Seismic noise can be divided to Random and coherent in reflection survey. The ground roll is a coherent noise in land seismic data that has high energy، high amplitude، low frequency and low velocity. It usually masks the reflections. Therefore، it must be attenuated in the seismic data processing. In this paper، we proposed a modification on the common offset common reflection surface method to attenuate ground roll and Random noise. The CO CRS stacking operator is a hyperbola; therefore، it fits the hyperbolic reflections in the prestack data. Ground roll and Random noise has linear and uncorrelated traveltime respectively. When the CO CRS operator is applied to the data، the reflection events can be detected by the coherency analyses. High coherency values belong to the reflection events، and low values indicate that no events with hyperbolic traveltime are detected. As a result، when the events are distinguished، any event with non-hyperbolic traveltime can be muted. We applied the proposed method on two real land data sets. The new method was compared with the f-k filtering and conventional CO CRS stacking after the f-k filtering. Results showed that the proposed method attenuated aliased ground roll better than the f-k filtering and conventional CRS. Further investigation was the effect of reflection amplitudes on ground roll attenuation by the CO CRS stacking. For a better attenuation، the minimum coherency of reflections had to be higher than the maximum coherency of the ground roll. Therefore، the intersection of the minimum reflections coherency and the maximum ground roll coherency is an SNR threshold (dB) for ground roll attenuation with FO CRS stacking.

In seismic data processing, the processing steps are completely affected by the data quality. Reflection seismic data are often affected by various noises including Random and coherent noises. Low signal to noise ratio can produce problems for stacking and migration steps, which ultimately leads to poor interpretation. There are many methods that can be used for noise removal or attenuation of seismic data. The basic assumption of the Fourier transform is that it considers stationary signal, thus, for non-stationary signals, it is not always applicable. Based on this fact that the wavelet transform decomposes a function by translation and stretching, it can provide time-scale representation of a signal. In this paper, we have used SURE-LET method for noise removal in the wavelet transform domain. In the SURE-LET method, any assumptions of noise free signals are avoided.

The presence of many types of noises such as Random noise in the seismic data cause some problems; so, they must be attenuated in the processing steps. Singular value decomposition (SVO) is a coherency and linear algebra based filter, which can detect horizontal events in the first eigenimages. For Random noise attenuation, after geometry assigning, in common depth point (COP) gather, after velocity analysis and dynamic corrections and before stacking data, SVO is applied to data. The aligned reflectors are detected at first eigenimages, then they are reconstructed; hence another eigenimage, which contains Random noise, is zeroed and the Random noise will be attenuated. Because the SVO can detect the horizontal event, if static and dynamic corrections are not applied to data correctly and in the common depth point gather, the reflectors have fluctuations and SVO cannot separate between reflectors and Random noise viable. In this paper, these steps are applied to a synthetic common depth point gather with various ratios of signal to noise and to a real common depth point gather from one of the Iranian land hydrocarbon field. According to the results, singular value decomposition can attenuate the Random noise and preserves the reflectors considrably. Furthermore, this subject is shown in the synthetic data with high noise level (SNR=1).

Time-frequency filtering is an acceptable technique for attenuating noise in 2-D (time-space) and 3-D (time-space-space) reflection seismic data. The common approach for this purpose is transforming each seismic signal from 1-D time domain to a 2-D time-frequency domain and then denoising the signal by a designed filter and finally transforming back the filtered signal to original time domain. The technique is efficient for ground roll and also Random noise attenuation. However, if we deal with a large data set and a great number of contaminated signals with ground roll noise, a much move consuming time will be required. In this paper, time-frequency filtering is formulated and carried out by a different approach. The data is transformed from original timespace domain into several single-frequency time-space domains, and the filters to reduce noise is designed in the new domains. The transform is easily and completely invertible. The employed time frequency analysis method is a high-resolution version of S-transform. Application to synthetic and real shot gathers confirms the good performance and efficiency of the method for attenuating ground roll noise and Random noise.

Anisotropic diffusion filtering (ADF) is widely used as an efficient method in Random noise attenuation problems, and various modifications to its original version have been proposed. The main reason could be the thought that ADF preserves edge features with acceptable performance beside noise attenuation procedure. In seismic data processing, however, it should be noticed that using ADF could cause severe changes (artifacts) in the zones that are highly contaminated with Random noise. In this paper, the optimum value is derived, by introducing an automatic framework based on two artificial intelligence (AI) algorithms, adaptive neuro-fuzzy inferences (ANFIS) and fuzzy c-mean clustering (FCM). The neuro-fuzzy network is trained using original data, successive ADF values are calculated for each data point, and FCM output is obtained in a weighted averaging manner adapted with estimated noise level. The trained network is, then, generalized to all data, and thus, the ANFIS optimized version of ADF, called here AOADF, is achieved. Comparison of the results of the ADF and AOADF experiments reveals that in synthetic common mid-point (CMP) gathers, the proposed method improves peak signal to noise ratio (PSNR) value, 40% higher than ADF (in the best case) and in real CMP and common offset sorted gathers, the performance of AOADF is considerably higher than ADF, in terms of Random noise attenuation without adding unwanted artifacts and preserving continuity of coherence components.

