Статті в журналах: "Exploration noise"

1

Liu, Jinyi, Zhi Wang, Yan Zheng, Jianye Hao, Chenjia Bai, Junjie Ye, Zhen Wang, Haiyin Piao, and Yang Sun. "OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (March 24, 2024): 13954–62. http://dx.doi.org/10.1609/aaai.v38i12.29303.

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In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty. However, in the presence of environmental stochasticity (noise), purely optimistic exploration may lead to excessive probing of high-noise areas, consequently impeding exploration efficiency. Hence, in exploring noisy environments, while optimism-driven exploration serves as a foundation, prudent attention to alleviating unnecessary over-exploration in high-noise areas becomes beneficial. In this work, we propose Optimistic Value Distribution Explorer (OVD-Explorer) to achieve a noise-aware optimistic exploration for continuous control. OVD-Explorer proposes a new measurement of the policy's exploration ability considering noise in optimistic perspectives, and leverages gradient ascent to drive exploration. Practically, OVD-Explorer can be easily integrated with continuous control RL algorithms. Extensive evaluations on the MuJoCo and GridChaos tasks demonstrate the superiority of OVD-Explorer in achieving noise-aware optimistic exploration.

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Blanchard, Olivier J., Jean-Paul L'Huillier, and Guido Lorenzoni. "News, Noise, and Fluctuations: An Empirical Exploration." American Economic Review 103, no. 7 (December 1, 2013): 3045–70. http://dx.doi.org/10.1257/aer.103.7.3045.

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We explore empirically models of aggregate fluctuations in which consumers form anticipations about the future based on noisy sources of information and these anticipations affect output in the short run. Our objective is to separate fluctuations due to changes in fundamentals (news) from those due to temporary errors in agents' estimates (noise). We show that structural VARs cannot be used to identify news and noise shocks, but identification is possible via a method of moments or maximum likelihood. Next, we estimate our model on US data. Our results suggest that noise shocks explain a sizable fraction of short-run consumption fluctuations. (JEL D84, E13, E21, E32)

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3

Narščius, Aleksas, and Donatas Bagočius. "Assessment of Seismic Exploration Noise at Lithuanian Area of the Baltic Sea." DARNIOS APLINKOS VYSTYMAS 21, no. 1 (May 10, 2024): 48–56. http://dx.doi.org/10.52320/dav.v21i1.299.

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An article presents an evaluation of the underwater noise of the performed bottom seismic surveys in the waters of the Lithuanian Baltic Sea, analyzes the underwater noise levels that were determined by mathematical modelling. It presents an assessment of the risks posed to marine mammals by extremely high intensity underwater noise levels in the research area. Possibilities of underwater noise reduction for these noisy activities discussed.

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4

Abrams, Michael L. "Noise suppression during seismic exploration." Journal of the Acoustical Society of America 84, no. 6 (December 1988): 2302. http://dx.doi.org/10.1121/1.397004.

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5

Liu, Qian, Xuan Feng, Cai Liu, Minghe Zhang, You Tian, and Hesheng Hou. "Metallic mineral exploration by using ambient noise tomography in Ashele copper mine, Xinjiang, China." GEOPHYSICS 87, no. 3 (March 14, 2022): B221—B231. http://dx.doi.org/10.1190/geo2020-0923.1.

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Rapid advances in the seismic exploration method have allowed its application in metallic mineral exploration. However, 2D seismic profiles are often insufficient to describe the shape and areal extent of ore-bearing rock masses away from survey lines. Although more complete, collecting 3D seismic data is expensive, time consuming, and may require considerable investment in surface access. The combination of ambient noise tomography and 2D seismic reflection exploration methods can produce acceptable results relatively quickly and at a low cost. The enormous Ashele copper deposit in northwest China is a typical deposit formed by volcanic eruption. It is rich in resources and possesses good prospecting potential in its deeper and peripheral areas. We performed ambient noise tomography to investigate a near-surface 3D S-wave velocity ([Formula: see text]) structure above a depth of 0.7 km in the Ashele mining area (approximately 8 × 12 km) using 25 days of continuous ambient noise data. From the combined interpretation of the 3D [Formula: see text] structure and the existing 2D seismic reflection profile, we infer that there may be ore-bearing rock masses in the western and northern sides of the research area. We report the discovery of an ancient volcano at a depth of 500 m on the western side of this region. The banded velocity anomalies and the existence of the ancient volcano signify the formation process of a bimodal volcanic rock association. It has been proven that the combination of ambient noise tomography and 2D seismic reflection exploration methods can produce important results in metallic mineral exploration. Therefore, ambient noise tomography can be used as an economical, convenient, and efficient method for future explorations, complementing the 2D seismic reflection exploration method.

