Inversion Of Self-potential Data Research Articles

Compact source inversion of self-potential data generated by geomicrobes

The self-potential (SP) method is a classical geophysical exploration method which has a wide application prospect in underground pollutant monitoring and other fields. However, due to the complexity of the formation mechanism and the lack of prior information, there are still quite a few difficulties in the precise quantitative inversion of the SP sources, and qualitative interpretation is frequently adopted in practical applications. In this work, we carry out inversion research on the SP data of geomicrobes to accurately invert and locate the spatial distribution of the SP sources which is closely relevant to microbial activities. The resistivity-based depth weighting matrix is added to the inversion algorithm to promote the migration of the SP sources from the earth surface to their original depth. And to conform to the actual distribution of the SP sources, the minimum support stabilizing function is introduced to impose additional compact constraint. Two synthetic models are firstly designed to verify the effectiveness and accuracy of the proposed algorithm. On this basis, the sandbox experiment that continuously observes and records the SP signals generated by the typical organism: Shiwanella Oneida MR-1 breaking down the organic matter is carried out. Then the observed data is inverted to locate the SP sources. The inversion results demonstrate that with the addition of Shiwanella Oneida MR-1 into the humus, the negative SP source immediately appear on the top of the humus, which increase sharply, then remain stable and then slowly decay over time. The negative SP sources are concentrated on the top of the humus, which is consistent with the theoretical analysis of the biogeobattery model.

2D Source Inversion of Self-potential Data Based on Particle Swarm Optimization and Gradient Method

The difficulty of accurately inverting the self-potential (SP) source has always been a major factor hindering the wide application of the SP method. Considering that particle swarm optimization has poor accuracy when in the face of high-dimensional SP inversion, while the gradient method depends on the selection of the initial solution, we try to combine these two algorithms to prompt the inversion result to jump out of local optimum.

Detecting gas upwelling hazards in coastal areas through integration of active and passive electrical and seismic methods (Fiumicino, Central Italy)

The accurate location of gas upwelling flows is still an open problem for non-invasive imaging techniques in populated areas. Gas blowouts of deep origin may represent a serious threat to human health in urban areas and should be correctly imaged with high-resolution for assessing the related hazards. In this work, we propose an integration of active (electrical resistivity tomography and high-resolution sub-bottom profiling complemented with the multibeam bathymetry) and passive (self-potential and ambient noise recordings) geophysical methods to image gas upwelling flows in the coastal area of Fiumicino (Central Italy), where the gas presence is well-documented by previous works. We demonstrate that merging seismic sub-bottom profiling and electrical resistivity tomography has enormous diagnostic potential for gas detection, since they combine the high resolution needed to correctly image the subsurface and the interfaces between different media with the high diagnostic capability of electrical methods to detect anomalies associated with the gas emissions. Passive seismic methods complement the analysis enabling an estimation of the shear-wave velocity through array measurements. Finally, the reconstruction of the natural electrical sources, inferred from the inversion of self-potential data, confirms the location of the near-surface gas upwelling flows assessed through the resistivity model. This work demonstrates that the integration of high-resolution active and passive seismic and electrical methods can be an effective choice for the accurate location of risk-prone areas by imaging the near surface gas pathways where borehole drilling is strongly limited if not forbidden.

3D Multicomponent Self-Potential Inversion: Theory and Application to the Exploration of Seafloor Massive Sulfide Deposits on Mid-Ocean Ridges

The marine self-potential (SP) method is currently playing an increasing role in the exploration and resource evaluation of seafloor massive sulfide (SMS) deposits. SP surveys are conducted using autonomous underwater vehicles (AUV), which yield multicomponent electric field datasets. By comparing with the single-component electrical field data used to date, the inversion of these multicomponent data is expected to provide a more accurate description of the 3D structure of SMS deposits beneath the seafloor (like gradiometry in gravity surveys). We introduce an inversion algorithm specifically adapted to multicomponent SP data. A synthetic model demonstrates that the inversion of multicomponent datasets allows us to better recover the amplitude of the current density and the morphology of the ore bodies compared to using a single component of the electrical field. Next, we apply our approach to a multicomponent SP dataset collected during the DY58 oceanic cruise at the Yuhuang hydrothermal field on the Southwest Indian Ridge. Subsequently, we reconstruct the three-dimensional (3D) geometry of the SMS deposits beneath the seafloor. The AUV-based SP system with the collection of multicomponent SP data inversion appears to be a powerful tool in the exploration and evaluation of seafloor sulfide resource and, in the future, could be used in concert with induced polarization data.

