scholarly journals A review and appraisal of arrival-time picking methods for downhole microseismic data

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. KS71-KS91 ◽  
Author(s):  
Jubran Akram ◽  
David W. Eaton
Keyword(s):  
Arrival Time ◽  
Hybrid Methods ◽  
Optimal Parameter ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
High Signal ◽  
Data Set ◽  
Single Level ◽  
Microseismic Data ◽  
Arrival Time Picking

We have evaluated arrival-time picking algorithms for downhole microseismic data. The picking algorithms that we considered may be classified as window-based single-level methods (e.g., energy-ratio [ER] methods), nonwindow-based single-level methods (e.g., Akaike information criterion), multilevel- or array-based methods (e.g., crosscorrelation approaches), and hybrid methods that combine a number of single-level methods (e.g., Akazawa’s method). We have determined the key parameters for each algorithm and developed recommendations for optimal parameter selection based on our analysis and experience. We evaluated the performance of these algorithms with the use of field examples from a downhole microseismic data set recorded in western Canada as well as with pseudo-synthetic microseismic data generated by adding 100 realizations of Gaussian noise to high signal-to-noise ratio microseismic waveforms. ER-based algorithms were found to be more efficient in terms of computational speed and were therefore recommended for real-time microseismic data processing. Based on the performance on pseudo-synthetic and field data sets, we found statistical, hybrid, and multilevel crosscorrelation methods to be more efficient in terms of accuracy and precision. Pick errors for S-waves are reduced significantly when data are preconditioned by applying a transformation into ray-centered coordinates.

First arrival time picking for microseismic data based on DWSW algorithm

2018 ◽  
Vol 22 (4) ◽  
pp. 833-840 ◽  
Author(s):  
Yue Li ◽  
Yue Wang ◽  
Hongbo Lin ◽  
Tie Zhong
Keyword(s):  
Arrival Time ◽  
Microseismic Data ◽  
First Arrival ◽  
First Arrival Time ◽  
Arrival Time Picking

scholarly journals A search for transiting planets in the β Pictoris system

2018 ◽  
Vol 615 ◽  
pp. A145 ◽  
Author(s):  
M. Mol Lous ◽  
E. Weenk ◽  
M. A. Kenworthy ◽  
K. Zwintz ◽  
R. Kuschnig
Keyword(s):  
Signal To Noise Ratio ◽  
Large Fraction ◽  
Young Star ◽  
High Signal ◽  
Data Set ◽  
Transiting Planets ◽  
Using Data ◽  
Exoplanet Systems ◽  
Transit Signals

Context. Transiting exoplanets provide an opportunity for the characterization of their atmospheres, and finding the brightest star in the sky with a transiting planet enables high signal-to-noise ratio observations. The Kepler satellite has detected over 365 multiple transiting exoplanet systems, a large fraction of which have nearly coplanar orbits. If one planet is seen to transit the star, then it is likely that other planets in the system will transit the star too. The bright (V = 3.86) star β Pictoris is a nearby young star with a debris disk and gas giant exoplanet, β Pictoris b, in a multi-decade orbit around it. Both the planet’s orbit and disk are almost edge-on to our line of sight. Aims. We carry out a search for any transiting planets in the β Pictoris system with orbits of less than 30 days that are coplanar with the planet β Pictoris b. Methods. We search for a planetary transit using data from the BRITE-Constellation nanosatellite BRITE-Heweliusz, analyzing the photometry using the Box-Fitting Least Squares Algorithm (BLS). The sensitivity of the method is verified by injection of artificial planetary transit signals using the Bad-Ass Transit Model cAlculatioN (BATMAN) code. Results. No planet was found in the BRITE-Constellation data set. We rule out planets larger than 0.6 RJ for periods of less than 5 days, larger than 0.75 RJ for periods of less than 10 days, and larger than 1.05 RJ for periods of less than 20 days.