Unmanned aerial vehicles (UAVs) like drones (quadcopters, hexacopters, octocopters, etc.) can be a source of significant acoustic noise. High noise makes them less suitable for use in densely populated urban areas, particularly during take-off, landing, and low-level flight, due to the noise annoyance. This paper reviews the methods and proposes some concepts for the attenuation of UAV noise. Both passive and active solutions are considered. Solutions with piston engine silencer, Q-tip propeller, more propeller blades, absorptive and reflective barrier, ducted propeller, sound absorbing ducts, synchrophaser, multichannel active noise cancellation (ANC) system with secondary sources circularly arranged around the propeller and the system with magnetically excited propeller blades are mentioneds

Summary Multiple reflections are coherent seismic noises whose presence, especially in marine data, lower data quality. In this research "dual-tree rational dilatation wavelet transform" Or DT-RADWT is used to attenuate multiple reflection noise from seismic data. The advantage of this transform to the dyadic discrete wavelet transform, is its fractional sampling, which allows for higher timefrequency resolution. The proposed algorithm in this research is wavelet domain noise analysis or WDNA, in which DT-RADWT and split Bergman iteration algorithm are used. WDNA is a data-based algorithm. The split Bergman iterative algorithm is designed to quickly obtain the optimal solution. Radon transform is a common method to attenuate multiple reflections, and it is used to obtain the initial pattern of multiple reflections. The purpose of WDNA is to improve Radon transform output and to better maintain primary reflections. The presence of high levels of Random noise reduces the quality process of noise reduction, but WDNA is designed to overcome the adverse effect of Random noise. The WDNA results in multiple reflection attenuation have been tested by synthetic and marine data, and their results have been compared with Radon and WDGA outputs. The results show good improvement in seismic data quality using WDNA algorithm in comparison with Radon transform. Introduction The reflection waves, which is reflected between the subsurface or free surface reflectors more than once before being received on the receivers, are called multiple reflections. Multiple reflections, often destructively interact with the primary reflections and reduce the quality of the seismic image. An inverse filter based on predictive deconvolution using the periodic feature is used to attenuate multiple reflections in the water. Multiple and primary reflections show different moveout and travel-times, This property is the basis of the theory of many multiple attenuation techniques such as CMP stacking, F-K filter, and Radon transform. Radon transform was first introduced by Johann Radon (1917) and for the first time, parabolic Radon conversion was used as a multiple attenuation technique by Hampson (1986). Since then, the Radon transform became one of the most widely used tools to suppress multiple noises. Goudarzi and Riahi (2013) presented WDGA method based on the data type, as an efficient way of attenuating various seismic noises. However, this approach, if there is a high level of Random noise in the data, cannot well separate the coherent noise from the reflections. Here we try to introduce a new method to solve this problem. Methodology and Approaches The proposed method in this research is called wavelet domain noise analysis (WDNA) algorithm. Similar to WDGA, this method is based on data, but because of the use of the split Bergman iteration is less sensitive to Random noise. It also reduces the time to reach an optimal solution and it has better convergence. These features enable better detection of the desired noise and better signal separation from the noise. The goal of this research is to apply the benefits of Radon transform, and at the same time, to use the DT-RADWT wavelet transform capabilities to provide high resolution. We take advantage of the split Bergman iterative algorithm to build a full multiple reflection model from initial multiple models (achieved from Radon filter). Finally, in the DT-RADWT domine, full model of multiple reflections would be subtracted from the input data, and thus, the filtered data would be obtained. Results and Conclusions In this research, the WDNA algorithm has been introduced and its application in attenuating multiple reflections from seismic data has been investigated. The WDNA algorithm is based on the data and requires an initial noise model that is obtained from Radon transform (or any other suitable filter) to attenuate multiple reflections and in the dual-tree wavelet transform domain, it is used to produce a complete noise model with the Bergman iteration algorithm. Subtracting the full noise model from seismic input data yields almost no multiple reflection noise and the initial reflections are well maintained. The use of the DT-RADWT wavelet transform increases the frequency resolution and split Bergman algorithm helps to achieve a fast convergent solution that also causes insensitivity with Random noise in the attenuation process of multiple reflections. The results of applying the WDNA method on synthetic and real data have resulted in better outputs than Radon and WDGA.

In order to increase the signal/noise ratio, one of the important challenges in seismic data processing is suppression of Random noises. In this paper f-x deconvolution or deconvolution in frequency-space domain is employed for reduction of Random noises from seismic section. This is based on data transformation from time-space domain to frequency-space domain. Seismic events are correlatable along x-direction from trace to trace but Random noises are not. In fact f-x deconvolution is able to predict coherent events from trace to trace in space direction. In this paper a computer code for f-x deconvolution has been written. This program applied on shot records and zero offset section with different levels of Random noises. Consequently, the ability of f-x deconvolution in reduction of Random noises has been proved.

Summary:noise is a factor that influences the reliability of the seismic data to provide a better understanding of the hydrocarbon reservoir characteristics. Coherent and Random (incoherent) noises are two important types of noise that contaminate the seismic reflection data. Power-line noise falls in the coherent type of noise (Yilmaz, 2001; Sheriff and Geldart, 1995). It is a class of noise often existing in land acquisition in populated areas. It produces a characteristic 50 or 60 Hz sinusoidal noise on seismic reflection traces and covers the seismic data from reflectors. Its amplitude is time invariant, whereas the seismic data amplitude decays with time (Xia and Miller, 2000; Dingus, 2010)…