Hypocenter relocation using a fast grid search method and a 3-D seismic velocity model for the Sumatra region

2013 ◽  
Author(s):  
Hendro Nugroho ◽  
Sri Widiyantoro ◽  
Andri Dian Nugraha
Keyword(s):  
Seismic Velocity ◽  
Velocity Model ◽  
Search Method ◽  
Grid Search ◽  
Grid Search Method ◽  
Hypocenter Relocation

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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