Aeromagnetic surveys play an important role in the exploration of natural resources of economic interest, as well as in regional geologic mapping. Magnetic anomalies caused by the lateral variations of magnetization in the earth’s crust often are characterized by smooth regional gradients with isolated features. The main goal of magnetic prospecting is to infer both the geometry and the magnetization of the geologic structure that causes the observed magnetic anomalies. However, akin to other potential-field methods, interpretation of magnetic field anomalies is non-unique because more than one distribution of magnetization (i.e., magnetic dipole moment per unit volume) and source geometry can explain the same observed magnetic anomaly. One important goal in the interpretation of magnetic data is to determine the geometry and the location of the magnetic source. This has recently become particularly important because of the large volumes of magnetic data that are being collected for environmental and geological applications. To this end, a variety of semiautomatic methods based on the use of derivatives of the magnetic field have been developed to determine magnetic source parameters such as locations of boundaries and depths. As faster computers and commercial software have become widely available, these techniques are being used more extensively. Utilizing first-order derivatives of the magnetic field, Euler deconvolution was first applied on profile data and subsequently on gridded data. The method has come into wide use as an aid for interpreting magnetic data. The main advantage of the Euler method is that it can provide automatic estimates of the source location of the causative magnetic anomalies. However, it requires an assumption about the geometry of the body that is the actual source. In practice, assumption is achieved by specifying a structural index h to define the source geometry in generalized situations, setting a good strategy for discriminating, and selecting meaningful solutions. Recent extensions to the Euler method allow h to be estimated from the data, with the calculation of Hilbert transforms of the derivatives. The SPI method, which requires second-order derivatives of the field, uses a term known as the local wavenumber to provide a rapid estimate of the depth of buried magnetic bodies. The local wavenumber was defined as the spatial derivative of the local phase. The SPI method worked on gridded data, but assumed a contact model (h=0). Later extensions to the method enabled calculation of h, but these required third-order derivatives. The calculation of third-order derivatives from gridded data is problematic, so the use of profile data was advocated by Smith et al. (2005).In a more recent paper, a linearized least-squares method was applied to obtain information about the depth and nature of the buried sources from first-and second-order derivatives of the field (the ANALYTIC SIGNAL and its horizontal gradient). However, their approach requires knowledge of the horizontal position of the source, inferred from the peak of the ANALYTIC SIGNAL. Inappropriate sampling of the data and/or noise can make the selection of the horizontal position inaccurate. As a result, these inaccuracies lead to errors in the estimatation of both the depth and the nature of the sources.To overcome the limitations of the previous studies and to improve the process of estimating the source parameters using the ANALYTIC SIGNAL approach, an automatic method is presented to estimate horizontal location, depth, and the nature of 2D magnetic sources using derivatives of the ANALYTIC SIGNAL. Derivatives of the field of up to only the second order are used. First, a generalized equation is derived and solved using the least-squares method to provide source location parameters without any a priori information about the nature of the source. Then, using the estimated source location parameters, the nature of the source is obtained. To implement the method, the anomalies are first identified using the ANALYTIC SIGNAL peak. The method is then applied to a data window around the peak, where the SIGNAL-to-noise ratios of both the ANALYTIC SIGNAL derivative and the horizontal gradient of the ANALYTIC SIGNAL are relatively high. The determination of the number of data selected is based on the quality of the data and interference from nearby sources. The optimum number of selected data is small enough to see only a single anomaly and large enough to contain sufficient variations in the anomaly within the window. In this study, data for which the ANALYTIC SIGNAL values are greater than 10% of the peak value were used within each window.The presented method was applied successfully to synthetic magnetic data from 2D models with random noise as well as on a 3D synthetic Bishop model. In synthetic examples, we tested the feasibility of the proposed method; using theoretical anomalies of 2D magnetic models buried at different depths. These models were a horizontal cylinder with an infinite horizontal extent and a thin dike with infinite depth extent. The total-field anomaly values were calculated along a 100 km profile striking south–north at intervals of 1 km.Good results were obtained on a real magnetic dataset related to an ore field in Jalal-Abad, Iran, which has a broad correlation with drilling. In this regard, the results obtained by the proposed method were selected as start point in 2D modeling, and this shows a good fit with the measured profile.

