Journal articles on the topic 'Event picking'
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Thompson, Troy A., Matthew G. Lamont, Bruce M. Hartley, and Michael E. Glinsky. "Automated event picking in prestack hyperspace." ASEG Extended Abstracts 2004, no. 1 (December 2004): 1–4. http://dx.doi.org/10.1071/aseg2004ab148.
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Zeng, Hongliu, Milo M. Backus, Kenneth T. Barrow, and Noel Tyler. "Stratal slicing, Part I: Realistic 3-D seismic model." GEOPHYSICS 63, no. 2 (March 1998): 502–13. http://dx.doi.org/10.1190/1.1444351.
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Two‐dimensional, fenced 2-D, and 3-D isosurface displays of some realistic 3-D seismic models built in the lower Miocene Powderhorn Field, Calhoun County, Texas, demonstrate that a seismic event does not necessarily follow an impedance boundary defined by a geological time surface. Instead, the position of a filtered impedance boundary relative to the geological time surface may vary with seismic frequency because of inadequate resolution of seismic data and to the en echelon or ramp arrangement of impedance anomalies of sandstone. Except for some relatively time‐parallel seismic events, the correlation error of event picking is large enough to distort or even miss the majority of the target zone on stratal slices. In some cases, reflections from sandstone bodies in different depositional units interfere to form a single event and, in one instance, an event tying as many as six depositional units (interbedded sandy and shaly layers) over 50 m was observed. Frequency independence is a necessary condition for selecting time‐parallel reference events. Instead of event picking, phantom mapping between such reference events is a better technique for picking stratal slices, making it possible to map detailed depositional facies within reservoir sequences routinely and reliably from 3-D seismic data.
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Chen, Yangkang. "Automatic microseismic event picking via unsupervised machine learning." Geophysical Journal International 222, no. 3 (June 20, 2020): 1750–64. http://dx.doi.org/10.1093/gji/ggaa186.
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SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.
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Saragiotis, Christos, Tariq Alkhalifah, and Sergey Fomel. "Automatic traveltime picking using instantaneous traveltime." GEOPHYSICS 78, no. 2 (March 1, 2013): T53—T58. http://dx.doi.org/10.1190/geo2012-0026.1.
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Event picking is used in many steps of seismic processing. We present an automatic event picking method that is based on a new attribute of seismic signals, instantaneous traveltime. The calculation of the instantaneous traveltime consists of two separate but interrelated stages. First, a trace is mapped onto the time-frequency domain. Then the time-frequency representation is mapped back onto the time domain by an appropriate operation. The computed instantaneous traveltime equals the recording time at those instances at which there is a seismic event, a feature that is used to pick the events. We analyzed the concept of the instantaneous traveltime and demonstrated the application of our automatic picking method on dynamite and Vibroseis field data.
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Tan, Yuyang, and Chuan He. "Improved methods for detection and arrival picking of microseismic events with low signal-to-noise ratios." GEOPHYSICS 81, no. 2 (March 1, 2016): KS93—KS111. http://dx.doi.org/10.1190/geo2015-0213.1.
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Detection and arrival picking of microseismic events with low signal-to-noise ratios (S/N) are problematic because these events are usually obscured by ambient noise. We have developed an intraevent coherence-based event detection method to address this problem. The innovations of this method include the adaptation of a crosscorrelation, least-squares-based algorithm to achieve better moveout correction for successive record segments and the use of a multichannel semblance coefficient to identify the microseismic events. After finding the events, we adopted a new picker to determine their P- and S-wave arrival times. This picker was developed by combining three aspects of the distinction between seismic signal and ambient noise, namely, the (1) amplitude, (2) polarization, and (3) statistic property differences. We evaluated the performance of the proposed methods using a real data set recorded during an 11-stage hydraulic fracture stimulation. We have determined that, for microseismic event detection, the proposed method has an overall false trigger rate of 12%. As for arrival picking, the average picking error of the new picker is [Formula: see text] and its standard deviation is [Formula: see text]. Comparison of the results of different event detection and arrival picking methods versus the S/N of the data demonstrates that the proposed methods are more applicable for detection and arrival picking of low S/N microseismic events.
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Chen, Yangkang. "Automatic microseismic event picking via unsupervised machine learning." Geophysical Journal International 212, no. 1 (September 27, 2017): 88–102. http://dx.doi.org/10.1093/gji/ggx420.
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Chu, C. K. P., and J. M. Mendel. "First break refraction event picking using fuzzy logic systems." IEEE Transactions on Fuzzy Systems 2, no. 4 (1994): 255–66. http://dx.doi.org/10.1109/91.324805.
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Burinskienė, Aurelija, and Tone Lerher. "Improving Retail Warehouse Activity by Using Product Delivery Data." Processes 9, no. 6 (June 17, 2021): 1061. http://dx.doi.org/10.3390/pr9061061.
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This paper presents a research study which is dedicated to the improvement in retail warehouse activity. This study aims to improve activity by identifying an efficient order picking strategy. (1) Background: The literature review shows the application of order picking strategies, but research related to their selection lacks an integrated approach. (2) Methods: The authors use the discrete event simulation method for the analysis of order picking strategies. The application of the discrete event simulation method enables various scenario tests in retail warehouses, allowing one to benchmark order picking strategies. By using the simulation model, experiments were designed to evaluate order picking strategies that are dependent on the delivery of the product distance variable. This research uses analysis of cost components and helps to identify the best possible order picking strategy to improve the overall warehouse performance. The authors benchmarked order picking strategies and presented constraints following product delivery data concerning their applications. (3) Results: The results presented show that the application of the order sorting strategy delivers 46.6% and the order batching strategy 6.7% lower costs compared to the single picking strategy. The results of the order batching strategy could be improved by 8.34% when the product clustering action is used. (4) Conclusions: The authors provide a theoretical framework which follows the application of order picking strategies using the product delivery data approach, which is the main scientific novelty of this paper. Recommendations are provided regarding the application of the proposed framework for the future improvement in retail warehouse activity.
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McCormack, Michael D., David E. Zaucha, and Dennis W. Dushek. "First‐break refraction event picking and seismic data trace editing using neural networks." GEOPHYSICS 58, no. 1 (January 1993): 67–78. http://dx.doi.org/10.1190/1.1443352.
