The VLF-EM imaging of potential collapse on the LUSI embankment

Sungkono, Sungkono; Husein, Alwi; Prasetyo, Hardi; Bahri, Ayi Syaeful; Santos, Fernando A. Monteiro; Santosa, Bagus Jaya

doi:10.1016/j.jappgeo.2014.08.004

Cited by 27 publications

(16 citation statements)

References 77 publications

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“…In order to validate the inversion result, the result is correlated with resistivity cross section in P.79-P.82 LUSI embankment [10]. Fig.4 shows the embankment generally has relative high value of resistivity above 11 m which is correlated as the interface between first and second layers in shear wave velocity (Fig.3) because embankment is harder than the layer below [9]. Furthermore, low resistivity in embankment is interpreted by fracture containing mud fluid [9].…”

Section: Field Data Inversionmentioning

confidence: 98%

“…In the last section, we have implemented RR-PSO inversion for field data that is acquired in P.79 -P.82 LUSI (Lumpur Sidoarjo) embankment. LUSI area is composed of soft rocks which have high ductility while embankment material contains hard rock which have low ductility [9]. In this inversion, four layers model with Poisson's ratio 0.4-0.45 (for clay and sand) of each layer and large search space range is used to obtain seismic field stratigraphy.…”

Section: Field Data Inversionmentioning

confidence: 99%

See 1 more Smart Citation

RR-PSO: fast and robust algorithm to invert Rayleigh waves dispersion

Laby¹,

Sungkono²,

Santosa³

et al. 2016

ces

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Shear-wave velocity (Vs) is commonly used in geophysical and environmental studies as one of important parameters to characterize near surface study. This parameter can be estimated via the inversion of Rayleigh waves dispersion curves. In this paper, regressive-regressive particle swarm optimization (RR-PSO) algorithm has been developed and used to invert Rayleigh waves dispersion. The algorithm was tested and applied for synthetics (a free-noise and a noise-added) and field data. The results show that RR-PSO is a fast inversion tool and robust toward noise which can precisely determine the lithology of survey field.

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Section: Field Data Inversionmentioning

confidence: 98%

Section: Field Data Inversionmentioning

confidence: 99%

RR-PSO: fast and robust algorithm to invert Rayleigh waves dispersion

Laby¹,

Sungkono²,

Santosa³

et al. 2016

ces

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, it lies on soft grounds, which is composed of uncompacted clay and silt soils. Therefore, LUSI embankment faces three types of embankment failures: hydraulic, seepage, and structural failures [46]. Additionallya, LUSI embankment area is controlled by two faults: Watukosek and Siring faults [13].…”

Section: ) Inversion Of Lumpur Sidoarjo (Lusi) Embankmentmentioning

confidence: 99%

A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm

Ramadhani

Sungkono

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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A new approach based on global optimization technique is applied to invert Self-Potential (SP) data which is a highly nonlinear inversion problem. This technique is called Memory-based Hybrid Dragonfly Algorithm (MHDA). This algorithm is proposed to balance out the high exploration behavior of Dragonfly Algorithm (DA), which causes a low convergence rate and often leads to the local optimum solution. MHDA was developed by adding internal memory and iterative level hybridization into DA which successfully balanced the exploration and exploitation behaviors of DA. In order to assess the performance of MHDA, it is firstly implemented to invert the single and multiple noises contaminated in synthetic SP data, which were caused by several simple geometries of buried anomalies: sphere and inclined sheet. MHDA is subsequently implemented to invert the field SP data for several cases: buried metallic drum, landslide, and Lumpur Sidoarjo (LUSI) embankment anomalies. As a stochastic method, MHDA is able to provide Posterior Distribution Model (PDM), which contains possible solutions of the SP data inversion. PDM is obtained from the exploration behavior of MHDA. All accepted models as PDM have a lower misfit value than the specified tolerance value of the objective function in the inversion process. In this research, solutions of the synthetic and field SP data inversions are estimated by the median value of PDM. Furthermore, the uncertainty value of obtained solutions can be estimated by the standard deviation value of PDM. The inversion results of synthetic and field SP data show that MHDA is able to estimate the solutions and the uncertainty values of solutions well. It indicates that MHDA is a good and an innovative technique to be implemented in solving the SP data inversion problem.

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“…Characteristics of the VLF-EM method that utilize lowfrequency signals, causing relatively high noise in observation data. The NA-MEMD (Noise-Assisted MEMD) method is used to improve VLF-EM data observation accuracy by eliminating high-frequency noise and reducing low frequencies [10]. The inversionprocessed data is NA-MEMD filtered data which has been quantitatively analyzed using Fraser and K-Hjelt filters.…”

Section: B Vlf-em and Resistivity Data Inversionmentioning

confidence: 99%

Identification of Soil Contamination using VLF-EM and Resistivity Methods: A Case Study

Inayah

Santosa

Warnana

et al. 2019

JTS

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Hazardous waste is a waste with properties which can pollute and damage the environment, human health, and other living things. Lakardowo is a village that is suspected of being contamination by hazardous waste derived from industrial activities. Measurements with the VLF-EM and Resistivity method were used in this study. In addition, groundwater level mapping and soil sampling in locations around the industry were conducted for the XRF test. Groundwater mapping results show that groundwater flow leads to the Northeast-South and Southwest of the industrial site. The soil samples that have been tested by XRF show the presence of heavy metals, wherein hazardous wastes generally contain various types of heavy metals that are conductive or have low resistivity values. Quantitative interpretation of VLF-EM data shows the presence of low resistivity anomalies at several measurement sites, which are suspected of having been contaminated with soil by waste. Resistivity data processing results, showing a low resistivity anomaly (≤ 3 Ω.m) located to the north (near an industrial site) and spreading towards the southwest along the measurement path. The result of a combination of quantitative interpretations of both methods, obtained, the direction of anomalous flow of hazardous waste moves southeastward and towards deeper soil coating following the direction of rock coating.

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The VLF-EM imaging of potential collapse on the LUSI embankment

Cited by 27 publications

References 77 publications

RR-PSO: fast and robust algorithm to invert Rayleigh waves dispersion

RR-PSO: fast and robust algorithm to invert Rayleigh waves dispersion

A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm

Identification of Soil Contamination using VLF-EM and Resistivity Methods: A Case Study

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