Gravity and its usage in gravity interpretation is still a challenging field. It is not easy to compute these gradients especially in the case of noisy data. ANALYTICal SIGNAL is one of the new methods that uses gravity gradients to locate the perturbing body. This method had already been used for high-resolution magnetic and gravity data and rarely used for low-quality gravity data. The gravity gradients and ANALYTICal SIGNAL have been applied in two different areas, Zahedan where we are looking for Choromite anomalies and Tehran (Mard Abad) where we are investigating for low density anomaly, Probably hydrocarbon.

This paper presents a new method for interpretation of two dimensional (2D) magnetic anomaly data. The new method uses a combination of ANALYTIC SIGNAL and its total gradient to estimate the depth and nature-i. e. structural index-of an isolated magnetic source. However, the proposed method is sensitive to noise. In order to lower the effect of noise, upward continuation technique is applied to smooth the anomaly. Tests on synthetic noise-free and noise-corrupted magnetic data show that the new method can successfully estimate the depth and nature of the causative source. For practical application of the method, it was applied to measured magnetic anomaly data from Khalilabad area. For validity of the method, it was tested on a synthetic example with and without random noise. After adding 5%, 10% and 15% random noise to the synthetic data, the maximum error for the model parameters was seen to be less than ± 3%. Moreover, it was found that the inversion results of magnetic data from an area in northeast Sirjan was in good agreement with the results from Euler deconvolution of the ANALYTIC SIGNAL of the magnetic data. Introduction An important goal in the interpretation of magnetic anomaly data is to obtain the depth and the geometry or structural index of the causative source. To this end, a large number of methods exist to accomplish this goal. The ANALYTIC SIGNAL method is a popularly used method for this purpose. Using the ANALYTIC SIGNAL method, we can calculate both the depth and structural index of causative sources. However, the improved ANALYTIC SIGNAL method, presented in his paper, needs the computation of third-order derivatives of the magnetic anomaly and requires data of high precision or strict filtering. Methodology and Approaches In this paper, we present an improved ANALYTIC SIGNAL method to interpret 2D magnetic anomaly data. The proposed method uses a combination of the ANALYTIC SIGNAL and its total gradient to estimate the depth and the structural index of the causative source. The feasibility of the proposed method to compute the source parameters is displayed on synthetic and measured magnetic anomalies. Results and Conclusions We have proposed a new method to estimate the depth and the structural index of the causative source using the ratio of ANALYTIC SIGNAL to its total gradient. Our method provides two linear equations to estimate individually the depth and nature of a magnetic source. The feasibility of the new method is demonstrated on synthetic magnetic anomaly with and without random noise. For noise-free data, the structural index and the depth, estimated by the new method, are consistent with the theoretical values. For noise-corrupted data, our approach can provide reasonable results by applying upward continuation technique to lower the effect of noise. Application of the method on measured data indicates that the results of the method are in god agreement with the results computed by the Euler deconvolution of ANALYTIC SIGNAL method.