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Interactive seismic processing systems for editing noisy seismic traces and picking first‐break refraction events have been developed using a neural network learning algorithm. We employ a backpropagation neural network (BNN) paradigm modified to improve the convergence rate of the BNN. The BNN is interactively “trained” to edit seismic data or pick first breaks by a human processor who judiciously selects and presents to the network examples of trace edits or refraction picks. The network then iteratively adjusts a set of internal weights until it can accurately duplicate the examples provided by the user. After the training session is completed, the BNN system can then process new data sets in a manner that mimics the human processor. Synthetic modeling studies indicate that the BNN uses many of the same subjective criteria that humans employ in editing and picking seismic data sets. Automated trace editing and first‐break picking based on the modified BNN paradigm achieve 90 to 98 percent agreement with manual methods for seismic data of moderate to good quality. Productivity increases over manual editing, and picking techniques range from 60 percent for two‐dimensional (2-D) data sets and up to 800 percent for three‐dimensional (3-D) data sets. Neural network‐based seismic processing can provide consistent and high quality results with substantial improvements in processing efficiency.
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Lumban Gaol, Y. H., R. K. Lobo, S. S. Angkasa, A. Abdullah, I. Madrinovella, S. Widyanti, A. Priyono, et al. "Preliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking Using Machine Learning with STA/LTA As the Input Parameters." IOP Conference Series: Earth and Environmental Science 873, no. 1 (October 1, 2021): 012060. http://dx.doi.org/10.1088/1755-1315/873/1/012060.
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Abstract The Short Term Averaging/Long Term Averaging (STA/LTA) has been widely used to detect earthquake arrival time. The method simply calculates the ratio of moving average of the waveform amplitude at short and long-time windows. However, although STA/LTA signals can distinguish between real events and noise, we still recognize some lack of accuracies in first P wave arrival pickings. In this study, we attempt to implement one machine learning method popularly, Artificial Neural Network (ANN) that employ input, hidden and output layer similar as human brain works. Note that in this study, we also try to add input parameters with another derivative signal attributes such as Recursive STA/LTA and Carl STA/LTA. The processing step started by collecting event waveforms from the Agency of Meteorology, Climatology and Geophysics. We chose regional events with moment magnitude higher than 3 in the Maluku region Indonesia. Next, we apply all STA/LTA attributes to the input waveforms. We also tested our STA/LTA with synthetic data and additional noise. Further step, we manually picked the arrival of P wave events and used this as the output for ANN. In total, we used 100 events for arrival data training in P wave phases. In the validation process, an accuracy of more than 0.98 can be obtained after 200 iterations. Final outputs showed, that in average, the difference between manual picking and automatic picking from ANN is 0.45 s. We are able to increase the accuracy by band pass filter (0.1 – 3 Hz) all signal and improve the mean into 0.15s difference between manual picking and ANN picks.
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D’Angelo, Nicoletta, Giada Adelfio, Marcello Chiodi, and Antonino D’Alessandro. "Statistical Picking of Multivariate Waveforms." Sensors 22, no. 24 (December 8, 2022): 9636. http://dx.doi.org/10.3390/s22249636.
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In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth’s surface and the X- and Y-axes being parallel to the surface and generally oriented in North–South and East–West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes.
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Glinsky, Michael E., Grace A. Clark, Peter K. Z. Cheng, K. R. Sandhya Devi, James H. Robinson, and Gary E. Ford. "Automatic event picking in prestack migrated gathers using a probabilistic neural network." GEOPHYSICS 66, no. 5 (September 2001): 1488–96. http://dx.doi.org/10.1190/1.1487094.
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We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made, such as hyperbolic moveout. We train a probabilistic neural network for voxel classification using event times, subsurface points, and offsets (ground truth information) picked manually by expert interpreters. The key to success is using effective features that capture the important behavior of the measured signals. We test a variety of features calculated in a local neighborhood about the voxel under analysis. Selection algorithms ensure that we use only the features that maximize class separability. This event‐picking algorithm has the potential to reduce significantly the cycle time and cost of 3‐D prestack depth migration while making the velocity model inversion more robust.
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Mccormack, M. D., and D. E. Zaucha. "Automated Trace Editing and Refraction Event Picking Using Neural Networks." Revue de l'Institut Français du Pétrole 47, no. 3 (May 1992): 393–405. http://dx.doi.org/10.2516/ogst:1992030.
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Song, Fuxian, H. Sadi Kuleli, M. Nafi Toksöz, Erkan Ay, and Haijiang Zhang. "An improved method for hydrofracture-induced microseismic event detection and phase picking." GEOPHYSICS 75, no. 6 (November 2010): A47—A52. http://dx.doi.org/10.1190/1.3484716.
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The ability to detect small microearthquakes and identify their P- and S-phase arrivals is a key issue in hydrofracture downhole monitoring because of the low signal-to-noise ratios (S/N). An array-based waveform correlation approach (matched filter) is applied to improve the detectability of small magnitude events with mechanisms and locations similar to a nearby master event. After detecting the weak events, a transformed spectrogram method is used to identify the phase arrivals. The technique has been tested on a downhole monitoring data set of the microseismic events induced by hydraulic fracturing. It is shown that, for this case, one event with a S/N around [Formula: see text], which is barely detectable using an array-stacked short-time average/long-time average (STA/LTA) detector under a reasonable false alarm rate, is readily detected on the array-stacked correlation traces. The transformed spectrogram analysis of the detected events improves P- and S-phase picking.
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Pal, Abhishesh, Gautham Das, Marc Hanheide, Antonio Candea Leite, and Pål Johan From. "An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies." Agronomy 12, no. 6 (May 29, 2022): 1299. http://dx.doi.org/10.3390/agronomy12061299.