Arrival-time picking of microseismic events based on MSNet

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Guanqun Sheng ◽  
Shuangyu Yang ◽  
Xiaolong Guo ◽  
Xingong Tang
Keyword(s):  
Arrival Time ◽  
Spatial Information ◽  
Synthetic Data ◽  
Microseismic Monitoring ◽  
Monitoring Data ◽  
Convolutional Network ◽  
Low Snr ◽  
Microseismic Data ◽  
Microseismic Events ◽  
Arrival Time Picking

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, as field monitoring data contain many microseismic events with low signal-to-noise ratios (SNRs), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-SNR environments. Therefore, we propose a deep convolutional network that combines the U-net and DenseNet approaches to pick arrival times automatically. This novel network, called MSNet not only retains the spatial information of any input signal or profile based on the U-net, but also extracts and integrates more essential features of events and non-events through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of the proposed method. First, we describe the structure of MSNet and the workflow of the proposed picking method. Then, datasets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different SNRs and field microseismic data from different monitoring areas. By comparing the picking results of the proposed method with the results of U-net and short-term average and long-term average (STA/LTA) methods, the effectiveness of the proposed method is verified. The arrival picking results of synthetic data and microseismic field data show that the proposed network has increased adaptability and can achieve high accuracy for picking the arrival-time of microseismic events.

scholarly journals Public HARPS radial velocity database corrected for systematic errors

2020 ◽  
Vol 636 ◽  
pp. A74 ◽  
Author(s):  
Trifon Trifonov ◽  
Lev Tal-Or ◽  
Mathias Zechmeister ◽  
Adrian Kaminski ◽  
Shay Zucker ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Signal To Noise Ratio ◽  
Auxiliary Information ◽  
Easy Access ◽  
High Signal ◽  
M Dwarfs ◽  
Data Set ◽  
New Public ◽  
Systematic Effects ◽  
User Friendly

Context. The High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph has been mounted since 2003 at the ESO 3.6 m telescope in La Silla and provides state-of-the-art stellar radial velocity (RV) measurements with a precision down to ∼1 m s−1. The spectra are extracted with a dedicated data-reduction software (DRS), and the RVs are computed by cross-correlating with a numerical mask. Aims. This study has three main aims: (i) Create easy access to the public HARPS RV data set. (ii) Apply the new public SpEctrum Radial Velocity AnaLyser (SERVAL) pipeline to the spectra, and produce a more precise RV data set. (iii) Determine whether the precision of the RVs can be further improved by correcting for small nightly systematic effects. Methods. For each star observed with HARPS, we downloaded the publicly available spectra from the ESO archive and recomputed the RVs with SERVAL. This was based on fitting each observed spectrum with a high signal-to-noise ratio template created by coadding all the available spectra of that star. We then computed nightly zero-points (NZPs) by averaging the RVs of quiet stars. Results. By analyzing the RVs of the most RV-quiet stars, whose RV scatter is < 5 m s−1, we find that SERVAL RVs are on average more precise than DRS RVs by a few percent. By investigating the NZP time series, we find three significant systematic effects whose magnitude is independent of the software that is used to derive the RV: (i) stochastic variations with a magnitude of ∼1 m s−1; (ii) long-term variations, with a magnitude of ∼1 m s−1 and a typical timescale of a few weeks; and (iii) 20–30 NZPs that significantly deviate by a few m s−1. In addition, we find small (≲1 m s−1) but significant intra-night drifts in DRS RVs before the 2015 intervention, and in SERVAL RVs after it. We confirm that the fibre exchange in 2015 caused a discontinuous RV jump that strongly depends on the spectral type of the observed star: from ∼14 m s−1 for late F-type stars to ∼ − 3 m s−1 for M dwarfs. The combined effect of extracting the RVs with SERVAL and correcting them for the systematics we find is an improved average RV precision: an improvement of ∼5% for spectra taken before the 2015 intervention, and an improvement of ∼15% for spectra taken after it. To demonstrate the quality of the new RV data set, we present an updated orbital solution of the GJ 253 two-planet system. Conclusions. Our NZP-corrected SERVAL RVs can be retrieved from a user-friendly public database. It provides more than 212 000 RVs for about 3000 stars along with much auxiliary information, such as the NZP corrections, various activity indices, and DRS-CCF products.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