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6

Osowski, Stanislaw, Krzysztof Siwek, and Tomasz Grzywacz. "Exploration of noisy data in differential electronic nose." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 35, no. 4 (July 4, 2016): 1382–92. http://dx.doi.org/10.1108/compel-08-2015-0279.

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Purpose – The paper is concerned with exploration of sensor signals in differential electronic nose. It is a special type of nose, which applies double sensor matrices and exploits only their differential signals, which are used in recognition of patterns associated with them. The purpose of this paper is to study the application of differential nose in dynamic measurement of aroma of 11 brands of cigarettes. Design/methodology/approach – The most important task in pattern recognition using electronic nose is its resistance to the noise corrupting the measurement. The authors will analyze and compare the performance of the nose in the noisy environment by applying two classifier systems: the support vector machine (SVM) and random forest (RF) of decision trees. Findings – On the basis of numerical experiments the authors have found that application of SVM as the classifier in the electronic nose is more advantageous than RF, especially at high level of noise and small number of measuring sensors. Its application allowed to recognize 11 brands of cigarettes with the accuracy close to 100 percent. Practical implications – Thanks to application of two identical sensors working in a differential mode the authors avoid the baseline estimation and thus the solution is well suited for on-line dynamic measurements of the process. Originality/value – The paper has studied the advantages and limitations of the differential electronic nose following from the existence of the noise, corrupting the measurements. It has pointed an important role of the applied classifier system in getting the electronic nose of the highest quality.

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7

Yang, Yu, Qi Ran, Kang Chen, Cheng Lei, Yusheng Zhang, Han Liang, Song Han, and Cong Tang. "Denoising Seismic Data via a Threshold Shrink Method in the Non-Subsampled Contourlet Transform Domain." Mathematical Problems in Engineering 2022 (August 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/1013623.

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In seismic exploration, effective seismic signals can be seriously distorted by and interfered with noise, and the performance of traditional seismic denoising approaches can hardly meet the requirements of high-precision seismic exploration. To remarkably enhance signal-to-noise ratios (SNR) and adapt to high-precision seismic exploration, this work exploits the non-subsampled contourlet transform (NSCT) and threshold shrink method to design a new approach for suppressing seismic random noise. NSCT is an excellent multiscale, multidirectional, and shift-invariant image decomposition scheme, which can not only calculate exact contourlet transform coefficients through multiresolution analysis but also give an almost optimized approximation. It has better high-frequency response and stronger ability to describe curves and surfaces. Specifically, we propose to utilize the superior performance NSCT to decomposing the noisy seismic data into various frequency sub-bands and orientation response sub-bands, obtaining fine enough transform high frequencies to effectively achieve the separation of signals and noises. Besides, we use the adaptive Bayesian threshold shrink method instead of traditional handcraft threshold scheme for denoising the high-frequency sub-bands of NSCT coefficients, which pays more attention to the internal characteristics of the signals/data itself and improve the robustness of method, which can work better for preserving richer structure details of effective signals. The proposed method can achieve seismic random noise attenuation while retaining effective signals to the maximum degree. Experimental results reveal that the proposed method is superior to wavelet-based and curvelet-based threshold denoising methods, which increases synthetic seismic data with lower SNR from −8.2293 dB to 8.6838 dB, and 11.8084 dB and 9.1072 dB higher than two classic sparse transform based methods, respectively. Furthermore, we also apply the proposed method to process field data, which achieves satisfactory results.

