Inversion of Amplitude from the 2-D Analytic Signal of Self-Potential Anomalies

In the present study, analytic signal amplitude (ASA) or total gradient (TG) inversion of self-potential anomalies has been carried out using very fast simulated annealing (VFSA) global optimization technique. The results of VFSA optimization demonstrate the application and efficacy of the proposed method for idealized synthetic hypothetical models and real single and multiple geological structures. The model parameters deciphered here are the amplitude coefficient (k), horizontal location (x0), depth of the body (z), and shape (q). Inversion of the model parameter suggests that constraining the horizontal location and the shape factor offers the most reliable results. Investigation of convergence rate, histogram, and cross-plot examination suggest that the interpretation method developed for the self-potential anomalies is stable and the model parameters are within the estimated ambiguity. Inversion of synthetic noise-free and noise-corrupted data for single structures and multiple structures in addition to real field information exhibits the viability of the method. The model parameters estimated by the present technique were in good agreement with the real parameters. The method has been used to invert two field examples (Sulleymonkoy anomaly, Ergani, Turkey, Senneterre area of Quebec, Canada) with application of subsurface mineralized bodies. This technique can be very much helpful for mineral or ore bodies investigation of idealized geobodies buried within the shallow and deeper subsurface

A Comparison Study Using Particle Swarm Optimization Inversion Algorithm For Gravity Anomaly Interpretation Due To A 2D Vertical Fault Structure

A new approach to the inversion of gravity data utilizing the Particle Swarm Optimization (PSO) algorithm is used to model 2D vertical faults. The PSO algorithm is stochastic in nature; its development was motivated by the communal in-flight performance of birds looking for food. The birds are represented by particles (or models). Individual particles have a location and a velocity vector. The location vectors represent the parameter value. PSO is adjusted with random particles (models) and searches for targets by updating generations. Herein, the PSO algorithm is applied to three synthetic data sets (residual only with and without noise, residual plus regional, residual plus anomaly generated by a buried cylinder structure) and two field gravity data sets acquired across known faults in Egypt. Assessment of the synthetic data demonstrates that the PSO algorithm generates superior results if a first horizontal gradient (FHG) filter is applied first. The robustness of the PSO inversion algorithm is demonstrated for both synthetic and field gravity data

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

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

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

The stability of an embankment is generally influenced by a number of factors, such as deformation, fractures, overtopping, seepage, etc. Fractures and seepage are commonly found in the LUSI (Sidoarjo mud flow) embankment. In this study, analysis of self-potential (SP) data was applied to identify fractures and seepage in the LUSI embankment. Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) and Continuous Wavelet Transform (CWT) were applied to determine the location of seepage and fractures in the subsurface based on SP data. The results were correlated with the 2D direct current resistivity (DCR) method, which showed that both methods worked well and were compatible in detecting and localizing fracture and seepage in the LUSI embankment

Magnetic Inversion Approach For Modeling Data Acquired Across Faults: Various Environmental Cases Studies

An effective extension to the particle swarm optimizer scheme has been developed to visualize and modelize robustly magnetic data acquired across vertical or dipping faults. This method can be applied to magnetic data sets that support various investigations, including mining, fault hazards assessment, and hydrocarbon exploration. The inversion algorithm is established depending on the second horizontal derivative technique and the particle swarm optimizer algorithm and was utilized for multi-source models. Herein, the inversion method is applied to three synthetic models (a dipping fault model contaminated without and with different Gaussian noises levels, a dipping fault model affected by regional anomaly, and a multi-source model) and three real datasets from India, Australia, and Egypt, respectively. The output models confirm the inversion approach\u27s accuracy, applicability, and efficacy. Also, the results obtained from the suggested approach have been correlated with those from other methods published in the literature

A hybrid optimization scheme for self-potential measurements due to multiple sheet-like bodies in arbitrary 2D resistivity distributions

Self-potential (SP) is a passive geophysical method that can be applied in a straightforward manner with minimum requirements in the field. Nonetheless, interpretation of SP data is particularly challenging due to the inherited nonuniqueness present in all potential methods. Incorporating information regarding the target of interest can facilitate interpretation and increase the reliability of the final output. In the current paper, a novel method for detecting multiple sheet-like targets is presented. A numerical framework is initially described that simulates sheet-like bodies in an arbitrary 2D resistivity distribution. A scattered field formulation based on finite-differences is employed that allows the edges of the sheet to be independent of the grid geometry. A novel analytical solution for two-layered models is derived and subsequently used to validate the accuracy of the proposed numerical scheme. Lastly, a hybrid optimization is proposed that couples linear least-squares with particle-swarm optimization (PSO) in order to effectively locate the edges of multiple sheet-like bodies. Through numerical and real data, it is proven that the hybrid optimization overcomes local minimal that occur in complex resistivity distributions and converges substantially faster compared to traditional PSO

Introductory Chapter: Mineral Exploration from the Point of View of Geophysicists

International audienc

Magnetic field analysis using the improved global particle swarm optimization algorithm to estimate the depth and approximate shape of the buried mass

In this paper, the optimization algorithm based on the population as improved global particle swarm optimization is described and used for inverse modelling of two-dimensional magnetic field data. This algorithm is able to estimate the parameters of depth, shape factor, amplitude coefficient, magnetic inclination angle and origin point coordinates. To evaluate the efficiency of this method, the magnetic field of an artificial model was analysed, with and without added random noise. The results suggest that the proposed algorithm is capable of model parameter estimation with high accuracy. Accordingly, the improved global particle swarm optimization algorithm was used to analyse the magnetic field of the study area in the Ileh region in Iran located in Taybad city. The study area is very rich in terms of iron resources. The estimate for the study area is that the depth of the buried mass centre is about 114.9 m and its approximate shape is similar to a horizontal cylinder based on the calculated shape factor value which is 1.76. The calculated depth is an acceptable match with the average depth of drillings

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

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