scholarly journals A benchmark case study for seismic event relative location

2020 ◽  
Vol 223 (2) ◽  
pp. 1313-1326
Author(s):  
S J Gibbons ◽  
T Kværna ◽  
T Tiira ◽  
E Kozlovskaya
Keyword(s):  
Time Delay ◽  
Signal To Noise Ratio ◽  
Source Region ◽  
Ground Truth ◽  
Double Difference ◽  
High Signal ◽  
Relative Location ◽  
Data Set ◽  
Velocity Models ◽  
Event Location

Summary ‘Precision seismology’ encompasses a set of methods which use differential measurements of time-delays to estimate the relative locations of earthquakes and explosions. Delay-times estimated from signal correlations often allow far more accurate estimates of one event location relative to another than is possible using classical hypocentre determination techniques. Many different algorithms and software implementations have been developed and different assumptions and procedures can often result in significant variability between different relative event location estimates. We present a Ground Truth (GT) dataset of 55 military surface explosions in northern Finland in 2007 that all took place within 300 m of each other. The explosions were recorded with a high signal-to-noise ratio to distances of about 2°, and the exceptional waveform similarity between the signals from the different explosions allows for accurate correlation-based time-delay measurements. With exact coordinates for the explosions, we are able to assess the fidelity of relative location estimates made using any location algorithm or implementation. Applying double-difference calculations using two different 1-D velocity models for the region results in hypocentre-to-hypocentre distances which are too short and it is clear that the wavefield leaving the source region is more complicated than predicted by the models. Using the GT event coordinates, we are able to measure the slowness vectors associated with each outgoing ray from the source region. We demonstrate that, had such corrections been available, a significant improvement in the relative location estimates would have resulted. In practice we would of course need to solve for event hypocentres and slowness corrections simultaneously, and significant work will be needed to upgrade relative location algorithms to accommodate uncertainty in the form of the outgoing wavefield. We present this data set, together with GT coordinates, raw waveforms for all events on six regional stations, and tables of time-delay measurements, as a reference benchmark by which relative location algorithms and software can be evaluated.

Automated arrival-time picking using a pixel-level network

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V415-V423
Author(s):  
Yuanyuan Ma ◽  
Siyuan Cao ◽  
James W. Rector ◽  
Zhishuai Zhang
Keyword(s):  
Seismic Data ◽  
Arrival Time ◽  
Spatial Information ◽  
Training Data ◽  
Convolutional Network ◽  
Data Set ◽  
Arrival Times ◽  
Data Volume ◽  
Borehole Seismic ◽  
Arrival Time Picking

Arrival-time picking is an essential step in seismic processing and imaging. The explosion of seismic data volume requires automated arrival-time picking in a faster and more reliable way than existing methods. We have treated arrival-time picking as a binary image segmentation problem and used an improved pixel-wise convolutional network to pick arrival times automatically. Incorporating continuous spatial information in training enables us to preserve the arrival-time correlation between nearby traces, thus helping to reduce the risk of picking outliers that are common in a traditional trace-by-trace picking method. To train the network, we first convert seismic traces into gray-scale images. Image pixels before manually picked arrival times are labeled with zeros, and those after are tagged with ones. After training and validation, the network automatically learns representative features and generates a probability map to predict the arrival time. We apply the network to a field microseismic data set that was not used for training or validation to test the performance of the method. Then, we analyze the effects of training data volume and signal-to-noise ratio on our autopicking method. We also find the difference between 1D and 2D training data with borehole seismic data. Microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.

scholarly journals The VANDELS ESO public spectroscopic survey: Observations and first data release