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8

Wang, Rubin, Guanzheng Wang, and Jinchao Zheng. "An Exploration of the Range of Noise Intensity That Affects the Membrane Potential of Neurons." Abstract and Applied Analysis 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/801642.

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Neuronal activity in the human brain occurs in a complex physiologic environment, and noise from all aspects in this physiologic environment affects all aspects of nervous-system function. An essential issue of neural information processing is whether the environmental noise in a neural system can be estimated and quantified in a proper way. In this paper, we calculated the neural energy to estimate the range of critical values of thermal noise intensity that markedly affect the membrane potential and the energy waveform, in order to define such a noisy environment which neuronal activity relies on.

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9

Sun, Chuxiong, Rui Wang, Qian Li, and Xiaohui Hu. "Reward Space Noise for Exploration in Deep Reinforcement Learning." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 10 (May 21, 2021): 2152013. http://dx.doi.org/10.1142/s0218001421520133.

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A fundamental challenge for reinforcement learning (RL) is how to achieve efficient exploration in initially unknown environments. Most state-of-the-art RL algorithms leverage action space noise to drive exploration. The classical strategies are computationally efficient and straightforward to implement. However, these methods may fail to perform effectively in complex environments. To address this issue, we propose a novel strategy named reward space noise (RSN) for farsighted and consistent exploration in RL. By introducing the stochasticity from reward space, we are able to change agent’s understanding about environment and perturb its behaviors. We find that the simple RSN can achieve consistent exploration and scale to complex domains without intensive computational cost. To demonstrate the effectiveness and scalability of the proposed method, we implement a deep Q-learning agent with reward noise and evaluate its exploratory performance on a set of Atari games which are challenging for the naive [Formula: see text]-greedy strategy. The results show that reward noise outperforms action noise in most games and performs comparably in others. Concretely, we found that in the early training, the best exploratory performance of reward noise is obviously better than action noise, which demonstrates that the reward noise can quickly explore the valuable states and aid in finding the optimal policy. Moreover, the average scores and learning efficiency of reward noise are also higher than action noise through the whole training, which indicates that the reward noise can generate more stable and consistent performance.

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10

McCracken, K. G., M. L. Oristaglio, and G. W. Hohmann. "Minimization of noise in electromagnetic exploration systems." GEOPHYSICS 51, no. 3 (March 1986): 819–32. http://dx.doi.org/10.1190/1.1442134.

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The noise in electromagnetic (EM) surveys that originates in time‐dependent magnetic fields is quantified as a function of geographic location, season, and polarization. An overall variability of [Formula: see text] is observed. The variance of the observed geophysical data caused by this noise is quantified in terms of the noise bandwidth of the signal‐processing system. While a time‐domain EM (TEM) system has a wide bandwidth for the target (i.e., synchronous) signal, it has a narrow bandwidth for the asynchronous EM noise. A procedure is developed to compute the magnetic moment required to provide a specified ratio of signal to noise (S/N) in the survey data for frequency‐domain EM (FEM) and both step‐ response and impulse‐response TEM. This procedure indicates that magnetic moments in the range [Formula: see text] to [Formula: see text] are necessary for moving transmitter systems, and up to [Formula: see text] for fixed‐loop systems, for both frequency and time domain. A number of models of the overburden are established, and we show that the half‐space response is strongly dependent upon geology and weathering history. A variation of three orders of magnitude in the half‐space response occurs throughout the world. A procedure is defined to calculate the smallest target response detectable in the presence of a specified level of geologic noise. From this response we show that the overburden in the tropical and arid zones of the Earth strongly desensitizes the EM method. This is particularly severe for those styles of mineralization yielding short decay time constants. We conclude it would be impossible to detect targets with time constants <3 ms in substantial portions of the world’s arid zone using EM alone. An overall survey design procedure is defined that determines whether a specified target would be detectable in the presence of the prevailing geologic noise, and the magnetic moment that would be required to detect it in the presence of the anticipated EM noise. The manner in which the primary field corrupts both frequency‐ and time‐domain systems is also analyzed, and we conclude that the TEM method is essentially free of primary field effects, while the simpler forms of FEM can be corrupted by primary field effects which obscure targets with secondary responses that are less than 10 percent of the primary field.

