Robust interpretation of single and multiple self-potential anomalies via flower pollination algorithm

Sungkono, Sungkono

doi:10.1007/s12517-020-5079-4

Cited by 19 publications

(6 citation statements)

References 56 publications

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“…Each anomaly controls a different area with uncertainty less than 21 m for position and 16 m for depth. Furthermore, the data were inverted using global optimization methods [4,42,43], where the results suggest that all the anomaly bodies at this line are horizontal cylinders. The anomalies can be interpreted as piping or seepage [3].…”

Section: Figurementioning

confidence: 99%

Self-Potential Method to Assess Embankment Stability: A Study related to the Sidoarjo Mud Flow

Sungkono

Wasilah

Widyaningrum

et al. 2020

J. Eng. Technol. Sci.

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Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) can be used for qualitative interpretation of self-potential (SP) data.  Continuous Wavelet Transform (CWT) analysis was applied to identify multiple anomaly sources from SP data.  NA-MEMD and CWT were applied to SP data for qualitative and quantitative interpretations, respectively.  The combination of NA-MEMD and CWT on SP data was used to identify the location of fractures and seepage in the LUSI embankment.  The interpretation of the result was supported by direct current resistivity.

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Section: Figurementioning

confidence: 99%

Self-Potential Method to Assess Embankment Stability: A Study related to the Sidoarjo Mud Flow

Sungkono

Wasilah

Widyaningrum

et al. 2020

J. Eng. Technol. Sci.

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“…Table 1 presents the synthetic model parameters, the parameter model bounds for the search spaces and the inversion results from the IMSOS algorithm. Recently, MHAs have succeeded in recovering the model parameters of multiple SP anomaly sources, including BHA [9], PSO [25], the Genetic Prices Algorithm (GPA) [27], Very Fast Simulated Annealing (VFSA) [28], the Flower Pollination Algorithm (FPA) [10] and the Whale Optimization Algorithm (WOA) [29]. GPA and VFSA need many more forward modeling evaluations for global optimization compared to the other algorithms [9,10,25,29].…”

Section: Self-potential Datamentioning

confidence: 99%

“…Recently, MHAs have succeeded in recovering the model parameters of multiple SP anomaly sources, including BHA [9], PSO [25], the Genetic Prices Algorithm (GPA) [27], Very Fast Simulated Annealing (VFSA) [28], the Flower Pollination Algorithm (FPA) [10] and the Whale Optimization Algorithm (WOA) [29]. GPA and VFSA need many more forward modeling evaluations for global optimization compared to the other algorithms [9,10,25,29]. We compared inversion using IMSOS to PSO [25] and BHA [9], where both IMSOS and BHA algorithms were successful in inverting SP data containing multi-body anomalies.…”

Section: Self-potential Datamentioning

confidence: 99%

“…MC is computationally expensive since the posterior sampling necessitates calculation of the likelihood function [5]. However, some meta-heuristic algorithms (MHAs), including the Genetic Algorithm (GA) [6], the Particle Swarm Optimization (PSO) [7], the Differential Evolution (DE) [8], the Black Holes Algorithm (BHA) [9], the Flower Pollination Algorithm (FPA) [10], etc. provide posterior sampling of model parameters with the capacity to trade off between exploration and exploitation of the search space.…”

Section: Introductionmentioning

confidence: 99%

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Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Sungkono

Grandis

2021

J. Eng. Technol. Sci.

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Symbiotic Organisms Search (SOS) is a global optimization algorithm inspired by the natural synergy between the organisms in an ecosystem. The interactive behavior among organisms in nature simulated in SOS consists of mutualism, commensalism, and parasitism strategies to find the global optimum solution in the search space. The SOS algorithm does not require a tuning parameter, which is usually used to balance explorative and exploitative search by providing posterior sampling of the model parameters. This paper proposes an improvement of the Modified SOS (MSOS) algorithm, called IMSOS, to enhance exploitation along with exploration strategies via a modified parasitism vector. This improves the search efficiency in finding the global minimum of two multimodal testing functions. Furthermore, the algorithm is proposed for solving inversion problems in geophysics. The performance of IMSOS was tested on the inversion of synthetic and field data sets from self-potential (SP) and vertical electrical sounding (VES) measurements. The IMSOS results were comparable to those of other global optimization algorithms, including the Particle Swarm Optimization, the Differential Evolution and the Black Holes Algorithms. IMSOS accurately determined the model parameters and their uncertainties. It can be adapted and can potentially be used to solve the inversion of other geophysical data as well.

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“…Of special interest is the recently published research of Sungkono (2020), where the author proposed to employ posterior distribution model of the SP anomaly inversion. The advantages of this approach are that the SP data may contain single and multiples of SP sources and this method does not require prior assumptions over the shape of the anomaly source.…”

Section: Some Common Aspects Of Magnetic and Sp Fieldsmentioning

confidence: 99%

Quantitative analysis of self-potential anomalies: Review of case studies from various SP applications

Eppelbaum

2021

Preprint

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Self-potential (SP) method is one of the most non-expensive and unsophisticated geophysical methods. However, its application limits absence of reliable interpreting methodology, first for the complex geological-environmental conditions. The typical disturbances appearing in the SP method are discussed. To exclude these noise components before the quantitative analysis, some ways for their removing (elimination) are presented. Some brief review of the available interpretation methods is presented. For the magnetic method of geophysical prospecting, special quantitative procedures applicable under complex physical-geological environments (oblique polarization, uneven terrain relief and unknown level of the normal field), have been recently developed. Earlier detected common peculiarities between the magnetic and SP fields have been extended. These common aspects make it possible to apply the advanced procedures developed in magnetic prospecting to SP method. Besides the reliable determination of the depth of anomalous targets, these methodologies enable to calculate the corrections for non-horizontal SP observations and direction of polarization vector. For classification of SP-anomalies is proposed to use a new parameter – 'self-potential moment'. These quantitative procedures (improved modifications of characteristic point, tangent techniques and areal method) have been successfully tested on SP models and employed in real situations in mining, archaeological, environmental and technogenic geophysics. The obtained results indicate practical importance of the developed methodologies.

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Robust interpretation of single and multiple self-potential anomalies via flower pollination algorithm

Cited by 19 publications

References 56 publications

Self-Potential Method to Assess Embankment Stability: A Study related to the Sidoarjo Mud Flow

Self-Potential Method to Assess Embankment Stability: A Study related to the Sidoarjo Mud Flow

Improved Modified Symbiosis Organisms Search (IMSOS): A New and Adaptive Approach for Determining Model Parameters from Geoelectrical Data

Quantitative analysis of self-potential anomalies: Review of case studies from various SP applications

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