2018 ◽  
Vol 616 ◽  
pp. A174 ◽  
Author(s):  
L. Pentericci ◽  
R. J. McLure ◽  
B. Garilli ◽  
O. Cucciati ◽  
P. Franzetti ◽  
...  
Keyword(s):  
Galaxy Evolution ◽  
Spectroscopic Data ◽  
Signal To Noise Ratio ◽  
High Redshift ◽  
Measurement Procedure ◽  
Integration Time ◽  
High Signal ◽  
Redshift Distribution ◽  
Data Set ◽  
Data Release

This paper describes the observations and the first data release (DR1) of the ESO public spectroscopic survey “VANDELS, a deep VIMOS survey of the CANDELS CDFS and UDS fields”. The main targets of VANDELS are star-forming galaxies at redshift 2.4 < z < 5.5, an epoch when the Universe had not yet reached 20% of its current age, and massive passive galaxies in the range 1 < z < 2.5. By adopting a strategy of ultra-long exposure times, ranging from a minimum of 20 h to a maximum of 80 h per source, VANDELS is specifically designed to be the deepest-ever spectroscopic survey of the high-redshift Universe. Exploiting the red sensitivity of the refurbished VIMOS spectrograph, the survey is obtaining ultra-deep optical spectroscopy covering the wavelength range 4800–10 000 Å with a sufficiently high signal-to-noise ratio to investigate the astrophysics of high-redshift galaxy evolution via detailed absorption line studies of well-defined samples of high-redshift galaxies. VANDELS-DR1 is the release of all medium-resolution spectroscopic data obtained during the first season of observations, on a 0.2 square degree area centered around the CANDELS-CDFS (Chandra deep-field south) and CANDELS-UDS (ultra-deep survey) areas. It includes data for all galaxies for which the total (or half of the total) scheduled integration time was completed. The DR1 contains 879 individual objects, approximately half in each of the two fields, that have a measured redshift, with the highest reliable redshifts reaching zspec ~ 6. In DR1 we include fully wavelength-calibrated and flux-calibrated 1D spectra, the associated error spectrum and sky spectrum, and the associated wavelength-calibrated 2D spectra. We also provide a catalog with the essential galaxy parameters, including spectroscopic redshifts and redshift quality flags measured by the collaboration. We present the survey layout and observations, the data reduction and redshift measurement procedure, and the general properties of the VANDELS-DR1 sample. In particular, we discuss the spectroscopic redshift distribution and the accuracy of the photometricredshifts for each individual target category, and we provide some examples of data products for the various target typesand the different quality flags. All VANDELS-DR1 data are publicly available and can be retrieved from the ESO archive. Two further data releases are foreseen in the next two years, and a final data release is currently scheduled for June 2020, which will include an improved rereduction of the entire spectroscopic data set.

Ultrasonic Cure Monitoring of GFRP Laminates

2015 ◽  
Vol 1110 ◽  
pp. 82-87 ◽  
Author(s):  
Akihiro Wada ◽  
Chang Hae Pak ◽  
Eiji Kitagawa ◽  
Hiroshi Ito ◽  
Yuki Sasaki
Keyword(s):  
Lamb Wave ◽  
Arrival Time ◽  
Early Stage ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
Curing Process ◽  
Zero Crossing ◽  
Cure Monitoring ◽  
Accuracy Of Measurement

Ultrasonic testing is applied to monitor the curing process of GFRP laminates used for rehabilitation of aged sewerage pipes. Instead of conventional method in which through-thickness ultrasonic wave is used, wheel type probes are adopted to transmit and receive in-plane Lamb wave. It is well known that wave velocity depends on the stiffness of the propagating medium, so that it could be used as cure index of resin. However, because of the relatively low signal-to-noise ratio of detected signals, determination of wave arrival time with zero-crossing detecting method leads to nonnegligible error. In order to improve the precision of measurement, AIC (Akaike Information Criterion) is introduced and the arrival time of Lamb wave is monitored in curing process. Two types of waveform, pulse and burst waves are examined to confirm the effect of waveform on the accuracy of measurement. It is found that the arrival time of Lamb wave decreases with the increase of cure degree, and approach asymptotically to constant value during post cure process. Also, AIC analysis with pulse wave is found to be more reliable to monitor the early stage of resin cure.

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