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11

罗, 文. "Background Noise Analysis Method of Seismic Exploration." Open Journal of Acoustics and Vibration 11, no. 04 (2023): 111–18. http://dx.doi.org/10.12677/ojav.2023.114012.

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12

Prasetyowati, Sri Arttini Dwi, Bustanul Arifin, Agus Adhi Nugroho, and Muhammad Khosyi’in. "Exploration of Generator Noise Cancelling Using Least Mean Square Algorithm." Journal of Electrical Technology UMY 6, no. 1 (June 30, 2022): 22–32. http://dx.doi.org/10.18196/jet.v6i1.14826.

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Generator noise can be categorized as monotonous noise, which is very annoying and needs to be eliminated. However, noise-cancelling is not easy to do because the algorithm used is not necessarily suitable for each noise. In this study, generator noise was obtained by recording near the generator (outdoor signal) and from the room (indoor signal). Noise generator exploration is carried out to determine whether the noise signal can be removed using the Adaptive LMS method. Exploration was carried out by analyzing statistical signals, spectrum with Fast Fourier Transform (FFT) and Inverse FFT (IFFT), and analyzing the frequency distribution of the remaining noise. The results showed that the correlation coefficients were close to each other. Outdoor and indoor signals are at low frequency. The behavior of FFT and IFFT if described in two dimensions, namely real and imaginary axes, formed a circle with a zero center and has parts that come out of the circle. It confirms that noise-cancelling with adaptive LMS can be realized well even though some noise is still left. The residual noise has formed an impulse that showed normally distributed with mean=-0.0000735 and standard deviation =0.000735. This indicates that the residual noise was no longer disturbing.

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13

Wang, Xin, Shu-Li Sun, Kai-Hui Ding, and Jing-Yan Xue. "Weighted Measurement Fusion White Noise Deconvolution Filter with Correlated Noise for Multisensor Stochastic Systems." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/257619.

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For the multisensor linear discrete time-invariant stochastic control systems with different measurement matrices and correlated noises, the centralized measurement fusion white noise estimators are presented by the linear minimum variance criterion under the condition that noise input matrix is full column rank. They have the expensive computing burden due to the high-dimension extended measurement matrix. To reduce the computing burden, the weighted measurement fusion white noise estimators are presented. It is proved that weighted measurement fusion white noise estimators have the same accuracy as the centralized measurement fusion white noise estimators, so it has global optimality. It can be applied to signal processing in oil seismic exploration. A simulation example for Bernoulli-Gaussian white noise deconvolution filter verifies the effectiveness.

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14

Scales, John A., and Roel Snieder. "What is noise?" GEOPHYSICS 63, no. 4 (July 1998): 1122–24. http://dx.doi.org/10.1190/1.1444411.

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The concept of “noise” plays a crucial role in the statistical analysis of data. As an example of a noisy record consider Figure 1 that shows the ground motion of the seismological station NE51 in St. Petersburg after an earthquake in Egypt. (In earthquake seismology, periods may be orders of magnitude larger than in exploration seismology, but the principles are the same.) This time series shows no distinct arrivals or other apparent signatures of an organized nature. Given the proximity of the recording station to a major population center and to the coast, such a noisy record does not seem to be very surprising.

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15

Hollenstein, Jakob, Georg Martius, and Justus Piater. "Colored Noise in PPO: Improved Exploration and Performance through Correlated Action Sampling." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12466–72. http://dx.doi.org/10.1609/aaai.v38i11.29139.