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Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can be reduced by optimally deploying a fleet of agricultural robots and schedule them by anticipating the human activity behaviour (state) during harvesting. In this paper, we propose a framework for spatio-temporal prediction of human pickers’ activities while they are picking fruits in agriculture fields. Here we exploit temporal patterns of picking operation and 2D discrete points, called topological nodes, as spatial constraints imposed by the agricultural environment. Both information are used in the prediction framework in combination with a variant of the Hidden Markov Model (HMM) algorithm to create two modules. The proposed methodology is validated with two test cases. In Test Case 1, the first module selects an optimal temporal model called as picking_state_progression model that uses temporal features of a picker state (event) to statistically evaluate an adequate number of intra-states also called sub-states. In Test Case 2, the second module uses the outcome from the optimal temporal model in the subsequent spatial model called node_transition model and performs “spatio-temporal predictions” of the picker’s movement while the picker is in a particular state. The Discrete Event Simulation (DES) framework, a proven agricultural multi-robot logistics model, is used to simulate the different picking operation scenarios with and without our proposed prediction framework and the results are then statistically compared to each other. Our prediction framework can reduce the so-called unproductive logistics time in a fully manual harvesting process by about 80 percent in the overall picking operation. This research also indicates that the different rates of picking operations involve different numbers of sub-states, and these sub-states are associated with different trends considered in spatio-temporal predictions.
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Wang, Hong Jun, Xiang Jun Zou, D. J. Zou, J. Liu, and Tian Hu Liu. "Study on Location Simulation for Picking Manipulator in Virtual Environment Based on Information Fusion of Multi-Sensor." Key Engineering Materials 392-394 (October 2008): 596–600. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.596.
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In picking manipulator location system, it is the key problem that the positions of obi-object and picking manipulator are exactly determined in complex environment. Based on multi-sensor information fusion method, a data fusion system of multi-sensor integrating laser-sensor for absolute location with ultrasonic-sensor for inspection impediment was presented. Firstly, data collection and fusion were implemented employing a two- level distribution system. Secondly, the method of data collection and fusion in virtual environment was discussed, and the result data could drive picking manipulator 3D model to dynamically move in real-time using event and route mechanisms provided by virtual environment, which could simulate the process of picking manipulator being accurately located. Finally, a location simulation system was developed by VC++ and EON SDK.
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Rentsch, S., S. Buske, S. Lüth, and S. A. Shapiro. "Fast location of seismicity: A migration-type approach with application to hydraulic-fracturing data." GEOPHYSICS 72, no. 1 (January 2007): S33—S40. http://dx.doi.org/10.1190/1.2401139.
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We propose a new approach for the location of seismic sources using a technique inspired by Gaussian-beam migration of three-component data. This approach requires only the preliminary picking of time intervals around a detected event and is much less sensitive to the picking precision than standard location procedures. Furthermore, this approach is characterized by a high degree of automation. The polarization information of three-component data is estimated and used to perform initial-value ray tracing. By weighting the energy of the signal using Gaussian beams around these rays, the stacking is restricted to physically relevant regions only. Event locations correspond to regions of maximum energy in the resulting image. We have successfully applied the method to synthetic data examples with 20%–30% white noise and to real data of a hydraulic-fracturing experiment, where events with comparatively small magnitudes [Formula: see text] were recorded.
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Billings, S. D., M. S. Sambridge, and B. L. N. Kennett. "Errors in hypocenter location: Picking, model, and magnitude dependence." Bulletin of the Seismological Society of America 84, no. 6 (December 1, 1994): 1978–90. http://dx.doi.org/10.1785/bssa0840061978.
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Abstract The location procedures of seismic events are influenced by two major classes of errors, the error in picking individual seismic phases and modeling error due to the departure of the real Earth from the reference model used in the location. Both classes of error influence the estimate of location and it is difficult to separate them. The role of picking errors can be assessed by a nonlinear analysis using a Monte Carlo procedure. Arrivals times are perturbed with random numbers drawn from a normal distribution, and the event is relocated using these perturbed arrival times. By repeating the procedure many times, a cluster of locations is obtained, which can be used to investigate the effects of picking errors on the hypocenter. This analysis is insensitive to velocity-model errors as these are fixed for a given combination of stations and phases. Some care must be exercised when analysing multidimensional distributions in two-dimensional slices because of a projection effect. The modeling error due to the influence of lateral heterogeneity in the Earth is examined by comparing the locations of the same event using different combinations of phases and network geometries, which reinforces the need to use arrivals other than P for accurate depth resolution. The sensitivity of P arrivals to changes in depth are swamped by model errors, and inclusion of depth-sensitive phases such as pP is highly recommended. The effect of picking errors on location is found to be much smaller than the mislocation caused by neglecting lateral heterogeneity when only P arrivals are used. Consequently, the Monte Carlo analysis, which is primarily aimed at picking errors only, is most appropriate when multiple phases have been used to more accurately constrain the hypocenter, especially for the depth component. Altering the type of phase data used in the location plays a similar role in changing the network geometry, in that both are mechanisms that influence the nature of the constraint on the hypocenter. By relocating events with network geometries corresponding to the different magnitudes, it is found that the location of the event can be affected significantly by the magnitude, and when using robust statistics to describe earthquake residuals, the mislocation can occur in a systematic manner. The effect is marked in regions with significant lateral variations in seismic velocities. For example, low-magnitude events in the Flores Sea are found to be dragged toward Australia as a result of the fast paths to Australian stations relative to the iasp91 reference velocity model.
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Li, Zhenhua, and Mirko van der Baan. "Microseismic event localization by acoustic time reversal extrapolation." GEOPHYSICS 81, no. 3 (May 2016): KS123—KS134. http://dx.doi.org/10.1190/geo2015-0300.1.
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Traditional ray-based methods for microseismic event localization require picking of P- and S-wave first arrivals, which is often time consuming. Polarization analysis for each event is often also needed to determine its absolute location. Location methods based on reverse time extrapolation avoid the need for first-arrival time picking. Traditional reverse time extrapolation only incorporates particle velocity or displacement wavefields. This is an incomplete approximation of the acoustic representation theorem, which leads to artifacts in the back-propagation process. For instance, if the incomplete approximation is used for microseismic event locations using three-component (3C) borehole recordings, it produces a ghost event on the opposite side of the well, which leads to ambiguous interpretations. We have developed representation-theorem-based reverse time extrapolation for microseismic event localization, combining the 3C particle velocities (displacements) and the pressure wavefield. The unwanted ghost location is removed by explicitly incorporating a wavefield and its spatial derivative. Moreover, polarization analysis is not needed, because wavefields will focus at its absolute location during back propagation. Determination of microseismic event locations using wavefield extrapolation also necessitates a robust focusing criterion. The Hough transform allows for accurate determination of source timing and location by summing wavefront energy in the time-space domain. Synthetic examples demonstrated the good performance of the wavefield extrapolation scheme and focusing criterion in complex velocity fields for borehole acquisition geometries.