Penentuan Kedalaman dan Sudut Kemiringan Sesar Grindulu di Pacitan Menggunakan Metode Crow Search Algorithm (CSA) pada Data Self-Potential (SP)

Self-Potential (SP) is a geophysical survey method that is relatively easy and inexpensive. Interpretation of SP data can be used for various purposes such as the detection of landslide-prone areas, exploration of various types of minerals, and identification of the parameters of a fault or crack. In this study, SP data acquisition was carried out in Tambakrejo Village, Pacitan District with a total of 102 measurement data which aims to determine the depth and dip of the Grindulu Fault. SP data acquired in the field needs to be corrected for reference, namely corrections caused by a displacement of the starting point of measurement. This data is then filtered to increase the signal-to-noise ratio (SNR) and sharpen the resulting anomalies. This filtering process is carried out using the ICEEMD (Improved Complete Ensemble Empirical Mode Decomposition) method which is a development of the EMD method. Furthermore, the SP data inversion process to obtain model parameters is carried out by utilizing the CSA (Crow Search Algorithm) method. Based on the anomaly model generated from the SP data inversion process, it can be concluded that the Grindulu Fault was identified at a distance of 803,8 meters from the starting point of measurement with depths ranging from 11,06 to 102,74 meters. Furthermore, based on distance, depth, and anomaly shape data, the dip value can be calculated. The calculation results show that the dip of the Grindulu Fault in the study area is 75.58o. Identification of the Grindulu Fault in the form of depth and dip is very important in efforts to model the fault comprehensively.

Inversion of self-potential data by a hybrid DE/PSO algorithm

Inversion of self-potential data by a hybrid DE/PSO algorithm

Inversion of self-potential data using generalized regression neural network

Inversion of self-potential data using generalized regression neural network

Self-potential data inversion utilizing the Bat optimizing algorithm (BOA) with various application cases

The Bat optimizing algorithm (BOA) is one of the metaheuristic algorithms and applied here to interpret self-potential (SP) data. The BOA is depending upon a bat echolocation behavior for global optimization, which the global optimum solution reached at the suggested minimum value of the objective function. The best interpretive source parameters for the subsurface structures occurred at the minimal the objective function value (global best solution). The BOA is applied to 2D SP anomaly data to estimate the characteristic source parameters (i.e., the depth to center, amplitude coefficient, origin location, geometric shape factor, and polarization and inclination angle of the causative buried structure). The BOA can be applied to single and multiple source structures in the restricted class of simple geometric shapes, which these bodies help in the validation of the subsurface ore and mineral targets. The stability and efficiency of the proposed BOA have been examined by several synthetic examples. In addition, three different real field examples from Germany and Indonesia have been successfully applied to ore and mineral investigation and geological structure studies. In general, the achieved results are in good agreement with the available borehole data and results mentioned in the literature.

Application of Optimal Control to Inversion of Self-Potential Data: Theory and Synthetic Examples

Last decades, there has been an increased interest in the use of the self-potential (SP) method in hydrogeophysics. In response to this strong interest, we develop a novel approach to the inversion of SP data. Mathematically, the SP inverse problem is the source identification problem for the Poisson equation. Our approach substantially differs from the standard regularization approach, which explicitly includes the forward-problem operator into the cost functional. We formulated the inverse problem is as an optimal control problem and then translate it into a variational system. The system is approximated in suitable finite-element spaces giving rise to an algebraic problem with the saddle-point structure. In contrast to the standard approach, which leads to a dense linear system, our method results in a system with a sparse block matrix. It can be efficiently solved by either direct sparse solvers or preconditioned iterative solvers. In this paper, we present the formulation of the problem and its finite-element approximation. We discuss the iterative solution and preconditioning strategies. Our software implementation is based on an industrial finite-element package. We also present a numerical experiment with node-based linear finite elements on tetrahedral grids. Our results suggest that the proposed approach may serve as a rapid and reliable tool for large-scale SP inverse problems. Moreover, the same technique can easily be extended to a wide range of geophysical linear inverse problems, such as inversions of magnetic and gravity data.

