Relocation of seismicity of the Pannonian Basin using the Bayesloc multiple event location algorithm between 1996 and 2019

2020 ◽  
Author(s):  
Barbara Czecze ◽  
István Bondár
Keyword(s):  
Pannonian Basin ◽  
Ground Truth ◽  
Velocity Model ◽  
Measure Data ◽  
Seismic Events ◽  
Seismicity Pattern ◽  
Statistical Framework ◽  
Event Location ◽  
Location Algorithm ◽  
Location Methods

<p>The objective of this work was to relocate the entire seismicity of the Pannonian Basin with the Bayesloc algorithm, a Markov-Chain Monte Carlo inversion scheme using a Bayesian statistical framework.</p><p><span>In the Hungarian National Seismological Bulletin the magnitudes and event locations are determined with the iLoc location algorithm using the 3D global RSTT velocity model, and we used these locations as initial coordinates. In our work, we have used all of the instrumentally registered seismic events between 1996 and 2019 in the Pannonian Basin.</span></p><p><span>During data preprocessing we used graph theory to measure data connectivity. Similar to all multiple-event location methods, Bayesloc performs better when events are recorded on a common network. </span></p><p><span>We used</span> <span>several hundreds</span> <span>of ground truth events (quarry blasts, mine explosions, earthquakes)</span> <span>to tie down</span> <span>the seismicity pattern to known ground truth locations by giving them tighter prior distributions.</span></p><p><span>Based on the day-time peak on the origin-hour distribution of the bulletin earthquakes we assume that there are anthropogenic events labeled as earthquakes in the catalog, therefore we created a „Suspected</span> <span>explosions (SX)” group to set prior constrains.</span></p><p><span>The results show that the events around the mines are dramatically better clustered. The prior constraints contributed remarkably to the outcome of the relocation. We show that the results present an improved view of the seismicity of the region.</span></p>

Reassessment of source parameters of ‘major’ southern African earthquakes

2020 ◽  
Vol 123 (1) ◽  
pp. 59-74
Author(s):  
V. Midzi ◽  
T. Pule ◽  
T. Mulabisana ◽  
B. Zulu ◽  
B. Manzunzu
Keyword(s):  
Source Parameters ◽  
Ground Truth ◽  
Velocity Model ◽  
Parametric Data ◽  
Reliable Source ◽  
Hazard And Risk ◽  
Location Algorithm ◽  
Phase Data ◽  
International Seismological Centre ◽  
Large Earthquakes

Abstract Moderate to large earthquakes within an earthquake catalogue contribute significantly to the seismic hazard and risk assessment results of any region. Thus it is prudent to ensure these events have reliable source parameters (epicentres and magnitude). The dataset of events compiled in this study contains a total of 117 instrumentally recorded events of magnitude M ≥5.0, whose parameters were obtained from the Council for Geoscience (CGS) and International Seismological Centre (ISC) databases. The events are mostly located in South Africa with a few in neighbouring countries. Parametric data made up of all available phase data and amplitudes associated with each of the earthquakes were compiled. The availability of these data enabled the earthquake epicentres and magnitude values to be recalculated using the velocity model and the local magnitude relation that are currently being used by the CGS in its analysis of national seismic data. The accuracy of the relocations was determined by producing and analysing three parameters, the azimuthal distribution of seismograph stations (GAP), root-mean-square of travel time residuals (RMS) and epicenter location error data. The analysis of these parameters showed that there was an improvement in the accuracy of the relocated events. Using the ISC location algorithm, iLOC, eight preselected events were further analysed. From this analysis, two earthquakes were found to satisfy the conditions for Ground Truth (GT595%) candidacy whilst four events satisfied the criteria for GT2090% candidacy.