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Chen, Yangkang. "Expression of Concern: Automatic microseismic event picking via unsupervised machine learning." Geophysical Journal International 221, no. 3 (April 7, 2020): 2051. http://dx.doi.org/10.1093/gji/ggaa134.
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Xiantai, Gou, Li Zhimin, Qin Na, and Jin Weidong. "Adaptive picking of microseismic event arrival using a power spectrum envelope." Computers & Geosciences 37, no. 2 (February 2011): 158–64. http://dx.doi.org/10.1016/j.cageo.2010.05.022.
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Witten, Ben, and Jeffrey Shragge. "Image-domain velocity inversion and event location for microseismic monitoring." GEOPHYSICS 82, no. 5 (September 1, 2017): KS71—KS83. http://dx.doi.org/10.1190/geo2016-0561.1.
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Microseismic event locations obtained from seismic monitoring data sets are often a primary means of determining the success of fluid-injection programs, such as hydraulic fracturing for oil and gas extraction, geothermal projects, and wastewater injection. Event locations help the decision makers to evaluate whether operations conform to expectations or parameters need to be changed and may be used to help assess and reduce the risk of induced seismicity. However, obtaining accurate event location estimates requires an accurate velocity model, which is not available at most injection sites. Common velocity updating techniques require picking arrivals on individual seismograms. This can be problematic in microseismic monitoring, particularly for surface acquisition, due to the low signal-to-noise ratio of the arrivals. We have developed a full-wavefield adjoint-state method for locating seismic events while inverting for P- and S-wave velocity models that optimally focus multiple complementary images of recorded seismic events. This method requires neither picking nor initial estimates of event location or origin time. Because the inversion relies on (image domain) residuals that satisfy the differential semblance criterion, there is no requirement that the starting model be close to the true velocity. We determine synthetic results derived from a model with conditions similar to a field-acquisition scenario in terms of the number and spatial sampling of receivers and recorded coherent and random noise levels. The results indicate the effectiveness of the methodology by demonstrating a significantly enhanced focusing of event images and a reduction of 95% in event location error from a reasonable initial model.
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Zhang, Xiong, Huihui Chen, Wei Zhang, Xiao Tian, and Fangdong Chu. "Generalized neural network trained with a small amount of base samples: Application to event detection and phase picking in downhole microseismic monitoring." GEOPHYSICS 86, no. 5 (September 1, 2021): KS95—KS108. http://dx.doi.org/10.1190/geo2020-0955.1.
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The deep-learning method has been successfully applied to many geophysical problems to extract features from seismic big data. However, some applications may not have sufficient available data to directly train a generalized neural network. We have applied data augmentation on a significantly small number of samples to train a generalized neural network for microseismic event detection and phase picking, which could be used in different project settings and areas. We use the U-Net architecture consisting of 2D convolutional layers to create the prediction function, and we map the waveforms recorded by using multiple receivers to the P/S arrival time labels; thus, the neural network can learn the P/S moveout features from multiple receivers. The training set is generated by simulating various realizations of the data based on 10 original samples from the beginning of a hydraulic fracturing stage. The trained neural network is then used to detect the events and pick the P/S phases from the continuous data for different stages and projects. A grid search from a precalculated traveltime table is performed to determine the event location after an event is detected. We build a real-time event detection and location workflow without human intervention by combining the neural network and grid search method, and we apply the workflow to a different stage from the training events and a completely independent project that the neural network has not encountered. The results indicate that microseismic events are successfully detected and located, and the picking performance of the neural network is superior to that of a traditional autoregression picker.
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Forte, Emanuele, Matteo Dossi, Michele Pipan, and Anna Del Ben. "Automated phase attribute-based picking applied to reflection seismics." GEOPHYSICS 81, no. 2 (March 1, 2016): V141—V150. http://dx.doi.org/10.1190/geo2015-0333.1.
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We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process.
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Lobo, R. K., Y. H. L. Gaol, D. Y. Fatimah, A. Abdullah, D. A. Zaky, S. K. Suhardja, A. D. Nugraha, et al. "An Attempt to Pick Teleseismic P Wave Arrival Using Envelope and Artificial Neural Network Algorithm." IOP Conference Series: Earth and Environmental Science 873, no. 1 (October 1, 2021): 012059. http://dx.doi.org/10.1088/1755-1315/873/1/012059.
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Abstract Seismic events detection and phase picking play an essential role in earthquake studies. Typical event detection is done visually or manually on recorded seismogram by choosing a series of higher amplitude signals recorded on at least 4 stations. More sophisticated methods have been used in event detection and picking with additional attributes such as Short Time Average over Long Time Average (STA/LTA). This method is based on average number sampled at multiple predefined windows. However, STA/LTA is dependent on the window size which becomes its drawback. In this study, we explore one derivative attribute, popularly known as envelope or instantaneous amplitude. It has been extensively used in seismic reflection and refraction method. In principle, this method uses the Hilbert Transform to calculate complex seismic trace and take the magnitude of complex seismic trace as envelope amplitude that can be used to analyze P wave arrival time. We employed one of the machine learning methods, Artificial Neural Network (ANN). The ANN method works by analyzing various inputs and training them to recognize patterns in P wave arrival signals. We started our study by applying envelope attribute to synthetic data with noise addition. We found that with noisy data the envelope attribute still gives a clear signal for first-time arrival. Next, we trained 300 seismograms of teleseismic events recorded on IRIS-US networks and tested our trained program on 20 seismograms as a blind test. To compare performance between the two methods, we calculated the difference between the results of automatic picking and manual picking. The final calculation shows an average deviation of 0.355 seconds. Twenty-five percent of testing data (5 samples) has a deviation above 0.5 seconds, and 75% of the remainder (15 samples) already had a deviation under 0.5 seconds. The more significant deviations of the P wave picks are likely due to noisy signals in the data set and complex arrival signals. This study shows that the combination of envelope attribute and machine learning method is promising to distinguish teleseismic P wave arrival and automatically pick them.
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Lolli, Francesco, Francesco Lodi, Claudio Giberti, Antonio Maria Coruzzolo, and Samuele Marinello. "Order Picking Systems: A Queue Model for Dimensioning the Storage Capacity, the Crew of Pickers, and the AGV Fleet." Mathematical Problems in Engineering 2022 (February 24, 2022): 1–15. http://dx.doi.org/10.1155/2022/6318659.