Model parameter estimation and its uncertainty for 2-D inclined sheet structure in self-potential data using crow search algorithm

Fracture or fault can control several geological or geophysical events and exploration, where the fault associated with mineralization or fluid flow can be used as a source for Self-Potential (SP) anomaly. The 2-D inclined sheet can be used for modeling for fault interpretation using SP data. In this paper, the improved crow search algorithm (ICSA) using Levy flight is proposed for SP data inversion in determining SP model parameters. In order to evaluate ICSA, the ICSA is compared with standard crow search algorithm (CSA) for determining synthetic SP data that contains multiple anomalies with inclined sheet type structures. It was found that CSA is more explorative than ICSA, and both algorithms can estimate the posterior distribution model (PDM) of SP data. Using uncertainty analysis within the applied threshold in the objective function, both algorithms are reliable to determine PDM. Furthermore, ICSA is tested and implemented to both synthetic and field of SP anomalies for providing the posterior distribution model of SP parameters. The experimental results demonstrate that the ICSA is feasible and effective for determining model parameters and its uncertainty of mono- and multi-SP anomalies. Furthermore, estimating both model parameter and its uncertainty are sufficient for validation with previous researchers. Finally, the interpretation of multiple anomalies in SP anomaly crossing the Grindulu Fault in Pacitan, East Java, Indonesia, is analyzed.

An efficient global optimization method for self-potential data inversion using micro-differential evolution

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 ( $$ \mu $$ JADE) and vectorized random mutation factor (MVDE) were tested first 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 effective for inverting noisy SP data. Furthermore, in order to check the rationality of MDE variants, the algorithm is applied to seven field 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.

Groundwater Flow Monitoring by Fusion Probability Tomography of Self-Potential Data

Groundwater flow could produce an observable self-potential (SP) signal; it is, therefore, useful to use SP signal to investigate the condition of water flow, which is meaningful to the processes to which the water flow is critical, such as landslide warning, contaminant transport, and volcanic eruption. Most of the SP data inversion methods are based on prior in situ electrical resistivity information, which is difficult to be accurately obtained in the continuous monitoring of the vadose zone. We suggest a fusion scheme of integrating the electric charge occurrence probability (ECOP) tomography and the continuous complex wavelet transform (CCWT) method to process and interpret SP data when it lacks prior electrical structure information on the background media. A model of fracture flow with precipitation recharge is used to testify our proposed method. The results indicate that the combination scheme can locate the source of SP anomalies with improved accuracy. The proposed fusion probability tomography can locate the fracture flow with an explicit region of probability anomaly and suppress the irrelevant noises.

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 Data Inversion for Environmental and Hydrogeological Investigations

In this paper, we present a robust 2-D self-potential (SP) inversion algorithm that has proven to be suitable for both environmental and hydrogeological applications. The work proposed here continues from the recent advances in theoretical and experimental aspects of the self-potential method by detecting the depth and the shape of shallow electrical current density sources using the least square subspace preconditioned (LSQR) method to compute (an approximation to) the standard-form Tikhonov solution. The preconditioner is based on the subspace defined by the columns of the Kernel matrix and the method adopted for choosing the fixed value of the regularization parameter is the generalized cross-validation. The decrease of resolution, due to the fact that the self-potential field decays quickly with the distance, is controlled by a depth weighting matrix. A laboratory experimental setup has been assembled for locating two buried ferro-metallic bodies of any size at different depths using the inversion of self-potential signals associated with the redox process. The inverse problem is solved by accounting for the electrical conductivity distribution and the self-potential data in order to recover the source current density vector field. Both synthetic and real simulations, performed on a sand model with anomalies included, provide low-error inverted models whereas anomalies are well-detected for position and shape. The inversion algorithm has been also applied to a field data set collected in the San Vittorino Plain, located in Central Italy, in order to identify the location of sinkholes and investigate the effects of different resistivity structure assumptions on the streaming potential inversion results.