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Proximal Policy Optimization (PPO), a popular on-policy deep reinforcement learning method, employs a stochastic policy for exploration. In this paper, we propose a colored noise-based stochastic policy variant of PPO. Previous research highlighted the importance of temporal correlation in action noise for effective exploration in off-policy reinforcement learning. Building on this, we investigate whether correlated noise can also enhance exploration in on-policy methods like PPO. We discovered that correlated noise for action selection improves learning performance and outperforms the currently popular uncorrelated white noise approach in on-policy methods. Unlike off-policy learning, where pink noise was found to be highly effective, we found that a colored noise, intermediate between white and pink, performed best for on-policy learning in PPO. We examined the impact of varying the amount of data collected for each update by modifying the number of parallel simulation environments for data collection and observed that with a larger number of parallel environments, more strongly correlated noise is beneficial. Due to the significant impact and ease of implementation, we recommend switching to correlated noise as the default noise source in PPO.

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16

Li, Guanghui, Yue Li, and Baojun Yang. "Seismic Exploration Random Noise on Land: Modeling and Application to Noise Suppression." IEEE Transactions on Geoscience and Remote Sensing 55, no. 8 (August 2017): 4668–81. http://dx.doi.org/10.1109/tgrs.2017.2697444.

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17

Sunderland, Andrew, Ray Lockwood, Li Ju, and David G. Blair. "Low-frequency rotational isolator for airborne exploration." GEOPHYSICS 82, no. 2 (March 1, 2017): E27—E30. http://dx.doi.org/10.1190/geo2015-0521.1.

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We have determined the performance of a passive rotational vibration isolator for a time-domain airborne electromagnetic (TDEM) receiver. The isolator uses neutrally buoyant flotation to provide very soft suspension and a very low resonant frequency of 0.065 Hz ± 0.005 Hz. One of the limitations of mapping deeper targets in areas of conductive overburden with TDEM systems is that low-frequency coil-vibration noise provides a lower bound to the transmitter base frequency (typically limited to 25 Hz). The purpose of this new isolator is to improve coil vibration related noise between 5 and 20 Hz to allow the transmitter base frequency to be reduced. A fixed-wing flight test determined that a receiver inside the new isolator had five times less rotational noise at 10 Hz than a current commercial system.

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18

INOUE, ATSUSHI. "MEAN-PLUS-NOISE FACTOR MODELS: AN EMPIRICAL EXPLORATION." Japanese Economic Review 63, no. 3 (July 23, 2012): 289–309. http://dx.doi.org/10.1111/j.1468-5876.2012.00582.x.

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19

Planès, Thomas, Anne Obermann, Verónica Antunes, and Matteo Lupi. "Ambient-noise tomography of the Greater Geneva Basin in a geothermal exploration context." Geophysical Journal International 220, no. 1 (October 10, 2019): 370–83. http://dx.doi.org/10.1093/gji/ggz457.

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SUMMARYThe Greater Geneva Basin is one of the key targets for geothermal exploration in Switzerland. Until recently, information about the subsurface structure of this region was mostly composed of well-logs, seismic reflection lines, and gravity measurements. As part of the current effort to further reduce subsurface uncertainty, and to test passive seismic methods for exploration purposes, we performed an ambient-noise tomography of the Greater Geneva Basin. We used ∼1.5 yr of continuous data collected on a temporary seismic network composed of 28 broad-band stations deployed within and around the basin. From the vertical component of the continuous noise recordings, we computed cross-correlation functions and retrieved Rayleigh-wave group-velocity dispersion curves. We then inverted the dispersion curves to obtain 2-D group-velocity maps and proceeded to a subsequent inversion step to retrieve a large-scale 3-D shear-wave velocity model of the basin. We discuss the retrieved features of the basin in the light of local geology, previously acquired geophysical data sets, and ongoing geothermal exploration. The Greater Geneva Basin is an ideal natural laboratory to test innovative geothermal exploration methods because of the substantial geophysical data sets available for comparison. While we point out the limits of ambient-noise exploration with sparse networks and current methodology, we also discuss possible ways to develop ambient-noise tomography as an affordable and efficient subsurface exploration method.

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20

Fu, Changmin, Qingyun Di, and Zhiguo An. "Application of the CSAMT method to groundwater exploration in a metropolitan environment." GEOPHYSICS 78, no. 5 (September 1, 2013): B201—B209. http://dx.doi.org/10.1190/geo2012-0533.1.

