Add this article to private library Remove from private library Submit corrections to this record View record in the new ADS The book presents an introductory treatment of digital and analog communication systems with emphasis on digital systems. Attention is given to the following topics: systems and signal analysis, random signal theory, information and channel capacity, baseband data transmission, analog signal transmission, noise in analog communication systems, digital carrier modulation schemes, error control coding, and the digital transmission of analog signals. Bibtex entry for this abstract Preferred format for this abstract

Digital and analog communication systems

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. The network uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with an F-score of 99.95. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. The model is applied to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region. The performance of the model is compared with STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.

CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection

Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks and underlies most seismicity catalogs produced around the world. This task can be challenging because the number of sources is unknown, events frequently overlap in time, or can occur simultaneously in different parts of a network. We present PhaseLink, a framework based on recent advances in deep learning for grid‐free earthquake phase association. Our approach learns to link phases together that share a common origin and is trained entirely on millions of synthetic sequences of P and S wave arrival times generated using a 1‐D velocity model. Our approach is simple to implement for any tectonic regime, suitable for real‐time processing, and can naturally incorporate errors in arrival time picks. Rather than tuning a set of ad hoc hyperparameters to improve performance, PhaseLink can be improved by simply adding examples of problematic cases to the training data set. We demonstrate the state‐of‐the‐art performance of PhaseLink on a challenging sequence from southern California and synthesized sequences from Japan designed to test the point at which the method fails. For the examined data sets, PhaseLink can precisely associate phases to events that occur only ∼12 s apart in origin time. This approach is expected to improve the resolution of seismicity catalogs, add stability to real‐time seismic monitoring, and streamline automated processing of large seismic data sets.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018JB016674

PhaseLink: A Deep Learning Approach to Seismic Phase Association

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as −1.3 ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.

/pdf/cred-a-deep-residual-network-of-convolutional-and-recurrent-53emw73ljc.pdf

A novel compressive sensing (CS) algorithm for synthetic aperture radar (SAR) imaging is proposed which is called as the two-dimensional double CS algorithm (2-D-DCSA). We first derive the imaging operator for SAR, which is named as the chirp-scaling operator (CSO), from the chirp-scaling algorithm (CSA), then we show its inverse is a linear map, which transforms the SAR image to the received baseband radar signal. We show that the SAR image can be reconstructed simultaneously in the range and azimuth directions from a small number of the raw data. The proposed algorithm can handle large-scale data because both the CSO and its inverse allow fast matrix-vector multiplications. Both the simulated and real data are processed to test the algorithm and the results show that the 2-D-DCSA can be applied to reconstructing the SAR images effectively with much less data than regularly required.

http://ir.nssc.ac.cn/bitstream/122/2783/4/201472708.pdf

A Novel Compressive Sensing Algorithm for SAR Imaging

Azimuth ambiguity may introduce false targets into synthetic aperture radar images, particularly likely in inshore and oceanic observation. To suppress the azimuth ambiguities for any acquisition mode, a new model is developed to describe the impact of spatially variant azimuth antenna pattern weighting on azimuth ambiguities. By accurately estimating the ratio of ambiguous to main zone energy based on the model, the proposed algorithm selects the subspectra with less ambiguous disturbance, and adopts extrapolation with weighted energy measure to obtain a full spectrum. Due to spectral selection and extrapolation, the novel algorithm achieves superior performance in azimuth ambiguity suppression and resolution preservation, which is compared with the classical algorithm, and validated by applying TerraSAR-X and RADARSAT-2 images.

Suppression of Azimuth Ambiguities in Spaceborne SAR Images Using Spectral Selection and Extrapolation

Compressive sensing (CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements. This paper proposes a novel SAR range compression, namely compressive sensing with chirp scaling (CS-CS), achieving the same range resolution as conventional SAR approach, while using fewer range samplings. In order to realize accurate range cell migration correction (RCMC), chirp scaling principle is used to construct reference matrix for compressive sensing recovery. Additionally, error diagrams are designed for measurement of the performance of CS-CS, and some experiments of using real data are performed to deal with the errors caused by three conditions: SNR, sparsity and sampling.

