scholarly journals Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland, from the analysis of correlograms

Volcanica ◽  
2021 ◽  
pp. 135-147
Author(s):  
Sylvain Nowé ◽  
Thomas Lecocq ◽  
Corentin Caudron ◽  
Kristín Jónsdóttir ◽  
Frank Pattyn
Keyword(s):  
Seismic Data ◽  
Seismic Noise ◽  
Search Method ◽  
Seismic Interferometry ◽  
Seismic Sources ◽  
Grid Search ◽  
Noise Sources ◽  
Grid Search Method ◽  
Volcanic Events ◽  
Propagation Velocities

In this study, we locate and characterise the main seismic noise sources in the region of the Vatnajökull icecap (Iceland). Vatnajökull is the largest Icelandic icecap, covering several active volcanoes. The seismic context is very complex, with glacial and volcanic events occurring simultaneously and the classification between the two can become cumbersome. Using seismic interferometry on continuous seismic data (2011–2019), we calculate the propagation velocities and locate the main seismic sources by using hyperbolic geometry and a grid-search method. We identify and characterise permanent oceanic sources, seasonal glacial-related sources, and episodic volcanic sources. These results give a better understanding of the background seismic noise sources in this region and could allow the identification of seismic sources associated with potentially threatening events in real-time.

Permanent, seasonal, and episodic seismic sources around Vatnajökull, Iceland from the analyses of correlograms

2021 ◽  
Author(s):  
Sylvain Nowé ◽  
Thomas Lecocq ◽  
Corentin Caudron ◽  
Kristín Jónsdóttir ◽  
Frank Pattyn
Keyword(s):  
Seismic Noise ◽  
Cross Correlation ◽  
Seismic Interferometry ◽  
Seismic Sources ◽  
Monitoring Method ◽  
West Part ◽  
Location Methods ◽  
Seismic Background ◽  
The Impact ◽  
Propagation Velocities

<p>This study aims at characterizing different seismic sources in the region of the Vatnajökull glacier using seismic interferometry. Vatnajökull is the largest Icelandic icecap, covering 4active volcanic systems. The seismic context is therefore very complex with glacial and volcanic events occurring simultaneously and a classification between the two can become cumbersome. </p><p>We used seismic interferometry or cross-correlation of seismic noise on seismic data from 2011 to 2019). Being based on continuous records, this passive monitoring method is not relying on earthquakes to locate seismic sources. We computed the cross-correlation functions between every pair of seismic stations using MSNoise for different frequency bands, from 0.5 to 8 Hz. The first step towards the location of seismic sources was to calculate the propagation velocities for each frequency range. The total range of velocities is between 1.39 km/s and 3.92 km/s. Then, we used two different location methods based on the calculated propagation velocities. The first method is based on hyperbole’s geometry and provides the location of seismic sources as the intersection between several hyperboles, while the second one, the Ballmer’s method (Ballmer et al. 2013), is based on the calculation of theoretical differential times and provides location probabilities for the seismic sources. We located and characterized persistent oceanic seismic noise located along the southern shoreline of Iceland potentially associated with waves activity and geometry of the shore, as well as a seasonal glacial tremor around outlet glaciers in the west part of the Vatnajökull icecap, potentially linked to glacial processes inside the glacier or in the glacial rivers. The uncertainty of a few kilometers is observed. Some limitations exist for these methods. For example, The Ballmer’s method (Ballmer et al. 2013) is reliable for seismic sources inside the seismic network but can only give an azimuthal direction for seismic sources located outside of it. When using hyperboles, slightly different propagation velocities between pairs of stations can affect the precision of the intersection. Therefore, the association of the two methods is important to diminish the impact of these limitations. </p><p>These results provide a better understanding of the seismic background of this region and will be compared and validated with other localization methods in the future.</p>

Moho depth determination in the Eastern Alps-Pannonian basin-Carpathian mountains region based on H-K grid search method and CCP migration of receiver functions

2020 ◽  
Author(s):  
Dániel Kalmár ◽  
György Hetényi ◽  
István Bondár ◽  
Keyword(s):  
Pannonian Basin ◽  
Eastern Alps ◽  
Receiver Functions ◽  
Search Method ◽  
Moho Depth ◽  
Grid Search ◽  
Carpathian Mountains ◽  
Entire Study ◽  
Grid Search Method ◽  
Private Network

<p>We perform P-to-S receiver function analysis to determine a detailed map of the crust-mantle boundary in the Eastern Alps–Pannonian basin–Carpathian mountains junction. We use data from the AlpArray Seismic Network, the Carpathian Basin Project and the South Carpathian Project temporary seismic networks, the permanent stations of the Hungarian National Seismological network, stations of a private network in Hungary as well as selected permanent seismological stations in neighbouring countries for the time period between 2004.01.01. and 2019.03.31. Altogether 221 seismological stations are used in the analysis. Owing to the dense station coverage we can achieve so far unprecedented resolution, thus extending our previous work on the region. We applied three-fold quality control, the first two on the observed waveforms and the third on the calculated radial receiver functions, calculated by the iterative time-domain deconvolution approach. The Moho depth was determined by two independent approaches, the common conversion point (CCP) migration with a local velocity model and the H-K grid search. We show cross-sections beneath the entire investigated area, and concentrate on major structural elements such as the AlCaPa and Tisza-Dacia blocks, the Mid-Hungarian Fault Zone and the Balaton Line. Finally, we present the Moho map obtained by the H-K grid search method and pre-stack CCP migration and interpolation over the entire study area, and compare results of two independent methods to prior knowledge.</p>

scholarly journals Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1384
Author(s):  
Shuang Song ◽  
Shugang Li ◽  
Tianjun Zhang ◽  
Li Ma ◽  
Shaobo Pan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Search Method ◽  
Evaluation Index ◽  
Batch Size ◽  
Grid Search ◽  
Gas Concentration ◽  
Grid Search Method ◽  
Performance Evaluation Index ◽  
The Mean ◽  
Concentration Prediction

The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance evaluation index of the model’s prediction method; used the grid search method to optimize the hyperparameters of the batch size; and used the number of neurons, the learning rate, the discard ratio, the network depth, and the early stopping method to prevent overfitting. The gas concentration prediction models—based on RNN and PSO-SVR and PSO-Adam-BP neural networks—were compared and analyzed experimentally with the mean absolute percentage error (MAPE) as the performance evaluation index. The result show that using the grid search method to adjust the batch size, the number of neurons, the learning rate, the discard ratio, and the network depth can effectively find the optimal hyperparameter combination. The training error can be reduced to 0.0195. Therefore, Adam’s optimized RNN gas concentration prediction model had higher accuracy and stability than the BP neural network and SVR. During training, the mean absolute error (MAE) could be reduced to 0.0573, and the root mean squared error (RMSE) could be reduced to 0.0167; however, the MAPE could be reduced to 0.3384% during prediction. The RNN gas concentration prediction model and parameter optimization method based on Adam optimization can effectively predict gas concentration. This method shows high accuracy in the prediction of gas concentration time series and can be used as a reference model for predicting mine gas concentration.

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