Horizontal and vertical gradients of the potential fields are used routinely to enhance the edge of the magnetic and gravity sources; furthermore, they are used as useful tools in interpreting and processing of magnetic and gravity data. In general, the derivatives of the potential fields are divided into horizontal and vertical derivatives, and they have always been significant tools in interpreting and processing of potential data. The derivatives can be determined in two procedures, direct measuring when the data are recorded, and calculation using mathematical and numerical methods. Many interpreting methods, that estimate the depth, location and the shape of a potential source, are based on using the gradients of potential fields. For example, both ANALYTIC SIGNALs and Euler Deconvolution methods, that have been widely applied, basically use the potential field derivatives. In these methods, different kinds of first order derivatives or derivatives of other positive integer orders are commonly used. In the basic equations of these methods, it is possible to use the derivatives of fractional orders in place of derivatives of other positive integer orders. Derivatives are high pass filters. They intrinsically amplify any noise and shallow anomalies present in the data. Therefore, using high order derivatives would be less common. Instead of using high order derivatives, one should use fractional order derivatives of the field. Besides, negative order derivatives are applicable in these kinds of methods and equations, and they can be considered as an interesting property of negative order derivation that acts as a low pass filter. In addition, horizontal fractional derivatives can be used instead of reduction to the pole at low latitudes to eliminate the instability of the reduced data. In this paper, the methods of the field gradient calculation, their alternation, and the application of the fractional order derivatives in ANALYTIC SIGNALs and reduction to the pole were inquired. To study the effects of the derivatives of different orders, the method was applied to synthetic data generated by various magnetic models such as a thin dike, and a horizontal cylinder. In the next stage, to simulate the real cases, the data was contaminated by random noise. To produce the synthetic data, the forward modeling was used. Finally, the method was applied to an aeromagnetic data set acquired over an area in Sweden. According to the geological studies in this region, there exists a granite intrusive body with certain fractures in which Diabase veins have penetrated. The results show that the fractional order derivatives as well as negative order ones are useful in data processing, and they can be considered as the principle of some of interpreting methods. All of the processing steps in this paper have been performed by using the code that we have written in Matlab.

Degradation of rivers and the associated loss of biodiversity reduces ecosystem health and water quality. One of the best practical approaches to understand ecological status of a water body and determine impacts of human activities in reducing water quality is the use of benthic macroinvertebrates as evaluation tools for monitoring their biological integrity and health. The aim of this study was to evaluate the health of Zarin Gol River using SIGNAL index. Macroinvertebrate samples were taken using Surber sampler (an area of 900 cm2) with 3 replicates in 4 sampling sites (upstream, entrance of fish farm, forest area) in winter and spring seasons on Zarin Gol River. The total number of abundance of macroinvertebrate were counted 1971 belonging to 8 order, 19 families. The result showed that SIGNAL index among all stations are same and lies in quadrant a. but SIGNAL 2 score result indicates that station 3 lies in quadrant b. In general, based on the results of the distribution of macroinvertebrates and biotic index, the influence of these factors on Zarin Gol river is quite evidence and the stations that were affected by a variety of effluents (2, 4) had undesirable conditions in the studied river.

In order to obtain maximum information from magnetic and gravity anomaly maps, application of an edge detection method is necessary. In this regard two commonly used methods are derivative filters and local phase filters. In this paper, a MATLAB code is expanded to combine an ANALYTIC SIGNAL filter and a tilt angle filter as a new edge detection filter called ASTA filter. This method was demonstrated on synthetic magnetic data from 3 models and also on real magnetic and gravity data from southwest England. Findings show that the boundaries of a causative body are enhanced more accurately using this new filter, compared with other edge detection filters.The source code in MATLAB format is available from the authors on request.

In this paper a new method is proposed for interpretation of 2D magnetic data, using multiples of the ANALYTIC SIGNAL method, in which the ANALYTIC SIGNAL of magnetic anomaly is used directly to compute the depth and the structural index of the source instead of using its higher order derivatives. This method only needs the computation of the first order derivatives of the magnetic anomaly, so the results are more stable than the results obtained by the other existing ANALYTIC SIGNAL methods. This method is applied on synthetic magnetic data with and without noise, and the proposed method can successfully obtain the depth and the structural index of the sources. We also applied this method to interpret a real magnetic data over a shallow source related to the SOURK Iron Ore mine in Iran, whose source parameters are known from closely core drilling data, and the estimated results are in agreement with the true values.

Investigating the possibility of underground structures detection is one of the complicated problems. In this paper, magnetic data have been used and both direct and inverse problems have been considered. In direct problem, with the assumption of the known size and position of the structure, magnetic response is modeled. Then, using the modeled SIGNALs, some points about the detectability of the structures are discussed. In inverse problem, position of the underground target is estimated based on the ANALYTIC SIGNAL and Euler deconvolution methods and 3D inversion of the magnetic data. Finally, both of the direct and inverse problems are implemented based on the simulated data and some suggestions are made to decrease the probability of detectability of the underground targets. The results show a concealed military structure with a susceptibility more than 0. 05 SI and the depth of less than 200 m can be detected and located in a nonmagnetic host rocks using aeromagnetic survey with a resolution more than 60 m between lines. With increasing the magnetic susceptibility of structure, the possibility of detection will be increased; so that if the susceptibility exceeds 0. 01 SI, the possibility of detection will be increased to depth of 400 m.

