An efficient global optimization method for self-potential data inversion using micro-differential evolution
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Ó Indian Academy of Sciences (0123456789().,-volV)(0123456 789().,-volV)
An eDcient global optimization method for self-potential data inversion using micro-differential evolution SUNGKONO Departement of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia. e-mail: hening˙[email protected] MS received 11 January 2020; revised 28 April 2020; accepted 29 April 2020
Self-potential (SP) method has many applications, where the interpretation of SP data can be used for qualitative and quantitative interpretation. However, inversion of SP data in this paper is of quantitative interpretation and consists of highly non-linear, multimodal data and deploys global optimum method (GOM). Micro-differential evolution (MDE) is a GOM with small or micro-population size (5–8 populations) for each iteration. Consequently, this approach involves small numbers of forward computation in the inversion process. Two MDE variants, including adaptive MDE (lJADE) and vectorized random mutation factor (MVDE) were tested Brst for different level of noises containing synthetic SP data with single anomaly and applied to synthetic SP data of multiple anomalies. The MDE variants are reliable and eAective for inverting noisy SP data. Furthermore, in order to check the rationality of MDE variants, the algorithm is applied to seven Beld data from different applications, including groundwater exploration, shear zone tracing, water accumulation in landslides and embankment stability assessment. The model parameters revealed by MDE variants are accurate and show good agreement with the previous results estimated using other approaches. In addition, MDE variants also require fewer forward modelling calculations than other optimization approaches. Keywords. Multiple anomalies; model parameters; uncertainty analysis; fast inversion; micropopulation.
1. Introduction Self-potential (SP) method is a passive geophysical method which measures natural potentials. The potential is usually produced by electrokinetic, electrochemical, and thermoelectric sources. Hence, the SP method has wide applications including cavity identiBcation (Jardani et al. 2006), landslide study (Lapenna et al. 2003; Sungkono and Warnana 2018), embankment leakage detection (Moore et al. 2011; Sungkono and Warnana 2018), mineral and geothermal exploration (Biswas and
Sharma 2014a; Byrdina et al. 2012), landBll leachate identiBcation (Arora et al. 2007), and groundwater investigations (Monteiro Santos et al. 2002). SP method is often successful in producing both qualitative and quantitative interpretations in single and multiple anomalies. Interpretation of SP data can be classiBed into two sections, signal analysis and an inversion process. In the Brst section, SP data is considered as a signal, which is then analyzed using signal analysis method, for example, continuous wavelet transform (Saracco et al. 2004), Euler deconvolution
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(Agarwal and Srivastava 2009) and Hilbert transform (Sundararajan and Srinivas 1996), etc. Mea
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