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Nonlinear beamforming (NLBF) has emerged as a highly effective technology for enhancing seismic data quality. The crux of NLBF's success lies in its ability to robustly estimate local traveltime operators directly from input data, a process that entails solving millions or even billions of nonlinear optimization problems per input gather. Among the solvers used for estimating these operators is the 2 + 2 + 1 method, for which we have previously introduced algorithmic implementations on both the CPU and GPU platforms. In this paper, we present an efficiency-improved GPU algorithm for the 2 + 2 + 1 method, particularly beneficial when dealing with small data apertures in NLBF. Our enhanced GPU algorithm brings significant improvements in computation efficiency through several strategic measures, which include leveraging Horner's method to minimize the mathematical overhead of traveltime calculation, implementing a GPU-friendly data reduction algorithm to exploit GPU computational power, and optimizing shared GPU memory usage as the primary workspace whenever feasible. To demonstrate the tangible efficiency enhancement achieved by our new GPU algorithm, via two illustrative examples, we compare its performance with that of our previous implementation.

Local traveltime operators are an effective way to describe local kinematic wavefronts. They are useful for many applications. One of them is nonlinear beamforming for enhancing the signal-to-noise ratio of challenging seismic data. The so-called 2+2+1 method is a pragmatic approach to estimate unknown local traveltime operators from input data. However, its efficiency still has much room for improvement when the solution space is big. We accelerate the 2+2+1 method using graphics processing unit (GPU) computing with the Compute Unified Device Architecture (CUDA) programming language. We detail the CPU- and GPU-based 2+2+1 search algorithms and demonstrate the efficiency improvement using synthetic and field data examples. Compared to a standard multi-core CPU implementation, our new GPU implementation achieves almost the same quality results at only ∼10% run-time cost.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

As the Treaty Monitoring Community seeks to lower detection thresholds across its sparse sensor network, single-station location estimates and accurate backazimuth predictions become increasingly important. Accurate backazimuth predictions are traditionally limited to array stations, where beamforming provides high-confidence backazimuth prediction that can be reliably passed on to the associator. Three-component stations, on the other hand, rely on polarization analysis for backazimuth prediction, which suffers from both high error and low confidence. As such, very few three-component backazimuth predictions are passed on to the association algorithm. This study presents BazNet, a deep neural-network that takes in a three-component seismogram and produces both a backazimuth prediction and corresponding certainty measure. For existing stations with ample historical training data, the technique achieves an overall median absolute deviation of around 14∘, a modest improvement over the 15∘ achieved by polarization. More importantly, each estimate is accompanied by a robust certainty measure, allowing the selection of high-confidence predictions to be passed on to the associator. Using the BazNet certainty measure, roughly 60% of all three-component predictions can be selected with a median absolute deviation of just 6∘, which is on par with the predictions from a full beamformed seismic array. This represents a sevenfold improvement over the 8% of signals similarly selectable via polarization analysis. BazNet performance is demonstrated against 10 years of waveform data from 561,154 cataloged arrivals across nine stations selected from the global IMS Network: STKA, CPUP, VNDA, LPAZ, AAK, BOSA, ULM, BATI, INK.

The unwanted return signals from wind turbines can contaminate the weather-radar data that are used by forecasters and automatic algorithms to issue forecast and warnings for severe weather. Since wind turbines have moving components that generate return signals with non-zero Doppler velocity, traditional ground clutter filters are ineffective at removing wind turbine clutter (WTC). In this study, a WTC mitigation algorithm using the range-Doppler spectrum is developed and tested with simulated weather and WTC signals. Once the general locations of the WTC contamination are known, the proposed range-Doppler regression (RDR) algorithm exploits the spatial continuity of weather signals in the range domain to mitigate the WTC contamination while retaining as much weather signal as possible. In contrast to other proposed mitigation algorithms, the RDR algorithm is suited for real-time implementation on typical operational weather radars. Simulated data are used to optimise the parameters of the algorithm and evaluate its performance for stratiform- and convective-precipitation cases with different degrees of WTC contamination. Finally, a real data case is processed to illustrate the RDR algorithm's effectiveness. The results show that the RDR algorithm has the potential to effectively reduce the bias in spectral-moment estimates caused by WTC contamination in an operational environment.

