ارتقاقدرت تفكيك داده هاي لرزه اي با استفاده از فشرده سازي تبديل موجك گسسته

Resolution Enhancements of Seismic Data Using the Discrete Wavelet Transform Compression

موجك ها نمونه هاي انتقال يافته و مقياس شده يك تابع (موجك مادر) با طول متناهي و نوساني شديداً ميرا هستند. يكي از روش هاي افزايش پهناي باند و قدرت تفكيك داده هاي لرزه اي، استفاده از تبديل موجك است. در اين مقاله از تبديل موجك گسسته پايا به عنوان ابزاري براي ارتقا قدرت تفكيك داده هاي لرزه اي استفاده شده است. براي اين منظور، ضمن مشخص كردن ضرايب موجك در داده هاي لرزه اي در مقياس هاي بالاتر(فركانس هاي پايين تر) و پيش بيني آنها در مقياس هاي پايين تر (فركانس هاي بالاتر)، نسبت به بالا بردن قدرت تفكيك در داده هاي لرزه اي اقدام شده است. روش كار به اين صورت بود كه ابتدا ردلرزه ورودي با استفاده از تبديل موجك پايا تجزيه شد و سپس برونيابي برپايه تبديل هيلبرت انجام گرفت. در نهايت، همه اين ضرايب با وارون تبديل موجك پايا باز گردانده شد كه حاصل تبديل موجك فشرده شده بود. درنتيجه، اين روش باعث افزايش قدرت تفكيك در داده هاي لرزه اي مي گردد.

Decomposition wavelet transform of a function is based on wavelet function. Wavelets ( That known as daughter wavelets) are the samples of a transferred and compared function (mother wavelet) with finite length and an extremely conductive. Nowadays , to transformation and estimation of the wavelet is one of the most important steps in processing and interpreting the seismic data. Wavelet transform plays an important part in the deconvolution and finding the synthetic trace. Based on the convolutional model, a seismic trace is the convolution of seismic source wavelet and re flection coefficient series of the earth. Seismic source wavelet estimation is one of the most important stages in processing and interpretation of seismic data. Accurate estimation of wavelet increases the efficiency of the deconvolution and temporal resolution of seismic data. We present a way of extending the bandwidth of band-limited seismic dataset, thereby enhancing their resolution. We make use of the stationary wavelet trans form (SWT) for implementing this resolution enhancement. We show that by using the known wavelet coefficients of the input dataset at higher scales (lower frequencies), it is possible to predict the lower scale (higher frequency) thereby improving the resolution of the input seismic data. The paper points to the applications of wavelet transform in seismic data at first. Then, to find the amount of effect of a discrete wavelet. (a stationary Mohammad Bidarigh, RiyazKharrat, MohammadrezaMoradi and ImanFaramarzi Azad University, Petroleum University of Technologydiscrete wavelet has been used here.) On a synthetic trace and real trace, an algorithm has been developed. The results of the algorithm indicate that the more the steps in decomposition of the discrete wavelet transform, the further is reduced the signal in a certain time interval. This feature can be used to reduce the noise from the desired signal and to enhance the resolution of seismic data. In continue for doing this, first decompose incoming trace through stationary wavelet transform. Then conduct extrapolation based on Hilbert transform, at the end reverse of stationary wavelet t ransform, that represents transformation of compressed wavelet