Rapid Earthquake Association and Location

2019 ◽  
Vol 90 (6) ◽  
pp. 2276-2284 ◽  
Author(s):  
Miao Zhang ◽  
William L. Ellsworth ◽  
Gregory C. Beroza
Keyword(s):  
Travel Time ◽  
Time Windows ◽  
Grid Point ◽  
Seismic Events ◽  
Relative Location ◽  
Central Apennines ◽  
Location Method ◽  
Event Location ◽  
Seismic Characterization ◽  
Location Methods

ABSTRACT Rapid association of seismic phases and event location are crucial for real‐time seismic monitoring. We propose a new method, named rapid earthquake association and location (REAL), for associating seismic phases and locating seismic events rapidly, simultaneously, and automatically. REAL combines the advantages of both pick‐based and waveform‐based detection and location methods. It associates arrivals of different seismic phases and locates seismic events primarily through counting the number of P and S picks and secondarily from travel‐time residuals. A group of picks are associated with a particular earthquake if there are enough picks within the theoretical travel‐time windows. The location is determined to be at the grid point with the most picks, and if multiple locations have the same maximum number of picks, the grid point among them with smallest travel‐time residuals. We refine seismic locations using a least‐squares location method (VELEST) and a high‐precision relative location method (hypoDD). REAL can be used for rapid seismic characterization due to its computational efficiency. As an example application, we apply REAL to earthquakes in the 2016 central Apennines, Italy, earthquake sequence occurring during a five‐day period in October 2016, midway in time between the two largest earthquakes. We associate and locate more than three times as many events (3341) as are in Italy's National Institute of Geophysics and Volcanology routine catalog (862). The spatial distribution of these relocated earthquakes shows a similar but more concentrated pattern relative to the cataloged events. Our study demonstrates that it is possible to characterize seismicity automatically and quickly using REAL and seismic picks.

Results of the downhole microseismic monitoring at a pilot hydraulic fracturing site in Poland — Part 1: Event location and stimulation performance

2018 ◽  
Vol 6 (3) ◽  
pp. SH39-SH48 ◽  
Author(s):  
Wojciech Gajek ◽  
Jacek Trojanowski ◽  
Michał Malinowski ◽  
Marek Jarosiński ◽  
Marko Riedel
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Anisotropy ◽  
Transverse Isotropy ◽  
Velocity Model ◽  
Microseismic Monitoring ◽  
Baltic Basin ◽  
Hydraulic Stimulation ◽  
Lower Paleozoic ◽  
Event Location ◽  
Microseismic Events

A precise velocity model is necessary to obtain reliable locations of microseismic events, which provide information about the effectiveness of the hydraulic stimulation. Seismic anisotropy plays an important role in microseismic event location by imposing the dependency between wave velocities and its propagation direction. Building an anisotropic velocity model that accounts for that effect allows for more accurate location of microseismic events. We have used downhole microseismic records from a pilot hydraulic fracturing experiment in Lower-Paleozoic shale gas play in the Baltic Basin, Northern Poland, to obtain accurate microseismic events locations. We have developed a workflow for a vertical transverse isotropy velocity model construction when facing a challenging absence of horizontally polarized S-waves in perforation shot data, which carry information about Thomsen’s [Formula: see text] parameter and provide valuable constraints for locating microseismic events. We extract effective [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text] for each layer from the P- and SV-wave arrivals of perforation shots, whereas the unresolved [Formula: see text] is retrieved afterward from the SH-SV-wave delay time of selected microseismic events. An inverted velocity model provides more reliable location of microseismic events, which then becomes an essential input for evaluating the hydraulic stimulation job effectiveness in the geomechanical context. We evaluate the influence of the preexisting fracture sets and obliquity between the borehole trajectory and principal horizontal stress direction on the hydraulic treatment performance. The fracturing fluid migrates to previously fractured zones, while the growth of the microseismic volume in consecutive stages is caused by increased penetration of the above-lying lithologic formations.