The use of signals transmitted by global navigation satellite systems (GNSS) as a tool for earth remote sensing has been attracting growing interest in recent years, in particular for spaceborne missions. For such techniques, several on‐board processing strategies have been proposed, either based on on‐board signal generation or based on ‘blind’ interferometric processing. Presented is a comprehensive comparison of these two categories of GNSS‐R processing techniques by introducing a generalised decomposition of the cross‐correlation waveform.

The adjacency effect and non-uniform responses complicate the precise delimitation of the surface support of remote sensing data and their derived products. Thus, modeling spatial response characteristics (SRCs) prior to using remote sensing information has become important. A point spread function (PSF) is typically used to describe the SRCs of the observation cells from remote sensors and is always estimated in a laboratory before the sensor is launched. However, research on the SRCs of high-order remote sensing products derived from the observations remains insufficient, which is an obstacle to converting between multi-scale remote sensing products and validating coarse-resolution products. This study proposed a method that combines simulation and validation to establish SRC models of coarse-resolution albedo products. Two series of commonly used 500-m/1-km resolution albedo products, which are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data, were investigated using 30-m albedo products that provide the required sub-pixel information. The analysis proves that the size of the surface support of each albedo pixel is larger than the nominal resolution of the pixel and that the response weight is non-uniformly distributed, with an elliptical Gaussian shape. The proposed methodology is generic and applicable for analyzing the SRCs of other advanced remote sensing products.

This paper considers the smoothing problem for inhomogeneous Gauss–Markov Random Fields on a spherical lattice. Various observation models are considered, such as the case of noisy, possibly correlated, observations available only on a subset of sites, or a variable number of process components being measured. A 2D recursive optimal smoothing algorithm is derived, with computational complexity of O(N2) where N is the number of sites, in line with known more common algorithms for inhomogeneous fields on rectangular lattices. An application of the method in weather forecasting using real data is presented, showing the capability of the proposed method.

In this study, a novel method for target identification in a foliage environment is presented. This method is based on the ultra wideband (UWB) wireless sensor networks (WSNs) model, and the foliage environment is specially considered. The data used to identify the targets are derived from the received signal waveform, so most existing transceivers can be exploited as detecting sensors, which leads to a potential low-cost way to identify targets during the normal communications within the WSNs under foliage environment. The selected bispectra algorithm is applied to extract the feature vector, and chaos particle swarm optimisation-based support vector machine is used as the target classifier. Experiments with real-world data samples indicate that this method has an excellent classification performance in a foliage environment. Moreover, this method shows potential for online training.

Distributed sensing systems can transform an optical fiber cable into an array of sensors, allowing users to detect and monitor multiple physical parameters such as temperature, vibration and strain with fine spatial and temporal resolution over a long distance. Fiber-optic distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) systems have been developed for various applications with varied spatial resolution, and spectral and sensing range. Rayleigh scattering-based phase optical time domain reflectometry (OTDR) for vibration and Raman/Brillouin scattering-based OTDR for temperature and strain measurements have been developed over the past two decades. The key challenge has been to find a methodology that would enable the physical parameters to be determined at any point along the sensing fiber with high sensitivity and spatial resolution, yet within acceptable frequency range for dynamic vibration, and temperature detection. There are many applications, especially in geophysical and mining engineering where simultaneous measurements of vibration and temperature are essential. In this article, recent developments of different hybrid systems for simultaneous vibration, temperature and strain measurements are analyzed based on their operation principles and performance. Then, challenges and limitations of the systems are highlighted for geophysical applications.