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Designing an order picking system can be very complex, as several interrelated control variables are involved. We address the sizing of the storage capacity of the picking bay, the crew of pickers, and the AGV fleet, which are the most important variables from a tactical viewpoint in a parts-to-pickers system. Although order picking is a widely explored topic in the literature, no analytical model that can simultaneously deal with these variables is currently available. To bridge this gap, we introduce a queue model for Markovian processes, which enables us to jointly optimise the aforementioned control variables. A discrete-event simulation is then used to validate our model, and we then test our proposal with real data under different operative scenarios, with the aim of assessing the usefulness of the proposal in real settings.
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Wang, Hanchen, and Tariq Alkhalifah. "Direct microseismic event location and characterization from passive seismic data using convolutional neural networks." GEOPHYSICS 86, no. 6 (September 9, 2021): KS109—KS121. http://dx.doi.org/10.1190/geo2020-0636.1.
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The ample size of time-lapse data often requires significant event detection and source location efforts, especially in areas such as shale gas exploration regions where a large number of microseismic events are often recorded. In many cases, real-time monitoring and location of these events are essential to production decisions. Conventional methods face considerable drawbacks. For example, traveltime-based methods require traveltime picking of often noisy data, whereas migration and waveform inversion methods require expensive wavefield solutions and event detection. Both tasks require some human intervention, and this becomes a big problem when too many sources need to be located, which is common in microseismic monitoring. Machine learning has recently been used to identify microseismic events or locate their sources once they are identified and picked. We have used a novel artificial neural network framework to directly map seismic data, without any event picking or detection, to their potential source locations. We train two convolutional neural networks (CNNs) on labeled synthetic acoustic data containing simulated microseismic events to fulfill such requirements. One CNN, which has a global average pooling layer to reduce the computational cost while maintaining high-performance levels, aims to classify the number of events in the data. The other network predicts the source locations and other source features such as the source peak frequencies and amplitudes. To reduce the size of the input data to the network, we correlate the recorded traces with a central reference trace to allow the network to focus on the curvature of the input data near the zero-lag region. We train the networks to handle single-, multi-, and no-event segments extracted from the data. Tests on a simple vertical varying model and a more realistic Otway field model demonstrate the approach’s versatility and potential.
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Poliannikov, Oleg V., Michael Prange, Alison E. Malcolm, and Hugues Djikpesse. "Joint location of microseismic events in the presence of velocity uncertainty." GEOPHYSICS 79, no. 6 (November 1, 2014): KS51—KS60. http://dx.doi.org/10.1190/geo2013-0390.1.
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The locations of seismic events are used to infer reservoir properties and to guide future production activity, as well as to determine and understand the stress field. Thus, locating seismic events with uncertainty quantification remains an important problem. Using Bayesian analysis, a joint probability density function of all event locations was constructed from prior information about picking errors in kinematic data and explicitly quantified velocity model uncertainty. Simultaneous location of all seismic events captured the absolute event locations and the relative locations of some events with respect to others, along with their associated uncertainties. We found that the influence of an uncertain velocity model on location uncertainty under many realistic scenarios can be significantly reduced by jointly locating events. Many quantities of interest that are estimated from multiple event locations, such as fault sizes and fracture spacing or orientation, can be better estimated in practice using the proposed approach.
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Wang, Jianhua, Shilei Lu, Yubin Lan, and Lianglun Cheng. "An Efficient Complex Event Processing Algorithm Based on NFA-HTBTS for Massive RFID Event Stream." International Journal of Information Technologies and Systems Approach 11, no. 2 (July 2018): 18–30. http://dx.doi.org/10.4018/ijitsa.2018070102.
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This article describes how quickly picking up some valuable information from massive RFID event stream often faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of volume, velocity, variety, value and veracity. Aim to solving the problems above, an efficient complex event processing method based on NFA-HTBTS (Nondeterministic Finite Automaton-Hash Table B+ Tree Structure) is presented in this article. The achievement of this article lies in that we successfully use the union of NFA and HTBTS to realize the detection of complex event in massive RFID event stream. Specially, in our scheme, after using NFA to match related primitive events from massive RFID event stream, we use hash table and B+ tree structure to successfully realize the detection of complex event from large matched results above, as a result, these problems existed in current methods above can be effectively solved by our scheme. The simulation results show that our proposed scheme outperforms some general methods for massive RFID event stream.
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Zhou, Yijian, Han Yue, Qingkai Kong, and Shiyong Zhou. "Hybrid Event Detection and Phase‐Picking Algorithm Using Convolutional and Recurrent Neural Networks." Seismological Research Letters 90, no. 3 (April 10, 2019): 1079–87. http://dx.doi.org/10.1785/0220180319.
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Varney, Daniel, and Douglas Bousfield. "Discrete element method to predict coating failure mechanisms." January 2018 17, no. 01 (February 1, 2018): 21–28. http://dx.doi.org/10.32964/tj17.01.21.
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The mechanical properties of coating layers are critical for post-application processes such as calendering, printing, and folding. Discrete element methods (DEM) have been used to simulate basic deformations such as tensile and compression, but have not been used as a tool to predict cracking-at-the-fold (CAF) or picking. DEM has the potential to increase our understanding of these failure mechanisms at the particle level. We propose a method to model the three-point bending of a coating layer and also the out-of-plane picking event during printing (using a z-direction scenario and an approach involving a moving force/velocity). Properties of the binder and the binder concentration are input parameters for the simulation. The model predicts the crack formation of the layer, the flexural modulus, and the maximum flexural strain during bending. The model also predicts the forces required for picking to occur. Results are compared with those of complimentary studies.
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Wang, Yi, Xueyi Shang, and Kang Peng. "Locating Mine Microseismic Events in a 3D Velocity Model through the Gaussian Beam Reverse-Time Migration Technique." Sensors 20, no. 9 (May 8, 2020): 2676. http://dx.doi.org/10.3390/s20092676.
