| Mineral resources are the basis of the national economy. Gravity and magnetic exploration occupies an important position in the metal, oil and regional geological studies. Gravity and magnetic exploration mainly includes three aspects:gravity and magnetic data acquisition, data processing and interpretation of gravity and magnetic data. Gravity and magnetic data processing is the most important part and is the basis of gravity and magnetic interpretation. With the increasing complexity of exploration conditions, the signal to noise ratio of collected gravity and magnetic signal becomes lower and lower, and how to denoise and separate these gravity and magnetic data so as to improve the quality of data processing is the key problem we are eager to solve.There are three problems in the gravity and magnetic signals collected under Complex exploration:First, the acquisition of gravity and magnetic data contains a lot of noise (random noise and background noise), removing noise is the base to obtain valid data; Second, with increasing depth geological, gravity and magnetic signals generated are relatively weak, it may be background noise and random noise mask. How to extract the weak signal is an increasingly real problem; Third, gravity and magnetic data collected is superimposed on several geological information. To separate the superimposed signal will help improve the accuracy and precision of gravity and magnetic interpretation.Many scholars have conducted research on these issues, propose various solutions. They apply partial differential equations, computational mathematics, nonlinear science, artificial intelligence, the latest research results into infiltrate gravity and magnetic treatment. Regrettably, the information from the current point of view, these methods are mostly based on power spectrum or correlation, requires some a priori conditions, such as assuming the signal is stationary process, Gaussian, etc. Actually, during gravity and magnetic exploration, these prior conditions are often difficult to meet (or too expensive), so the need to find new signal processing method, and as such, this paper attempts to blind source separation technology into the gravity and magnetic data processing.Blind source separation is the most popular field of signal processing technologies. And its biggest advantage is:we need not to know the source signal characteristics and channel transmission parameters, based solely on the observed signal to complete the separation of the mixed signal. The problems during gravity and magnetic exploration are very identical to blind source separation:we did not know how many underground geologic body, nor did we know how these geological signals are mixed, except just that the observed signal, we expect these signals to determine subsurface observations. This paper attempts to deal with blind source separation of gravity and magnetic data, the main work and the results include:1. Through the "cocktail party" phenomenon, we has been described for blind source separation, noting blind source separation "blind" has two meanings:First, the source signal can not be observed directly; Second, transmission channels is unknown (unknown signal mixed mode). Blind source separation gives the definition of the generalized linear blind source separation and blind source separation problem instantaneous mixture. Pointed out that the basic conditions for blind source separation are:source signals as statistically independent and Gaussian signal is not more than one. The method is also described with reasoning the two uncertainties of blind source separation:the magnitude of uncertainty solution, the solution sequence uncertain.2. Expounded the theory of blind source separation, including probability and statistical theory, information theory, blind source separation pretreatment. In the theory of probability and statistics, the concept of statistical independence, cumulant and kurtosis are given; In information theory, given the independence of the term used to measure the signal:Entropy (Entropy), negative entropy (Negentropy), Kullback-Leibler divergence, mutual information (Mutual Information); We pointed blind source separation should be conducted before preprocessing, including the signal of zero mean (centered), albino.3. Depending on the signal independence measure difference, will blind source separation algorithm is divided into three categories:information theory-based blind source separation algorithm, based on higher order statistics for blind source separation algorithm (HOS), based on second-order statistics for blind source separation algorithm (SOS).(1)An algorithm based on information theory, including three categories:the amount of information maximization algorithm (Infomax), maximum likelihood estimation (MLE) and the output mutual information minimization algorithm (MMI). By derivation we found, Infomax algorithm and algorithm is actually equivalent to the MMI. When the output signal of the edge components of the probability density function of each source signal is equal to the probability density function, MLE Infomax also equivalent. It can be seen, three algorithms are identical in nature,the consistency can be proved by their performance index PI.(2)Algorithm based on higher order statistics (HOS), can realize the blind separation of mixed-signal can also extract only some special signal (can be used to extract the weak signal), this is where the second-order statistics (SOS) can not work. HOS describes three main ways:HJ neural network algorithm, JADE algorithm, FastICA algorithm, in which, HJ algorithm is not stable enough, will fail at low SNR; JADE algorithm introduced in the fourth-order cumulant matrix vector.The algorithm is simple,robust and good; EFICA is developed on the basis of an FastICA algorithm and is more suitable for actual project data processing, but the time complexity is about three times FastICA. Man using JADE, EFICA algorithm gravity and magnetic data were processed(3)An algorithm based on second-order statistics (SOS), introduces the second-order blind identification (SOBI), the core idea is dialogue after the covariance matrix of the joint approximate diagonalization. SOBI methods used in the text to process the actual gravity data.4. We select Infomax, FastICA for simulation experiment and the results verify the effectiveness of the algorithm.5.Gravity and magnetic signals for non-Gaussian judgement.The non-Gaussian of gravity and magnetic signal, is the prerequisites of use of blind source separation technology. Gaussian definition:normal distribution probability density signal. Gaussian measure defines two indicators:skewness kurtosis K and S, illustrating the Gaussian criterion:K, S for both the0is Gaussian, or non-Gaussian signal. Select the sphere, the gravity model upright rectangular, oblique magnetic force model and model were calculated at different depths of skewness kurtosis K and S, we didn’t find both K and S at the same is0, so the weight of the magnetic signal is generally non-Gaussian signal.6. Separate the gravity signals of the two spheres on the vertical dimension by blind source separation (both of the cores are on the Z axis). Choose two adjacent survey lines (at a distance of50m), after the analysis of the separable outcomes from the first survey line of the two spheres can found that:(1) The effect of the blind source separation is related to the position of the survey line. When the location of the two spheres is fixed, the longer distance between the survey line and the projection position on the ground of the center of the sphere, the better effect of the blind source separation.(2) The effect of the blind source separation is related to the distance of two spheres. When the location of the first sphere is fixed, the greater distance between the spheres, the better effect of the blind source separation.(3) The effect of the blind source separation is related to the depth of burial. When two sphere’s distance is unchanged, the larger extent of the depth of burial, the better effect of the blind source separation under certain conditions.(4) When the residual density is fixed,the effect of the blind source separation is related to the radius of the two spheres. When the position of the sphere cores is fixed, the smaller length of the radius, the better effect of the blind source separation.7. Separate the gravity signals of the two spheres on both horizontal stacking and vertical stacking by blind source separation. Choose two adjacent survey lines (at a distance of50m), after the analysis of the separable outcome from the first survey line of the two spheres can found that:(1) The effect of the blind source separation is related to the position of the survey line. When the location of the two spheres is fixed, the longer distance between the survey line and the projection position on the ground of the center of the sphere, the better effect of the blind source separation.(2) The effect of the blind source separation is related to the vertical distance of the two spheres. When two sphere’s horizontal distance is unchanged, the greater length of the vertical distance, the better effect of the blind source separation..(3) The effect of the blind source separation is related to the horizontal distance of the two spheres. When two sphere’s vertical distance is unchanged, the greater length of the horizontal distance, the better effect of the blind source separation.(4) The effect of the blind source separation is related to the depth of burial. When two sphere’s relative position is unchanged, the larger extent of the depth of burial, the better effect of the blind source separation under certain conditions.(5) When the residual density is fixed,the effect of the blind source separation is related to the radius of the two spheres. When the position of the sphere cores is fixed, the smaller length of the radius, the better effect of the blind source separation.8. Blind source separation method are used of gravity and magnetic data de-noising and extraction of weak signals. In this paper, EFICA algorithm are used and sub-four cases discussed in this issue:First, the linear background field conditions, weak signal extraction (linear background field, excluding random noise); Second,linear background field and under the conditions of weak random noise signal extraction (linear background field, including random noise); Thrid,non-linear background field under weak signal extraction (nonlinear background field, excluding random noise); Fourth,non-linear and random noise background field under weak signal extraction (nonlinear background field, including random noise). Experimental results show that, EFICA procedure of the above four cases can detect a weak signal well. In addition,EFICA method,moving average method, matched filtering, trend analysis, wavelet decomposition potential field-scale separation methods were compared, the results showed that: Compared with the traditional potential field approach, blind source separation in weak signal extraction has certain advantages.9. We process the actual gravity and magnetic data with a blind source separation method. We select Nanling region1:200,000Bouguer gravity anomalies plan,△T aeromagnetic anomalies plan for the study. In the two figures, two survey lines were selected with SOBI separation, results consistent with the geological conditions. Then explain the step of the transition from the survey line to the plane, gived the key procedures. The Program is used for Bouguer gravity anomalies process and got regional courts and local courts. Experimental results show that:the regional courts got from the method, compared with5km upward continuation plan Bouguer gravity anomaly are quite similar; Obtained by this method, Bureau domain field of gravity low and concealed granite boundary correspondence is good. Described above, blind source separation is an effective method for gravity and magnetic data processing, it is worth further study. |
PDF Full Text Request