【摘要】：隨著地震勘探逐步走入復(fù)雜地形勘探的環(huán)境,因而所獲得的地震信號(hào)的信噪比較低。同時(shí)這些復(fù)雜地形中的地震信號(hào),往往存在著較為嚴(yán)重的信號(hào)缺失和廢道,廢炮,這為地震信號(hào)的處理提出了新的要求和挑戰(zhàn)。而由于地震信號(hào)在頻域的稀疏性,其可以使用較少量的系數(shù)來表征整個(gè)地震信號(hào),同時(shí),各種使得地震信號(hào)質(zhì)量下降的污染都可以被視為對(duì)這種稀疏性的破壞。因而該性質(zhì)可以被有效地應(yīng)用其來對(duì)地震信號(hào)進(jìn)行更加精確的處理,從而提高地震剖面的質(zhì)量。本文圍繞地震信號(hào)的去噪,地震信號(hào)的缺失道重建以及超分辨率重建這三個(gè)主要問題,從深入學(xué)習(xí)稀疏表示和壓縮感知的理論框架出發(fā),重點(diǎn)研究了稀疏表示和壓縮感知理論在地震信號(hào)處理流程中得典型應(yīng)用。主要研究?jī)?nèi)容如下:對(duì)于地震去噪問題,本文在研究了Shearlet變換的理論與實(shí)現(xiàn)和閾值去噪的基礎(chǔ)上,提出了運(yùn)用各向異性擴(kuò)散濾波器對(duì)子帶進(jìn)行處理的去噪方法,達(dá)到了同時(shí)在Shearlet域上進(jìn)行了信號(hào)增強(qiáng)和噪聲抑制的目的。同時(shí),本文對(duì)其進(jìn)行了數(shù)值仿真與分析另外,本文針對(duì)地震數(shù)據(jù)的缺失道重構(gòu)問題,在對(duì)地震缺失道的模型進(jìn)行了分析的基礎(chǔ)上,針對(duì)多種促稀疏重構(gòu)方法進(jìn)行的研究,應(yīng)用與詳細(xì)的結(jié)果分析。并針對(duì)這些方法在地震數(shù)據(jù)重構(gòu)領(lǐng)域中的應(yīng)用范圍進(jìn)行了對(duì)比和分析。繼而本文設(shè)計(jì)了一種兩步法進(jìn)行地震數(shù)據(jù)的超分辨率重建的策略,依據(jù)不同的數(shù)據(jù)規(guī)模,選擇不同的方法和稀疏表示來分別對(duì)時(shí)間分辨率和空間分辨率進(jìn)行處理。并對(duì)理論地震數(shù)據(jù)以及實(shí)際地震數(shù)據(jù)進(jìn)行了算法的仿真與測(cè)試,取得了較好的效果。
[Abstract]:The signal-to-noise ratio (SNR) of the obtained seismic signal is low with the seismic exploration gradually moving into the environment of complex terrain exploration. At the same time, the seismic signals in these complex terrain often have more serious signal missing, abandoned road, abandoned gun, which put forward new requirements and challenges for seismic signal processing. Due to the sparsity of seismic signal in frequency domain, a small number of coefficients can be used to characterize the whole seismic signal. At the same time, all kinds of pollution that make the quality of seismic signal decline can be regarded as damage to the sparsity. Therefore, this property can be effectively used to process seismic signals more accurately, thus improving the quality of seismic profiles. This paper focuses on the three main problems of seismic signal denoising, seismic signal missing channel reconstruction and super-resolution reconstruction, starting from the theoretical framework of sparse representation and compression perception. The typical application of sparse representation and compression sensing theory in seismic signal processing is studied. The main research contents are as follows: for the problem of seismic denoising, based on the study of the theory and implementation of Shearlet transform and threshold denoising, an anisotropic diffusion filter is proposed to deal with subband denoising. The aim of both signal enhancement and noise suppression in Shearlet domain is achieved. At the same time, numerical simulation and analysis are carried out. In addition, aiming at the problem of reconstruction of missing traces in seismic data, based on the analysis of the model of seismic missing traces, this paper studies several methods to promote sparse reconstruction. Application and detailed result analysis. The application scope of these methods in seismic data reconstruction is compared and analyzed. Then this paper designs a two-step method for super-resolution reconstruction of seismic data. According to different data scale, different methods and sparse representation are selected to process temporal resolution and spatial resolution respectively. The theoretical seismic data and the actual seismic data are simulated and tested, and good results are obtained.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：P631.4;TN911.7

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本文編號(hào)：2274076