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Microseismic (MS) source location is a fundamental and critical task in mine MS monitoring. The traditional ray tracing-based location method can be easily affected by many factors, such as multi-ray path effects, waveform focusing and defocusing of wavefield propagation, and low picking precision of seismic phase arrival. By contrast, the Gaussian beam reverse-time migration (GBRTM) location method can effectively and correctly model the influences of multi-path effects and wavefield focusing and defocusing in complex 3D media, and it takes advantages of the maximum energy focusing point as the source location with the autocorrelation imaging condition, which drastically reduces the requirements of signal-to-noise ratio (SNR) and picking accuracy of P-wave arrival. The Gaussian beam technique has been successfully applied in locating natural earthquake events and hydraulic fracturing-induced MS events in one-dimensional (1D) or simple two-dimensional (2D) velocity models. The novelty of this study is that we attempted to introduce the GBRTM technique into a mine MS event location application and considered utilizing a high-resolution tomographic 3D velocity model for wavefield back propagation. Firstly, in the synthetic test, the GBRTM location results using the correct 2D velocity model and different homogeneous velocity models are compared to show the importance of velocity model accuracy. Then, it was applied and verified by eight location premeasured blasting events. The synthetic results show that the spectrum characteristics of the recorded blasting waveforms are more complicated than those generated by the ideal Ricker wavelet, which provides a pragmatic way to evaluate the effectiveness and robustness of the MS event location method. The GBRTM location method does not need a highly accurate picking of phase arrival, just a simple detection criterion that the first arrival waveform can meet the windowing requirements of wavefield back propagation, which is beneficial for highly accurate and automatic MS event location. The GBRTM location accuracy using an appropriate 3D velocity model is much higher than that of using a homogeneous or 1D velocity model, emphasizing that a high-resolution velocity model is very critical to the GBRTM location method. The average location error of the GBRTM location method for the eight blasting events is just 17.0 m, which is better than that of the ray tracing method using the same 3D velocity model (26.2 m).
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Gooding, Richard. "Shifting Sands." Electric and Hybrid Vehicle Technology International 2021, no. 3 (November 2021): 32–38. http://dx.doi.org/10.12968/s1467-5560(22)60255-0.
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Zero emission motorsport is fast picking up pace, and now the Dakar Rally endurance event is also entering the electrified age. However, it’s not just electric and hybrid powertrains that will duel in the harsh desert. The arrival of hydrogen-powered vehicles is an important step in the series’ clean-energy transition
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Hildyard, M. W., S. E. J. Nippress, and A. Rietbrock. "Event Detection and Phase Picking Using a Time-Domain Estimate of Predominate Period Tpd." Bulletin of the Seismological Society of America 98, no. 6 (December 1, 2008): 3025–32. http://dx.doi.org/10.1785/0120070272.
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Castellanos, Fernando, and Mirko van der Baan. "Waveform similarity for quality control of event locations, time picking, and moment tensor solutions." GEOPHYSICS 80, no. 6 (November 2015): WC99—WC106. http://dx.doi.org/10.1190/geo2015-0043.1.
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Supendi, Pepen, Supriyanto Rohadi, Dwikorita Karnawati, Andri Dian Nugraha, Sri Widiyantoro, Daryono, and Muzli. "Analysis of the destructive earthquakes end of 2017 (Mw 6.9) and early 2018 (Mw 6.1) south of West Java, Indonesia." E3S Web of Conferences 211 (2020): 02003. http://dx.doi.org/10.1051/e3sconf/202021102003.
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On December 15, 2017, and January 23, 2018, there were destructive earthquakes to the south of West Java, Indonesia, with Mw 6.9 and Mw 6.1, respectively. We have determined the hypocenter location for both mainshocks through re-picking of the Pand S-wave arrival times recorded by the Agency for Meteorology, Climatology, and Geophysics (BMKG) seismic stations in West Java and its vicinity. We have then relocated the aftershocks for both events. We have also conducted focal mechanism analysis to estimate the type of fault slip. Our results show the 2017 and 2018 events occurred in the intra-slab at 108.6 km and 46.5 km depths, respectively. The focal mechanism solution shows a thrust fault type with the strike direction almost perpendicular to the trench for the 2017 event, and it is almost parallel to the trench for the 2018 event.
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Romero, José Emilio, Manuel Titos, Ángel Bueno, Isaac Álvarez, Luz García, Ángel de la Torre, and Ma Carmen Benítez. "APASVO: A free software tool for automatic P-phase picking and event detection in seismic traces." Computers & Geosciences 90 (May 2016): 213–20. http://dx.doi.org/10.1016/j.cageo.2016.02.004.
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Ojha, Sajan Raj, Subhashis Das, and Sampada Karanjit. "A Process Ontology for a Confectionery Service Robot." International Journal of Semantic Computing 12, no. 01 (March 2018): 149–66. http://dx.doi.org/10.1142/s1793351x18400081.
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Confectionery items play a vital role in our lives. A plethora of confectionery items are ordered and consumed during various social gatherings, by a wide range of customers. The order may vary in terms of quantity, type of event and level of specificity. Moreover, some order may contain all the information needed while some may only contain the name of the festival for which suitable items need to be inferred and packaged accordingly. To understand all these intricacies for an automated agent involved in picking and packaging of items requires background knowledge. An automated system can perform these tasks only if, it comprises of a robust internal knowledge representation system. We, thus, propose an ontology for a service robot, SweetBot that defines pertinent concepts applicable to the confectionery domain needed by the robot in order to perform the picking and packaging of sweet items efficiently. We further evaluated the efficiency or the robustness of the model by using various SPARQL queries.
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Wilson, David C., Emily Wolin, William L. Yeck, Robert E. Anthony, and Adam T. Ringler. "Modeling Seismic Network Detection Thresholds Using Production Picking Algorithms." Seismological Research Letters 93, no. 1 (October 20, 2021): 149–60. http://dx.doi.org/10.1785/0220210192.
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Abstract Estimating the detection threshold of a seismic network (the minimum magnitude earthquake that can be reliably located) is a critical part of network design and can drive network maintenance efforts. The ability of a station to detect an earthquake is often estimated by assuming the spectral amplitude for an earthquake of a given size, assuming an attenuation relationship, and comparing the predicted amplitude with the average station background noise level. This approach has significant uncertainty because of unknown regional attenuation and complications in computing small event power spectra, and it fails to account for the specific capabilities of the automatic seismic phase picker used in monitoring. We develop a data-driven approach to determine network detection thresholds using a multiband phase picking algorithm that is currently in use at the U.S. Geological Survey National Earthquake Information Center. We apply this picking algorithm to cataloged earthquakes to determine an empirical relationship of the observability of earthquakes as a function of magnitude and distance. Using this relationship, we produce maps of detection threshold using station spatial configuration and station noise levels. We show that quiet, well-sited stations significantly increase the detection capabilities of a network compared with a network composed of many noisy stations. Because our method is data driven, it has two distinct advantages: (1) it is less dependent on theoretical assumptions of source spectra and models of regional attenuation, and (2) it can easily be applied to any seismic network. This tool allows for an objective approach to the management of stations in regional seismic networks.
