Network (ENSN). This algorithm generates a daily report of all events recorded by this sub-network. The main advantage of this proposed algorithm is its small processing time to give accurate P-wave arrival timings. This merit had been invested to make a proposal for Earthquake Early Warning (EEW) system using this algorithm. Additionally, this algorithm can detect very small events starting from micro-earthquakes due to the use of multiresolution analysis (MRA) of discrete wavelet transform (DWT). Results show a high rate of successful detections of 94.6%with low false alarm rate.

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence.