جستجوی مقالات مرتبط با کلیدواژه « تجزیه مد تجربی » در نشریات گروه « زمین شناسی »

The geophysicsl potential field separation refers to separation of the regional and local anomalies from the superimposed anomaly. The Empirical Mode Decomposition (EMD) proposed by Norden E Huang is a kind of spatial and temporal filtering process in terms of the signal extremum characteristic scales. It is a new data analysis method suitable for processing non-stationary and non-linear data. Its power to filter and decompose the data has earned it a high reputation in signal processing. Empirical mode decomposition is a time-frequency analysis method which can adaptively decompose complex signals. The decomposed component contains diffrent bands of frequencies from high to low، and the residual value is the signal trend component representing the signal averaged trend، which is similar to the regional anomalies in the geophysical field. The empirical mode decomposition (EMD) method is an algorithm for the analysis of multicomponent signals that breaks them down into a number of amplitude and frequency modulated zero-mean signals، termed intrinsic mode functions (IMFs). An IMF must fulfill two requirements: (1) the number of extrema and the number of the zero crossings are either equal or differ at most by one; (2) at any point، the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero. Based on this theory، applying the EMD to separate the geophysical potential field is proposed in this article. When EMD is used for anomaly separation، the problem is to identify properly which IMFs contain residual characteristics. Certain modes will consist of mainly residual، whereas other modes will contain regional and noise characteristics. Magnetic field anomalies are usually superposed large-scale structures and small-scale structure anomalies. Separation of these two categories of anomalies is the most important step in the data interpretation. Different methods have been introduced forthis work، but most of them are the semi-automatic methods; it means that the interpretator’s opinion can directly affect the results. In this study، EMD method has been used to separate regional and residual magnetic anomalies. EMD decomposition results in what is “residual”، which is similar to the regional anomaly of a potential field data. This residual does not require any preset parameters unlike contemporary field separation methods. This automatic method is based on the extraction of the intrinsic oscillatory modes of data. Efficiency of this method has been investigated on both synthetic and real data acquired in North Mahalat area of Markazi Province to study the regional subsurface geology with the purpose of geothermal reserver explorations. Compared to the conventional method of trend analysis، the EMD method is affected by less artificial influence، and we did not need to set any parameters beforehand. Otherwise، it reflected the potential field intrinsic physical characteristics better. Separation results showed that this technique had higher accuracy than conventional methods such as polynomial fitting and had a good consistency with regions geology. Finally، the results of the new method were compared with results of the upward continuation filter and we observed that these results were matched with the upward continuation filter.

Potential field anomalies are usually superposed large-scale structures and small-scale structures anomalies. Separation of these two categories of anomalies is the most important step in the data interpretation. Different methods have been introduced for these types of work, but most of them are the semi-automatic methods. In this paper, empirical mode decomposition method is used to differentiate regional and residual anomalies. This automatic method is based on extraction of the intrinsic oscillatory modes of data. Efficiency of this method is investigated on both synthetic and real data acquired on Tromspberg area of South Africa. Different results show that this technique have higher accuracy than conventional methods like as polynomial fitting.

The quality of seismic data varies tremendously, from areas where excellent reflections(or refractions) are obtained to areas in which the most modern equipment, complex fieldtechniques, and sophisticated data processing do not yield usable data. Between theseextremes, lie most areas in which useful results can be obtained. Seismic records aregenerally affected by various types of noise, such as ground rolls, multiples, randomnoise, and reflection and reflected refraction from near surface structures. Random noiseresultiing from random oscillation during data acquisition is one of the most importantand harmful noises that exists in seismic data over all times and frequencies. Many effortshave been made to remove this type of noise from seismic data. The predictive filter is anordinary method commonly used for random noise attenuation from seismic data. Thisfilter can be used in various domains, such as the f-x domain (Haris and White, 1997) andthe discrete Cosine domain (Lu and Liu, 2007). Jones and Levy (1987) removed events which were not coherent trace-to-trace events by means of the Karhunen-Loevetransform. The empirical mode decomposition (EMD) method is an algorithm for the analysis of multicomponent signals that breaks them down into a number of amplitude and frequency modulated zero-mean signals, termed intrinsic mode functions (IMFs). An IMF must fulfill two requirements: (1) the number of extrema and the number of zero crossings are either equal or differ at most by one; (2) at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero. In contrast to conventional decomposition methods such as wavelets, which performthe analysis by projecting the signal under consideration onto a number of predefinedbasis vectors, EMD expresses the signal as an expansion of basic functions that aresignal-dependent and estimated via an iterative procedure called sifting. Apart from thespecific applications of EMD, a more generalized task in which EMD can prove useful issignal denoising (Kopsinis and McLaughlin, 2009). When EMD is used for denoising, theproblem is to identify properly which IMFs contain noise characteristics. Certain modeswill consist mainly of noise, whereas other modes will contain both signal and noisecharacteristics. In the case of white Gaussian noise, the noise-only energy of the modesdecreases logarithmically. The first mode, carrying the highest amount of noise energy,will consist mainly of noise, and the effect of noise should gradually weaken with highermodes. In this paper, a new signal denoising method based on the empirical modedecomposition framework is used to suppress random noises in seismic data. A Noisysignal is decomposed into oscillatory components (IMFs). The empirical modedecomposition denoising method involves filtering each intrinsic mode function andreconstructing s the estimated signal using the processed intrinsic mode functions. Thedirect application of wavelet-like thresholding to the decomposition modes is, inprinciple, wrong and can have catastrophic consequences regarding the continuity of thereconstructed signal. This arises as a result of the special attributes of IMFs; namely, theyresemble an AM/FM modulated sinusoid with zero mean. Consequently, we used theinterval thresholding method instead of direct theresholding method to denoise seismicsignal. the efficiency of the proposed method was tested on both synthetic and real seismic data. In every case, results show that the denoising algorithm can suppress random noise significantly.