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Makwiza, Chikondi, and Heinz Erasmus Jacobs. "Sound recording to characterize outdoor tap water use events." Journal of Water Supply: Research and Technology-Aqua 66, no. 6 (July 17, 2017): 392–402. http://dx.doi.org/10.2166/aqua.2017.120.
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Obtaining disaggregated water use at the home typically involves expensive smart metering. In this study, water use events at the outdoor tap were captured using recorded sound. Outdoor taps at 10 homes were fitted with small-sized microphones and digital sound recorders. Sound files recorded over a 1-month period were used in the analysis. In the preliminary analysis, a human operator browsed through the sound recordings, picking out tap use events based on visually recognizable waveform and spectrogram features, then audibly verified each event identified before labeling. The performance of the corresponding automatic detection algorithm was reasonable, showing that water use events can be detected at precision and recall rates of at least 80% under suitable conditions. The results also showed that the technique is less suitable where the drop in pressure during peak demand periods results in significant reduction in the tap flowrate. Indirect flow sensing approaches are attractive for investigating water use event timing, because of the relatively lower cost when compared to conventional or smart water meters. Plumbing changes are not required as the recorder can be mounted on any exposed pipe section near the fixture of interest.
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Chauris, Hervé, Mark S. Noble, Gilles Lambaré, and Pascal Podvin. "Migration velocity analysis from locally coherent events in 2‐D laterally heterogeneous media, Part I: Theoretical aspects." GEOPHYSICS 67, no. 4 (July 2002): 1202–12. http://dx.doi.org/10.1190/1.1500382.
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We present a new method based on migration velocity analysis (MVA) to estimate 2‐D velocity models from seismic reflection data with no assumption on reflector geometry or the background velocity field. Classical approaches using picking on common image gathers (CIGs) must consider continuous events over the whole panel. This interpretive step may be difficult—particularly for applications on real data sets. We propose to overcome the limiting factor by considering locally coherent events. A locally coherent event can be defined whenever the imaged reflectivity locally shows lateral coherency at some location in the image cube. In the prestack depth‐migrated volume obtained for an a priori velocity model, locally coherent events are picked automatically, without interpretation, and are characterized by their positions and slopes (tangent to the event). Even a single locally coherent event has information on the unknown velocity model, carried by the value of the slope measured in the CIG. The velocity is estimated by minimizing these slopes. We first introduce the cost function and explain its physical meaning. The theoretical developments lead to two equivalent expressions of the cost function: one formulated in the depth‐migrated domain on locally coherent events in CIGs and the other in the time domain. We thus establish direct links between different methods devoted to velocity estimation: migration velocity analysis using locally coherent events and slope tomography. We finally explain how to compute the gradient of the cost function using paraxial ray tracing to update the velocity model. Our method provides smooth, inverted velocity models consistent with Kirchhoff‐type migration schemes and requires neither the introduction of interfaces nor the interpretation of continuous events. As for most automatic velocity analysis methods, careful preprocessing must be applied to remove coherent noise such as multiples.
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Ikoniadou, Eleni. "Abstract Time and Affective Perception in the Sonic Work of Art." Body & Society 20, no. 3-4 (September 2014): 140–61. http://dx.doi.org/10.1177/1357034x14546056.
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The purpose of this article is to explore the concept of rhythm as enabling relations and thus as an appropriate mode of analysis for digital sound art installation. In particular, the article argues for a rhythmanalysis of the sonic event as a ‘vibrating sensation’ (Deleuze and Guattari) that incorporates the virtual without necessarily actualizing it. Picking up on notions such as rhythm, time, affect, and event, particularly through their discussion in relation to Susanne Langer’s work, I argue for the consideration of the sonic event as an instance of a different kind of temporality subsisting underneath clock-time and sense perception. Ultimately, and this is the position of this essay, an investigation into experimental projects that interweave digital, sound, and aesthetic dimensions enables the articulation of a rhythmic time that helps account for the unknown, indeterminate, and unintentional forces immanent to the sonic.
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Kapetanidis, V., P. Papadimitriou, and K. Makropoulos. "A CROSS-CORRELATION TECHNIQUE FOR RELOCATION OF SEISMICITY IN THE WESTERN CORINTH RIFT." Bulletin of the Geological Society of Greece 43, no. 4 (January 25, 2017): 2015. http://dx.doi.org/10.12681/bgsg.11392.
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Local seismological networks provide data that allow the location of microearthquakes which otherwise would be dismissed due to low magnitudes and low signal-to-noise ratios of their seismic signals. The Corinth Rift Laboratory (CRL) network, installed in the western Corinth rift, has been providing digital waveform data since 2000. In this work, a semi-automatic picking technique has been applied which exploits the similarity between waveforms of events that have occurred in approximately the same area of an active fault. Similarity is measured by the crosscorrelation maxi-mum of full signals. Events with similar waveforms are grouped in multiplet clusters using the nearest-neighbour linkage algorithm. Manually located events act as masters, while automatically located events of each multiplet cluster act as slaves. By cross-correlating the P-wave or S-wave segments of a master event with the corresponding segments of each of its slave events, after appropriately aligning their offsets, the measured time-lag at the cross-correlation maximum can be subtracted from the arrival-time of the slave event. After the correction of the arrival-times, a double-difference technique is applied to the modified catalogue to further improve the locations of clusters and distinguish the active seismogenic structures in the tectonically complex Western Corinth rift.
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Long, Yun, Jun Lin, Bin Li, Hongchao Wang, and Zubin Chen. "Fast-AIC Method for Automatic First Arrivals Picking of Microseismic Event With Multitrace Energy Stacking Envelope Summation." IEEE Geoscience and Remote Sensing Letters 17, no. 10 (October 2020): 1832–36. http://dx.doi.org/10.1109/lgrs.2019.2952571.
