Global Inversion of Grounded Electric Source Time-domain Electromagnetic Data Using Particle Swarm Optimization

Global optimization inversion of grounded wire time-domain electromagnetic (TDEM) data was implemented through application of the particle swarm optimization (PSO) algorithm. This probabilistic approach is an alternative to the widely used deterministic local-optimization approach. In the PSO algorithm, each particle that constitutes the swarm epitomizes a probable geophysical model comprised by subsurface resistivity values at several layers and layer thicknesses. The forward formulation of the TDEM problem for calculating the vertical component of the induced magnetic field is first expressed in the Laplace domain. Transformation of the magnetic field from the Laplace domain into the time domain is performed by applying the Gaver-Stehfest numerical method. The implementation of PSO inversion to the TDEM problem is straightforward. It only requires adjustment of a few inversion parameters such as inertia, acceleration coefficients and numbers of iteration and particles. The PSO inversion scheme was tested on synthetic noise-free data and noisy synthetic data as well as to field data recorded in a volcanic-geothermal area. The results suggest that the PSO inversion scheme can effectively solve the TDEM 1D stratified earth problem.

Identification of Sediment Formation Based on Magnetic Content and Element Composition of Mud Volcano in Sangiran Sediment using VSM and X-Ray Fluorescence

Based on trace geological history and several studies, the Sangiran mud volcano provides insight into the geology and hydrology of the region, aquifer system in the basin, groundwater flow patterns and characteristics, rock lithology, hydrogeology condition, and saltwater trap mapping. Related to these conditions, studies were conducted on the magnetic content and composition of the major oxide compounds in the Sangiran sediments. Sample analysis was based on geochemical methods. The methods consist of frequency dependent magnetic susceptibility and vibrating sample magnetometer (VSM) analysis. Geochemical analyses using x-ray fluorescence (XRF) analysis have been conducted and various elemental grades have been determined. VSM results confirm that the magnetic content of Sangiran sediments is partly dominated by Fe (17.66 percent) contained in hematite (Fe2O3). At the same time, the samples of Sangiran sediment were enriched by Si, Fe, Al, Ca, Cl, Ti, and K according to XRF measurements. The samples exhibited the highest Si and Fe concentrations in samples T1 (Si is 29.48 percent and Fe is 13.66 percent) and T7 (Si is 24.95 percent and Fe is 12.01 percent). Meanwhile, in the T4 sample, the highest concentrations were Si and Ca, 23.45 percent and 13.45 percent, respectively. Retrieved from the magnetic susceptibility measurement, this paper confirm that Fe content is one of the components of volcanic ash in the Sangiran sediment.DOI: 10.17977/um024v8i12023p00

Implementation of the Gauss-Kronrod Quadrature Method (G7, K15) on 2D Gravity Anomaly Modeling in Basins with a Polynomial Variation of Density Distribution with Depth

Forward modeling of 2D gravity anomalies, considering density contrasts that vary polynomially with depth, was performed to examine basin structures. This process involved two main stages: deriving analytical formulas and executing numerical integration. The Gauss-Kronrod Quadrature Method, utilizing 7 Gauss points and 15 Kronrod points, was employed to precisely compute these integrals. Initial modeling applied to theoretical basement scenarios with fixed density contrasts showed gravity anomalies that accurately reflected the curvature of the basement. To validate the approach, it was then applied to real-world cases including the Sebastian Vizcaino Basin, San Jacinto Graben, and Sayula Basin. By incorporating suitable density contrasts, modeling lengths, and basement curvature shapes, the results revealed that both fixed-density and depth-variable density models produced gravity anomalies with patterns consistent with the actual basement curvature. These findings validate the modeling technique’s effectiveness in representing real geological features accurately. The study confirms that the Gauss-Kronrod Quadrature Method (G7, K15) is robust for analyzing 2D gravity anomalies, providing a reliable tool for understanding the influence of varying density contrasts on gravity responses

Nonlinear Inversion Using Very Fast Simulated Annealing for Horizontal Electric Dipole Time-Domain Electromagnetic Data

A nonlinear stochastic inversion scheme, called very fast simulated annealing (VFSA), was applied to the time-domain electromagnetic data generated from a horizontal electric dipole. The forward formulation of the vertical magnetic field was expressed in the Laplace domain by applying the Hankel integral transform. Time-domain transformation was performed by applying the inverse Laplace transform using the Gaver–Stehfest algorithm. In this study, for noise-free synthetic data, the VFSA scheme yielded the smallest misfit and an inverted resistivity model that resembled the test model. The addition of 5% random noise to the synthetic data produced the same level of misfit and a model that still mimicked the test model. However, the addition of 10% noise to the synthetic data resulted in a misfit value that was three times that of the first two values and a resistivity model with a large discrepancy with the test model, particularly at large depths. These results indicate the efficacy of the VFSA inversion scheme for inferring the subsurface resistivity structure from the electromagnetic data. This inversion scheme was applied to field data measured in a volcanic environment. The general pattern of the resistivity structure inferred by the VFSA inversion is consistent with the structure obtained previously by using a deterministic inversion scheme.</jats:p