Automated microseismic event location by amplitude stacking and semblance

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. KS191-KS210 ◽  
Author(s):  
Chengwei Zhang ◽  
Wenxiao Qiao ◽  
Xiaohua Che ◽  
Junqiang Lu ◽  
Baiyong Men
Keyword(s):  
Body Waves ◽  
S Waves ◽  
Advantages And Disadvantages ◽  
Location Uncertainty ◽  
Event Location ◽  
Time Average ◽  
Microseismic Events ◽  
Location Methods ◽  
Microseismic Event Location ◽  
Microseismic Event

Without the need to pick the arrival times of P- and S-waves, migration-based location methods, such as semblance-based and amplitude-stacking-based location methods, are best applied to microseismic events. By comparing and analyzing the advantages and disadvantages of these two methods, we have developed a new location method using amplitude information and semblance. First, we use the two-point ray-tracing method to calculate the traveltime of body waves from the trial point to each receiver, which determines the time-window positions of the P- and S-waves on all traces. Then, we calculate the semblance of the waveforms and the amplitude stacking of the ratio between the short-time average and the long-time average is computed upon the original waveform over the windows. Finally, the semblance weighted by amplitude stacking is used to image the spatial location of the microseismic events. Using experimental and synthetic data considering different factors that may affect the location result (e.g., the signal-to-noise ratio of the waveforms, the scale of the observation array, and the horizontal and vertical distances from the source to fracture zones), we perform microseismic event location with all three methods. According to the source imaging results from experimental and synthetic tests, the semblance method has great location uncertainty in the radial direction but it has good constraints in the circumferential direction; the amplitude-stacking method exhibits the opposite result; and the weighted-semblance method has good constraints in the circumferential and radial directions because it inherits the advantages of semblance-based and amplitude-stacking-based methods. Therefore, compared with existing migration-based location methods, our weighted-semblance method indicates stronger stability and lower location uncertainty, even when downhole monitoring is conducted with a limited aperture of the receiver array.

scholarly journals Statistical framework for validation without ground truth of choroidal thickness changes detection

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218776
Author(s):  
Tiziano Ronchetti ◽  
Christoph Jud ◽  
Peter M. Maloca ◽  
Selim Orgül ◽  
Alina T. Giger ◽  
...  
Keyword(s):  
Choroidal Thickness ◽  
Ground Truth ◽  
Statistical Framework ◽  
Changes Detection

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network

2019 ◽  
Vol 7 (3) ◽  
pp. SE113-SE122 ◽  
Author(s):  
Yunzhi Shi ◽  
Xinming Wu ◽  
Sergey Fomel
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Seismic Image ◽  
Data Generator

Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks (CNNs) with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large number of subvolumes into the network while using salt/nonsalt binary labels generated by thresholding the velocity model as ground truth labels. We test the model on validation data sets and compare the blind test predictions with the ground truth. Our results indicate that our method is capable of automatically capturing subtle salt features from the 3D seismic image with less or no need for manual input. We further test the model on a field example to indicate the generalization of this deep CNN method across different data sets.

A Combined Location Method for Mobile Robots Based on Dead Reckoning and WLAN

2014 ◽  
Vol 631-632 ◽  
pp. 649-653 ◽  
Author(s):  
Fang Jia ◽  
Kui Liu ◽  
De Cheng Xu
Keyword(s):  
Mobile Robots ◽  
Information Fusion ◽  
Low Cost ◽  
Dead Reckoning ◽  
Probability Matching ◽  
Location Method ◽  
Indoor Location ◽  
Location Algorithm ◽  
Location Methods ◽  
Received Signal Strength Indication

To minimize the deficiency of the existing indoor location methods for mobile robots, the RSSI (received signal strength indication) model of WLAN is established. Then a combined location method for mobile robots based on DR (dead reckoning) and WLAN is proposed, which employs PMLA (probability matching location algorithm) and KF (Kalman filter) for information fusion. Simulation results reveal that the combined location approach works well in eliminating the cumulative error of DR and reducing the fluctuation of WLAN location. As a result, the proposed method is capable of enhancing the positioning accuracy of mobile robots to a certain extent, promising a low-cost and reliable location scheme for its development.

Ground truth seismic events and location capability at Degelen mountain, Kazakhstan

2002 ◽  
Vol 131 (2) ◽  
pp. 155-171 ◽  
Author(s):  
Chad Trabant ◽  
Clifford Thurber ◽  
William Leith
Keyword(s):  
Ground Truth ◽  
Seismic Events
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