Self-potential Data Interpretation for Two Co-axial Structures Utilizing the RMS Parameter

2020 ◽  
Vol 25 (1) ◽  
pp. 15-23
Author(s):  
Khalid S. Essa ◽  
Zein E. Diab ◽  
Mahmoud Elhussein
Keyword(s):  
A Priori ◽  
Source Parameters ◽  
Data Interpretation ◽  
The Body ◽  
Inversion Method ◽  
Model Parameters ◽  
Polarization Angle ◽  
Potential Field Data ◽  
Rms Error ◽  
Self Potential

We have developed an algorithm to obtain the model parameters for two co-axial structures from self-potential data. The method uses the first numerical horizontal derivatives calculated from the observed self-potential anomaly employing filters of sequential window lengths (s-values) so as to gauge the model constraints for the shallow and deep structures. In addition, this algorithm uses a standard inversion method for solving a non-linear equation based on the lowest root-mean-square (RMS) error of the estimated model parameters. The body constraints are the depth, polarization angle and electric dipole moment of each structure. Our approach models the self-potential dataset as an aggregation of spheres, horizontal cylinders, and vertical cylinders. These simple bodies are used to approximate, without a priori expectations, the furthermost plausible position and/or area of intersection. In other words, the bodies are used to estimate the true values of the source parameters for the two-co-axial bodies at different s-values. Minimizing the RMS error has the advantage of optimizing all model factors. The proposed technique is tested using a numerical model with and without noise and on self-potential field data acquired at a site in Germany. In all cases, the assessed body parameters are reasonable approximations of the known values.

scholarly journals A particle swarm optimization method for interpreting self-potential anomalies

2019 ◽  
Vol 16 (2) ◽  
pp. 463-477 ◽  
Author(s):  
Khalid S Essa
Keyword(s):  
Particle Swarm Optimization ◽  
Moving Average ◽  
Source Parameters ◽  
Particle Swarm ◽  
Optimization Method ◽  
Polarization Angle ◽  
Swarm Optimization ◽  
The Usa ◽  
Residual Response ◽  
Self Potential

Abstract This paper describes the use of the particle swarm optimization (PSO) method for interpreting observed self-potential anomalies measured along a profile. First, the technique applies the second moving average to the observed self-potential data in order to eradicate the possible influence of the regional anomaly (up to the third-order polynomial effect) via the filter of consecutive window lengths (s-values) and to calculate the residual anomaly. Following that, the PSO method is applied to the residual response to infer the source parameters: amplitude coefficient (K), depth (z), polarization angle (θ) and the shape factor (q) of the underlying buried target. The technique has been applied to three different theoretical and two field examples from the USA and Turkey. Comparisons have shown that the source parameters retrieved from the technique described here are in good agreement with the available geologic and geophysical information.

scholarly journals A NEW METHOD OF INTERPRETATION OF SELF‐POTENTIAL FIELD DATA

Geophysics ◽  
1948 ◽  
Vol 13 (4) ◽  
pp. 600-608 ◽  
Author(s):  
L. de Witte
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Field Work ◽  
Data Interpretation ◽  
New Method ◽  
Positive Maximum ◽  
Potential Field Data ◽  
Negative Center ◽  
Location Depth ◽  
Self Potential

In this paper a new, efficient method is worked out for the interpretation of self‐potential field data. Interpretation of location, depth and dip of the ore body is made, using a pattern of equipotential lines. The negative center and the positive maximum of the potential are found and also the so‐called “mid‐value” point. The dip α, can be determined accurately for values between 5° and 85°. The method cannot be used for vertical polarization. The depth and location can be found with relative accuracy for α>10°. The main advantage of this new method is the ease of interpretation and a greater accuracy for the high‐dip angles. It should be stressed that, for correct and accurate interpretation, the positive maximum is as important as the negative center. Therefore, it should be carefully sought during the field work, and mapped to its full extent.

Multiscale resistivity inversion based on convolutional wavelet transform

2020 ◽  
Vol 223 (1) ◽  
pp. 132-143 ◽  
Author(s):  
Yonghao Pang ◽  
Lichao Nie ◽  
Bin Liu ◽  
Zhengyu Liu ◽  
Ning Wang
Keyword(s):  
Wavelet Transform ◽  
A Priori ◽  
Geophysical Methods ◽  
Spatial Domain ◽  
Inversion Method ◽  
Model Parameters ◽  
Imaging Method ◽  
Feature Parameters ◽  
Resistivity Model ◽  
Resistivity Inversion

SUMMARY The resistivity imaging method, an effective geophysical technique, has been widely used in environmental, engineering and hydrological fields. The inversion method based on smooth constraint is one of the most commonly used methods. However, this method causes the resistivity to change smoothly and makes it difficult to describe geological boundaries accurately. An accurate description of the target's boundaries often requires a priori information gained with other methods (such as other geophysical methods or geological drilling). To address this issue, a multiscale inversion method is proposed for extracting boundary features and inverting feature parameters from different scales. In this method, a convolution kernel is used to extract the boundary information from the resistivity model. The model parameters are transformed from the spatial domain to the feature domain via a convolutional wavelet transform. The feature parameters of different scales can then be obtained by solving the inversion equation in the feature domain. After that, the resistivity model of the spatial domain is reconverted from the feature domain by deconvolution transform of the inversion result. Numerical simulations and experiments show that the new multiscale resistivity inversion method has the ability to locate and depict boundaries of geological targets with high accuracy.

Joint inversion of Rayleigh-wave dispersion and P-wave refraction data for laterally varying layered models

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. EN49-EN59 ◽  
Author(s):  
Daniele Boiero ◽  
Laura Valentina Socco
Keyword(s):  
Rayleigh Wave ◽  
Joint Inversion ◽  
A Priori ◽  
P Wave ◽  
Inversion Method ◽  
Model Parameters ◽  
Data Set ◽  
Wave Refraction ◽  
Velocity Models ◽  
Refraction Data

We implemented a joint inversion method to build P- and S-wave velocity models from Rayleigh-wave and P-wave refraction data, specifically designed to deal with laterally varying layered environments. A priori information available over the site and any physical law to link model parameters can be also incorporated. We tested and applied the algorithm behind the method. The results from a field data set revealed advantages with respect to individual surface-wave analysis (SWA) and body wave tomography (BWT). The algorithm imposed internal consistency for all the model parameters relaxing the required a priori assumptions (i.e., Poisson’s ratio level of confidence in SWA) and the inherent limitations of the two methods (i.e., velocity decreases for BWT).

A constrained global inversion method using an overparameterized scheme: Application to poststack seismic data

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 613-626 ◽  
Author(s):  
Xin‐Quan Ma
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
A Priori ◽  
Constant Thickness ◽  
Inversion Method ◽  
Model Parameters ◽  
A Priori Information ◽  
Initial Model ◽  
Seismic Waveform ◽  
Priori Information

A global optimization algorithm using simulated annealing has advantages over local optimization approaches in that it can escape from being trapped in local minima and it does not require a good initial model and function derivatives to find a global minimum. It is therefore more attractive and suitable for seismic waveform inversion. I adopt an improved version of a simulated annealing algorithm to invert simultaneously for acoustic impedance and layer interfaces from poststack seismic data. The earth’s subsurface is overparameterized by a series of microlayers with constant thickness in two‐way traveltime. The algorithm is constrained using the low‐frequency impedance trend and has been made computationally more efficient using this a priori information as an initial model. A search bound of each parameter, derived directly from the a priori information, reduces the nonuniqueness problem. Application of this technique to synthetic and field data examples helps one recover the true model parameters and reveals good continuity of estimated impedance across a seismic section. This approach has the capability of revealing the high‐resolution detail needed for reservoir characterization when a reliable migrated image is available with good well ties.

3D inversion of potential field data using a marginalizing probabilistic method

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. G93-G106 ◽  
Author(s):  
Meixia Geng ◽  
Xiangyun Hu ◽  
Henglei Zhang ◽  
Shuang Liu
Keyword(s):  
Covariance Matrix ◽  
Field Data ◽  
Weighting Function ◽  
Probabilistic Method ◽  
A Priori ◽  
Inversion Method ◽  
Spatial Gradient ◽  
Underlying Assumption ◽  
Potential Field Data ◽  
Probabilistic Inversion