Quantitative interpretation of multiple self-potential anomaly sources by a global optimization approach

Multiple self-potential (SP) anomalies are analyzed by using a Genetic-Price Algorithm (GPA), which has been recently introduced for the inversion of SP data. The proposed approach is tested on multiple synthetic anomalies, which are modeled by horizontal cylinders. First, a forward modeling is used to analyze the resolution of such anomalies by varying all model parameters. Then, GPA is applied to invert synthetic multiple SP anomalies. The numerical analyses show that the proposed approach is able to fully characterize the anomaly sources by providing the correct values of the model parameters as well as the number of sources, even if Gaussian random noise is added to the synthetic data. Furthermore, to show the computational efficiency of GPA, the results of a comparative analysis with the Very Fast Simulated Annealing algorithm are given. The validity of the GPA approach is confirmed by its application to three examples of self-potential field data from mineral exploration and groundwater investigations, which are presented and discussed in relation to other inversion approaches. Finally, the quantitative interpretation of multiple anomalies along a SP profile crossing the Mt. Somma-Vesuvius volcano caldera (southern Italy) is provided.

Black hole algorithm for determining model parameter in self-potential data

Analysis of self-potential (SP) data is increasingly popular in geophysical method due to its relevance in many cases. However, the inversion of SP data is often highly nonlinear. Consequently, local search algorithms commonly based on gradient approaches have often failed to find the global optimum solution in nonlinear problems. Black hole algorithm (BHA) was proposed as a solution to such problems. As the name suggests, the algorithm was constructed based on the black hole phenomena. This paper investigates the application of BHA to solve inversions of field and synthetic self-potential (SP) data. The inversion results show that BHA accurately determines model parameters and model uncertainty. This indicates that BHA is highly potential as an innovative approach for SP data inversion.

Redox potential distribution of an organic-rich contaminated site obtained by the inversion of self-potential data

Mapping the redox potential of shallow aquifers impacted by hydrocarbon contaminant plumes is important for the characterization and remediation of such contaminated sites. The redox potential of groundwater is indicative of the biodegradation of hydrocarbons and is important in delineating the shapes of contaminant plumes. The self-potential method was used to reconstruct the redox potential of groundwater associated with an organic-rich contaminant plume in northern France. The self-potential technique is a passive technique consisting in recording the electrical potential distribution at the surface of the Earth. A self-potential map is essentially the sum of two contributions, one associated with groundwater flow referred to as the electrokinetic component, and one associated with redox potential anomalies referred to as the electroredox component (thermoelectric and diffusion potentials are generally negligible). A groundwater flow model was first used to remove the electrokinetic component from the observed self-potential data. Then, a residual self-potential map was obtained. The source current density generating the residual self-potential signals is assumed to be associated with the position of the water table, an interface characterized by a change in both the electrical conductivity and the redox potential. The source current density was obtained through an inverse problem by minimizing a cost function including a data misfit contribution and a regularizer. This inversion algorithm allows the determination of the vertical and horizontal components of the source current density taking into account the electrical conductivity distribution of the saturated and non-saturated zones obtained independently by electrical resistivity tomography. The redox potential distribution was finally determined from the inverted residual source current density. A redox map was successfully built and the estimated redox potential values correlated well with in-situ measurements.

Performance evaluation for intelligent optimization algorithms in self-potential data inversion

The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation.

Self-potential data inversion through a Genetic-Price algorithm

A global optimization method based on a Genetic-Price hybrid Algorithm (GPA) is proposed for identifying the source parameters of self-potential (SP) anomalies. The effectiveness of the proposed approach is tested on synthetic SP data generated by simple polarized structures, like sphere, vertical cylinder, horizontal cylinder and inclined sheet. An extensive numerical analysis on signals affected by different percentage of white Gaussian random noise shows that the GPA is able to provide fast and accurate estimations of the true parameters in all tested examples. In particular, the calculation of the root-mean squared error between the true and inverted SP parameter sets is found to be crucial for the identification of the source anomaly shape. Finally, applications of the GPA to self-potential field data are presented and discussed in light of the results provided by other sophisticated inversion methods.

Self-Potential data inversion through the integration of spectral analysis and tomographic approaches

Self-Potential data inversion through the integration of spectral analysis and tomographic approaches