The components of Gravity Gradient Tensor (GGT) is used for second-order derivatives of the gravitational potential field in the directions x, y and z in a Cartesian coordinate system. The third column of the gravity gradient tensor is Hilbert transform pairs of the first and the second columns. Many methods have been designed to estimate the depth, the horizontal position and the type of the sources from gravity gradient tensor components. Often, these methods are used derivatives of potential field data or their compounds in directions x, y, and z. Standard Euler deconvolution method is an approach in the interpretation of potential field data. It is able to locate the sources and to estimate the regional parameters with the assumption of the structural index. This approach is an automated method that has seen rapid development in recent years. The result of this method closely related to the precision of the assumed structural index parameter, and the accuracy is reduced in the presence of interference sources. Euler deconvolution of the directional ANALYTIC SIGNAL amplitudes is one of many methods to eliminate this problem. It is shown that the components of the gravity vector satisfy Euler's equation. Thus, it is proved that the amplitudes of directional ANALYTIC SIGNAL are homogenous and by putting in Euler's equation can estimate the location and the structural index of the gravity anomalies. In addition, two new equations were obtained from the combination of directional ANALYTIC SIGNAL amplitudes that is very effective in locating and estimating the structural index of gravity sources. This paper was examined the application of Euler's equation of the directional ANALYTIC SIGNAL amplitudes to determine the location and the structural index of gravity anomaly sources. First, it is proved that each of directional ANALYTIC SIGNAL amplitudes in directions x, y, and z satisfy Euler's equation. Second, using the combination of directional ANALYTIC SIGNAL amplitudes derived two new equations that is more successful in determining the location (horizontal positions and depth) and source type (structural index) directly over the edges of gravity anomaly sources. The maxima of ANALYTIC SIGNAL amplitude in the z-direction place directly on the edge of the anomaly sources, but the maxima of ANALYTIC SIGNAL amplitude in the x-and y-directions deviate from the edges. That is why the simultaneous use of two or three-directional ANALYTIC SIGNAL amplitude can provide more accurate solutions. The method described above was tested on the synthetic model in the presence of relatively high level Gaussian noise and interference sources. Finally, the method was applied to the Safoo manganese ore and obtained horizontal position, depth (~6 m) and structural index. MATLAB software was used to apply the abovementioned methods.

An important goal in mining exploration is the estimation of the depth and the thickness of the causative source. According to this simplification, several methods have been developed for interpreting magnetic field anomalies. In this article, the Hilbert transform has been used to calculate the depth and thickness of 3-D thin plate anomalies. The Hilbert-Fourier transform performs an important role in ANALYTIC SIGNALs. Since the total magnetic fields anomalies function has the characteristics necessary for an ANALYTIC function, i. e. its real and imaginary parts form a Hilbert transform pair, the function can be used to interpret networked data in terms of three-dimensional origins. The Hilbert transform does not change the amplitude of a function but shifts the phase by π /2 and-π /2 for positive and negative phase values, respectively. This paper uses a two-dimensional Hilbert transform and a 3-D ANALYTIC function to calculate the depth of a thin three-dimensional plate modeled based on the method of Talwani for noisy data and without noise data. The results show that the estimated depth values derived from the Hilbert transform method are associated with an error of less that 3% for data without noise, and an error of 8% for data of 15% noise. . This method was also tested on the real magnetic anomaly data from the Kheirabad iron mine located at 5 km NE of Golgohar, Sirjan, Iran. The results were compatible with the Euler method and with drilling information of the mine. The obtained depth is in good agreement with the actual depth, which confirms the application of the Hilbert transform for the interpretation of field data and estimation of magnetic anomalies depths.