Compressive sensing SAR range compression with chirp scaling principle

Synthetic aperture radar (SAR), with its all-weather and day/night capabilities, plays an important role in Earth observation. Traditionally, SAR satellites fly in low Earth orbits, which can result in fast Doppler accumulation but long revisiting intervals. Although increasing the number of satellites extends the observation coverage significantly, a complex constellation leads to unacceptable cost. The geosynchronous SAR system realizes sustained observations for a specific area; however, the long integral time results in the obvious defocusing of objects, even in micro-motion. To achieve a fast response and a short revisiting time with high accuracy and low costs, a constellation of imaging radar satellites called Constellation of Geostationary and Low Earth Orbit SAR (ConGaLSAR) is proposed in this letter; ConGaLSAR employs a novel transponding mode (MirrorSAR) to economically achieve efficient revisiting and phase/time synchronizations. In this system, the ground echoes are amplified by the low-orbit satellites and then retransmitted back to the illuminating system for sampling and downlinking. In view of the specific geometry of the system, the sensitivity, resolutions, and effective observing area are discussed and precisely defined. Based on the theoretical analyses, a typical case of ConGaLSAR, consisting of one geostationary orbit illuminator and 24 low-orbit transponders, can achieve a 92.4-min revisiting interval and 3-m resolution for the Pacific Ocean.

ConGaLSAR: A Constellation of Geostationary and Low Earth Orbit Synthetic Aperture Radar

The traditional radar system needs large bandwidth, and the increasing number of channels brings huge amount of data. These data can easily overflow the memory of the sensor or the bandwidth of the signal which transferred to the ground station. In order to solve this problem, a new method of acquiring Synthetic Aperture Radar (SAR) raw data and compressing pulse which based on the theory of Compressive Sensing (CS) theory are presented. In this method, CS SAR imaging is affected by noise, sampling rate and the sparsity of signal. Furthermore, Donoho-Tanner phase transition diagram is applied to show the performance of CS pulse compression. Engineers can intuitively find the scene and the sampling rate which is suitable for using compressed sensing synthetic aperture radar pulse compression.

Effects of noise, sampling rate and signal sparsity for compressed sensing Synthetic Aperture Radar pulse compression

Compared with low-Earth orbit synthetic aperture radar (SAR), a geosynchronous (GEO) SAR can have a shorter revisit period and vaster coverage. However, relative motion between this SAR and targets is more complicated, which makes range cell migration (RCM) spatially variant along both range and azimuth. As a result, efficient and precise imaging becomes difficult. This paper analyzes and models spatial variance for GEO SAR in the time and frequency domains. A novel algorithm for GEO SAR imaging with a resolution of 2 m in both the ground cross-range and range directions is proposed, which is composed of five steps. The first is to eliminate linear azimuth variance through the first azimuth time scaling. The second is to achieve RCM correction and range compression. The third is to correct residual azimuth variance by the second azimuth time-frequency scaling. The fourth and final steps are to accomplish azimuth focusing and correct geometric distortion. The most important innovation of this algorithm is implementation of the time-frequency scaling to correct high-order azimuth variance. As demonstrated by simulation results, this algorithm can accomplish GEO SAR imaging with good and uniform imaging quality over the entire swath.

/pdf/correcting-spatial-variance-of-rcm-for-geo-sar-imaging-based-2qycdbcv6f.pdf

Peng Xiao

Papers

Suppression of Azimuth Ambiguities in Spaceborne SAR Images Using Spectral Selection and Extrapolation

Compressive sensing SAR range compression with chirp scaling principle

ConGaLSAR: A Constellation of Geostationary and Low Earth Orbit Synthetic Aperture Radar

Effects of noise, sampling rate and signal sparsity for compressed sensing Synthetic Aperture Radar pulse compression

Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling.