Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide

Sompotan, Armstrong; Pasasa, Linus; Sule, Rachmat

doi:10.5614/itbj.eng.sci.2011.43.3.1

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Afterwards the neural network was tested using parts excluded from the training set. This procedure or, generalization phase calculates the model characteristics corresponding of unknown input This study is based on application of back propagation with tan-sigmoid computing unit which allows the output data range from -1-1, so the data needs to be simplified in order to run as activation function (Sompotan et al, 2011). The neural network contains three layers that are an input layer, a hidden layer and an output layer (Fig.…”

Section: Kriging Methodmentioning

confidence: 99%

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Analysis of Ionospheric Precursor of Earthquake using GIM-TEC, Kriging and Neural Network

Sompota¹,

Puspito²,

Joelianto³

et al. 2015

Asian J. of Earth Sciences

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Analysis of ionospheric precursor is not easy because the ionosphere is very dynamic as well as the earthquake phenomena. If the analysis method is the same dynamics with the earthquake phenomena, the estimation of earthquake parameters is possible to be realized. Neural network is an adaptive system that changes its structure to solve the problem during a learning phase. Therefore, the neural network is potentially to estimate the parameter of earthquake based on ionospheric precursor. A preliminary attempt was made to construct the neural network that can estimate the epicenter area. The GIM-TEC star method is useful to determine ionospheric anomalies associated with large earthquakes as ionospheric precursor data. The Kriging method is good to interpolate GIM-TEC star data as input of neural networks to estimate the epicenter area. The conclusion of five models of the ionosphere anomalies due to seismic activity show that the epicenter is at the edge of the less developed anomalies, whereas, for the growing anomalies, the epicenter is always located near the boundary of high and low density of TEC anomalies. The boundary is projection of the boundary of the unstressed and stressed rock area below the earth's surface.

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Section: Kriging Methodmentioning

confidence: 99%

“…Earth Sci., 8 (2): 32-44, 2015 epicenter area. The performance goal for all neural network applications was set to 1e-005 (Sompotan et al, 2011). In other words, the generalization performance is considered accurate for different models, when this goal is achieved.…”

Section: Kriging Methodmentioning

confidence: 99%

Analysis of Ionospheric Precursor of Earthquake using GIM-TEC, Kriging and Neural Network

Sompota¹,

Puspito²,

Joelianto³

et al. 2015

Asian J. of Earth Sciences

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“…2016)), a lower percentage (15%) applied explosives (Havenith et al, 2000;Heincke et al, 2010;Jongmans et al, 2009;Samyn et al, 2012;Solberg et al, 2012); 15% considered the combined sources, i.e., explosives, handy hammer and weight-drop (Ferrucci et al, 2000;Havenith et al, 2002;Jacob et al, 2018;Mauritsch et al, 2000;Wang et al, 2016); and there is no information regarding a signal supplier for 17% (Fig. 4) (Bekler et al, 2011;Capizzi and Martorana, 2014;Kul Yahşi and Ersoy, 2018;Ozcep et al, 2012;Sompotan et al, 2013;Yilmaz and Kamaci, 2018). Figure 5 shows the comparison among the power spectra for a sledgehammer, weightdrop, and dynamite.…”

Section: Data Acquisitionmentioning

confidence: 99%

Landslide Investigation Using Seismic Refraction Tomography Method: A Review

Imani

El-Raouf

Tian

2021

Annals of Geophysics

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Since the early 1960s, near-surface seismic refraction tomography (SRT) has been extensively used as a non-invasive and cost-effective geophysical method to characterize complex geological structures for landslide investigation. This geophysical technique is able to characterize the slope material, the sliding surface's geometry, the landslide mass movement, the physical properties of media, and the water saturation effects on the slope. Therefore, this method has become an appropriate method due to the increasing progress of novel algorithms and the improvements of field-data collection systems. In this paper, we attempt to review the essential research that investigated various types of landslides influenced by water saturation and landslide materials and identified in various areas, since the year 2000. Significant conclusions obtained by applying different survey strategies and data processing algorithms in seismic refraction surveys are widely discussed concentrating on the advantages and disadvantages of this method. The main results obtained by the few available studies applying time-lapse SRT (TLSRT) are particularly analyzed.

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“…Yu (2010) introduced two main approaches for estimating pore pressure: geological using basing modeling and geophysical using seismic velocity. Sompotan et al (2011) estimated the interval velocity cube for a southwestern oilfield in Iran. Maurya et al (2020) published a book about Post-stack data analysis.…”

Section: Introductionmentioning

confidence: 99%

Inversion of seismic data to modeling the Interval Velocity in an Oilfield of SW Iran

Kianoush,

Mohammadi,

Hosseini

et al. 2023

Preprint

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Seismic velocity is considered the best attribute related to formation pressure changes. Integrating seismic attributes and well-logging data through seismic inversion predicts the reservoir characteristics across the field with the highest accuracy. This study especially presents seismic velocity for the whole south Azadegan Field in SW Iran for carbonate formations. The considered dataset includes 3D seismic data, vertical seismic profiling (VSP), logging data of 23 wells, and geological information. Here, we estimated the interval velocity using post-stack migration velocity, seismic inversion, and the relationship between the acoustic impedance (AI) model and the sonic log to predict formation pressure. As a result, the correlation coefficient of 0.71 and a high inversion accuracy (8.76% relative error) is concluded. The actual and predicted P-wave (Vp) correlation coefficient is calculated as 0.74 and all sevens as 0.79 using an AI seismic attribute. Thus, the estimated Vp agrees with the original well-log values. Inverted AI cubes in the deeper formations of the field are about 8000-15000 [(m/s)*(g/cm3)], which could be referred to as calcareous formations. The correlation of the Vp cube resulting from the Sequential Gaussian simulation (SGS) considering co-kriging with the AI, with the initial velocity cube using the inverse distance weighted (IDW) method being 0.54 is more than the same method applied with interval migration velocity trend in co-kriging. The anisotropy of the final Vp cube for the vertical variogram range is 96m, and for major and minor directions is 11850 m.

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Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide

Cited by 5 publications

References 15 publications

Analysis of Ionospheric Precursor of Earthquake using GIM-TEC, Kriging and Neural Network

Analysis of Ionospheric Precursor of Earthquake using GIM-TEC, Kriging and Neural Network

Landslide Investigation Using Seismic Refraction Tomography Method: A Review

Inversion of seismic data to modeling the Interval Velocity in an Oilfield of SW Iran

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