Real Time Continuous Reservoir Strength Prediction from Grain Size Distribution Information Using Neural Network

2006 ◽  
Author(s):  
Gbenga Oluyemi ◽  
Babs Oyeneyin ◽  
Chris MacLeod
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Size Distribution ◽  
Real Time ◽  
Grain Size Distribution ◽  
Strength Prediction

scholarly journals Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

2021 ◽  
Vol 21 (5) ◽  
pp. 1667-1683
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Indian Ocean ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Deep Neural Network ◽  
Inverse Model ◽  
Indian Ocean Tsunami ◽  
Flow Conditions ◽  
2004 Indian Ocean Tsunami

Abstract. The 2004 Indian Ocean tsunami caused significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the physical characteristics of modern tsunamis.

scholarly journals Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1461 ◽  
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Discrete Element Method ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Discrete Element ◽  
Fine Sediment ◽  
Gravel Bed ◽  
Artificial Neural

In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in r ecosystem management. Conventional porosity prediction methods are restricting in terms of the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure of the packing gravel-bed and fine sediment infiltration processes under various forces. The results of the DEM simulations were verified with the experimental data of porosity and fine sediment distribution. Further, an algorithm was developed for calculating high-resolution results of porosity and grain size distribution in vertical and horizontal directions from the DEM results, which were applied to develop a Feed Forward Neural Network (FNN) to predict bed porosity based on grain size distribution. The reliable results of DEM simulation and FNN prediction confirm that our framework is successful in predicting porosity change of gravel-bed.

scholarly journals Examples of Multi-Sensor Determination of Eruptive Source Parameters of Explosive Events at Mount Etna

2021 ◽  
Vol 13 (11) ◽  
pp. 2097
Author(s):  
Valentin Freret-Lorgeril ◽  
Costanza Bonadonna ◽  
Stefano Corradini ◽  
Franck Donnadieu ◽  
Lorenzo Guerrieri ◽  
...  
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Real Time ◽  
Grain Size Distribution ◽  
Source Parameters ◽  
Radar Data ◽  
Mount Etna ◽  
Infrared Sensors ◽  
Paroxysmal Activity

Multi-sensor strategies are key to the real-time determination of eruptive source parameters (ESPs) of explosive eruptions necessary to forecast accurately both tephra dispersal and deposition. To explore the capacity of these strategies in various eruptive conditions, we analyze data acquired by two Doppler radars, ground- and satellite-based infrared sensors, one infrasound array, visible video-monitoring cameras as well as data from tephra-fallout deposits associated with a weak and a strong paroxysmal event at Mount Etna (Italy). We find that the different sensors provide complementary observations that should be critically analyzed and combined to provide comprehensive estimates of ESPs. First, all measurements of plume height agree during the strong paroxysmal activity considered, whereas some discrepancies are found for the weak paroxysm due to rapid plume and cloud dilution. Second, the event duration, key to convert the total erupted mass (TEM) in the mass eruption rate (MER) and vice versa, varies depending on the sensor used, providing information on different phases of the paroxysm (i.e., unsteady lava fountaining, lava fountain-fed tephra plume, waning phase associated with plume and cloud expansion in the atmosphere). As a result, TEM and MER derived from different sensors also correspond to the different phases of the paroxysms. Finally, satellite retrievals for grain-size can be combined with radar data to provide a first approximation of total grain-size distribution (TGSD) in near real-time. Such a TGSD shows a promising agreement with the TGSD derived from the combination of satellite data and whole deposit grain-size distribution (WDGSD).

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