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Peng, Kang, Hongyang Guo, and Xueyi Shang. "Data field application in removing large P-phase arrival picking errors and relocating a mine microseismic event." Soil Dynamics and Earthquake Engineering 139 (December 2020): 106359. http://dx.doi.org/10.1016/j.soildyn.2020.106359.
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Amorim‐Lopes, Mário, Luís Guimarães, João Alves, and Bernardo Almada‐Lobo. "Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete‐event simulation." International Transactions in Operational Research 28, no. 2 (July 30, 2020): 687–715. http://dx.doi.org/10.1111/itor.12852.
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Hirabayashi, Nobuyasu. "Real-time event location using model-based estimation of arrival times and back azimuths of seismic phases." GEOPHYSICS 81, no. 2 (March 1, 2016): KS25—KS40. http://dx.doi.org/10.1190/geo2014-0357.1.
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I have devised a real-time procedure for locating events using an estimation method that analyzes arrival times and back azimuths of phases. The new procedure is applicable to data acquired by local array receivers, such as those used in single-well monitoring as well as by dense receiver networks, and also to noisy waveforms, such as those observed in hydraulic fracturing monitoring if the signal-to-noise ratio is greater than approximately 6 dB. The new procedure uses coalescence microseismic mapping to obtain predictions of arrival times. Based on these predictions, arrival times were estimated by picking the maximum of the ratio of the short-term average to the long-term average of a characteristic function computed for waveforms in an appropriate time window. The estimated arrival times were used in a probabilistic location method, and the probability density function (PDF) of the event location was generated. To locate events for a local array of receivers, the PDFs of event back azimuths obtained using polarizations were combined with the traveltime data to remove directional ambiguities. I have developed this method to generate the PDF of event back azimuths using the average of polarization misfits, which are the differences of the measured and computed polarizations for trial event locations, weighted by the signal-to-noise ratio. Synthetic and field data examples of single-well monitoring of hydraulic fracturing, which required the estimation of event back azimuths in addition to arrival times, were evaluated to determine the effectiveness of the new procedure.
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Zhang, Zhishuai, James W. Rector, and Michael J. Nava. "Microseismic hydraulic fracture imaging in the Marcellus Shale using head waves." GEOPHYSICS 83, no. 2 (March 1, 2018): KS1—KS10. http://dx.doi.org/10.1190/geo2017-0184.1.
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We have studied microseismic data acquired from a geophone array deployed in the horizontal section of a well drilled in the Marcellus Shale near Susquehanna County, Pennsylvania. Head waves were used to improve event location accuracy as a substitution for the traditional P-wave polarization method. We identified that resonances due to poor geophone-to-borehole coupling hinder arrival-time picking and contaminate the microseismic data spectrum. The traditional method had substantially greater uncertainty in our data due to the large uncertainty in P-wave polarization direction estimation. We also identified the existence of prominent head waves in some of the data. These head waves are refractions from the interface between the Marcellus Shale and the underlying Onondaga Formation. The source location accuracy of the microseismic events can be significantly improved by using the P-, S-wave direct arrival times and the head wave arrival times. Based on the improvement, we have developed a new acquisition geometry and strategy that uses head waves to improve event location accuracy and reduce acquisition cost in situations such as the one encountered in our study.
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Woollam, Jack, Andreas Rietbrock, Jens Leitloff, and Stefan Hinz. "HEX: Hyperbolic Event eXtractor, a Seismic Phase Associator for Highly Active Seismic Regions." Seismological Research Letters 91, no. 5 (July 15, 2020): 2769–78. http://dx.doi.org/10.1785/0220200037.
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Abstract The task of seismic phase association is to correlate the onsets of radiated seismic energy with an underlying source. Commonly applied within seismic monitoring networks for event detection, it forms a vital component of many seismic processing pipelines. With the complexity of this task naturally increasing with the number of phases to simultaneously correlate, rapid advancements in the number of sensors per seismic deployment, along with improved picking algorithms have greatly increased the volume of phases now recorded across seismic networks. Although traditional phase association methods work well for historic catalogs, they become unreliable when tasked with associating the frequent smaller events recorded in the latest seismic datasets. Accurately correlating such events is crucial if seismologists are to better understand the underlying physical processes. The phase association problem is, therefore, being revisited with novel techniques now being applied to improve performance. We present a new technique for associating seismic phases, Hyperbolic Event eXtractor (HEX). HEX adapts the logic of Random Sample Consensus, a model estimation approach widely used in the computer vision community and specifically designed to deal with high proportions of noise in the data distribution. We demonstrate the performance of HEX in associating phases over a synthetic dataset for a regional seismic network in northern Chile. Synthetic testing reveals that HEX can correlate seismic phases when events have up to a ∼15 s average spacing.
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Hansen, Katherine M., Kabir Roy‐Chowdhury, and Robert A. Phinney. "The sign filter for seismic event detection." GEOPHYSICS 53, no. 8 (August 1988): 1024–33. http://dx.doi.org/10.1190/1.1442539.
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The theory of statistical hypothesis testing is used to develop and apply a seismic signal detection filter. The filter, herein named the sign filter, scans a stacked section and designates a linear segment as “signal” or “noise” based on the value of the sign test statistic evaluated over the amplitudes within the segment; only the signals are passed. The sign test statistic is nonparametric, so that probabilistic calculations related to the filtering process do not require rigid assumptions regarding the noise distribution. Consequently, it is possible to calculate both the probability that the filter will pass a segment containing only noise, and the expected number of noise‐only segments to be passed. These numbers may be adjusted by changing the tunable parameters of the filter. The detector was tested on both synthetic and field data. For synthetic data, all of the signals present in the data were identified, and the output did not contain any spurious signals, even for a signal‐to‐noise ratio smaller than 1. For field data, the events chosen by the filter, for the most part, agree closely with those visible in the input section; and much of the spatially incoherent energy is suppressed. A few of the passed segments were not visually coherent in the input stack; we suggest a method by which such segments might be identified and removed. The method is fairly general and may be modified for different definitions of signal. The case of linear alignments is the easiest to implement, and the detector promises to be useful in both the processing (automatic picking of first arrivals in source gathers) and interpretation (identification of primary reflections in stacked sections) phases of seismic data analysis.