Probabilistic inversion methods have proven effective in solving many geophysical inverse problems. Structural orientation and spatial extent information can be efficiently incorporated the probabilistic inversion by the use of parameter covariances to produce a geologically realistic model. However, the use of a single model covariance matrix, with the underlying assumption of the presence of only one type of feature (e.g., similar size, shape, and orientation) in the subsurface, limits the ability of probabilistic inversions to recover geologically sound models. An approach based on marginalizing the probabilistic inversion is presented, which makes it possible to partition the inverse domain into various zones, each of which can have its own covariance matrix depending upon the features and/or depths of the sources. Moreover, a spatial gradient weighting function is introduced to enhance or attenuate the structural complexity in different zones. Thus, sources with different shapes, sizes, depths, and densities (or magnetic susceptibilities) can be simultaneously reconstructed. The sensitivity of the solutions to uncertainties in the a priori information, including the orientation, depth, and horizontal position as well as subdivision of the inversion domain, is analyzed. We found through synthetic examples and field data that the developed inversion method was a valid tool for exploration geophysics in presence of a priori geologic information.

scholarly journals A priori estimations of geometric parameters of an anomalous object in modeling the soil structure using RES3DINV software

2020 ◽  
Vol 30 (4) ◽  
pp. 696-710
Author(s):  
O.M. Nemtsova ◽  
T.M. Bannikova ◽  
V.M. Nemtsov
Keyword(s):  
Data Analysis ◽  
A Priori ◽  
Data Interpretation ◽  
Geometric Parameters ◽  
Inversion Method ◽  
A Priori Information ◽  
Input Parameters ◽  
Uniqueness Of The Solution ◽  
Ill Posed ◽  
Priori Information

We discuss the problem of proper use of software packages that implement methods for solving ill-posed problems. Most of the problems of processing experimental data belong to ill-posed problems. When using methods for solving ill-posed problems, there is a problem of non-uniqueness of the solution, which is solved by introducing a priori information. Obtaining a priori information is possible in different ways, but quantitative estimates involve the use of additional methods for data analysis. Obviously, additional methods should not be more complicated and labor intensive than the main data processing method. Using the RES3DINV electrical prospecting data analysis software as an example, the role of a priori information for obtaining reliable results is demonstrated. The RES3DINV software is used to build a soil model from the measured values of resistivity using electrical survey’s methods. When using the inversion method implemented in the software package, it is necessary to set the input parameters describing the geometric dimensions of the anomalous resistance object, which are usually unknown a priori. By model objects we demonstrate how the incorrect setting of input parameters affects the result of data interpretation. We show that the vector analysis method can be used as a way to obtain a priori information. This method allows us to obtain estimates of the geometric parameters of an anomalous object, does not involve high time and resource expenses, and can be used directly at the site of field experimental measurements.

Formulation, Design and Development of Niosome Based Topical Gel for Skin Cancer

2017 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Skin Cancer ◽  
Drug Release ◽  
Hemorrhagic Cystitis ◽  
The Body ◽  
Cancer Drug ◽  
Inversion Method ◽  
Body Parts ◽  
Basal Cells ◽  
Formulation Design ◽  
Topical Gel

Melanoma is the most dangerous type of skin cancer in which mostly damaged unpaired DNA starts mutating abnormally and staged an unprecedented proliferation of epithelial skin to form a malignant tumor. In epidemics of skin, pigment-forming melanocytes of basal cells start depleting and form uneven black or brown moles. Melanoma can further spread all over the body parts and could become hard to detect. In USA Melanoma kills an estimated 10,130 people annually. This challenge can be succumbed by using the certain anti-cancer drug. In this study design, cyclophosphamide were used as a model drug. But it has own limitation like mild to moderate use may cause severe cytopenia, hemorrhagic cystitis, neutropenia, alopecia and GI disturbance. This is a promising challenge, which is caused due to the increasing in plasma drug concentration above therapeutic level and due to no rate limiting steps involved in formulation design. In this study, we tried to modify drug release up to threefold and extended the release of drug by preparing and designing niosome based topical gel. In the presence of Dichloromethane, Span60 and cholesterol, the initial niosomes were prepared using vacuum evaporator. The optimum percentage drug entrapment efficacy, zeta potential, particle size was found to be 72.16%, 6.19mV, 1.67µm.Prepared niosomes were further characterized using TEM analyzer. The optimum batch of niosomes was selected and incorporated into topical gel preparation. Cold inversion method and Poloxamer -188 and HPMC as core polymers, were used to prepare cyclophosphamide niosome based topical gel. The formula was designed using Design expert 7.0.0 software and Box-Behnken Design model was selected. Almost all the evaluation parameters were studied and reported. The MTT shows good % cell growth inhibition by prepared niosome based gel against of A375 cell line. The drug release was extended up to 20th hours. Further as per ICH Q1A (R2), guideline 6 month stability studies were performed. The results were satisfactory and indicating a good formulation approach design was achieved for Melanoma treatment.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

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