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Urban and suburban areas of large cities impose great challenges to geophysical surveys because of high-level ambient noise. This is particularly true for controlled source audio-frequency magnetotellurics (CSAMT) because the frequency range of the method overlaps those of many civil and industrial noises. Among the different types of noise sources, perhaps the most noticeable one is the 50-Hz power grid. We have successfully applied the CSAMT method for groundwater exploration in a suburban Beijing area where the ambient noise level is about three times as high as would normally be encountered. Several steps were taken in assuring quality use of the method, including careful survey design, adequate frequency range selection, and large transmitter dipole length and transmitter-receiver spacing. The next step was static effect removal through low-pass filtering and topography correction to remove any nonhorizontal component in the electric field measurement. Finally, a 1D inversion method was applied to construct an (apparent) resistivity cross section. The survey revealed a low-resistivity, water-bearing layer sandwiched between a surface cover layer and the bedrock. The available well data suggested that the depth of the bedrock was accurately mapped, to within about 2 m, at a depth level of 150–165 m.

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21

Hua, Gaoyang, Zhiqiu Huang, Jinyong Wang, Jian Xie, and Guohua Shen. "Exploration Strategy Improved DDPG for Lane Keeping Tasks in Autonomous Driving." Journal of Physics: Conference Series 2347, no. 1 (September 1, 2022): 012020. http://dx.doi.org/10.1088/1742-6596/2347/1/012020.

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Abstract We propose an Exploration Strategy Improved Deep Deterministic Policy Gradient algorithm called ESI-DDPG for lane keeping tasks in autonomous driving. The actor network in DDPG outputs a policy which is deterministic, so it is necessary to add noise to actions so that the autonomous vehicle can fully explore the environment and learn the optimal policy. However, the initial weight of exploration noise is large, which makes the autonomous vehicle carry out a lot of invalid exploration in the early training stage. Therefore, we combine the Stanley method to make a weighted correction to the exploration noise, so that the exploration of the vehicle tends to be in the right direction and the training efficiency can be improved. In addition, due to the good sample data obtained in the training process, the driving policy finally learned is better. We choose TORCS as the experiment platform and the results show that, compared with DDPG, TD3 and SAC, our proposed algorithm can learn the driving policy faster and the final policy has smaller trajectory error while driving.

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Tang, Xu, Ruiqi Du, Jingjing Ma, and Xiangrong Zhang. "Noisy Remote Sensing Scene Classification via Progressive Learning Based on Multiscale Information Exploration." Remote Sensing 15, no. 24 (December 12, 2023): 5706. http://dx.doi.org/10.3390/rs15245706.

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Remote sensing (RS) scene classification has always attracted much attention as an elemental and hot topic in the RS community. In recent years, many methods using convolutional neural networks (CNNs) and other advanced machine-learning techniques have been proposed. Their performance is excellent; however, they are disabled when there are noisy labels (i.e., RS scenes with incorrect labels), which is inevitable and common in practice. To address this problem, some specific RS classification models have been developed. Although feasible, their behavior is still limited by the complex contents of RS scenes, excessive noise filtering schemes, and intricate noise-tolerant learning strategies. To further enhance the RS classification results under the noisy scenario and overcome the above limitations, in this paper we propose a multiscale information exploration network (MIEN) and a progressive learning algorithm (PLA). MIEN involves two identical sub-networks whose goals are completing the classification and recognizing possible noisy RS scenes. In addition, we develop a transformer-assistive multiscale fusion module (TAMSFM) to enhance MIEN’s behavior in exploring the local, global, and multiscale contents within RS scenes. PLA encompasses a dual-view negative-learning (DNL) stage, an adaptively positive-learning (APL) stage, and an exhaustive soft-label-learning (ESL) stage. Their aim is to learn the relationships between RS scenes and irrelevant semantics, model the links between clean RS scenes and their labels, and generate reliable pseudo-labels. This way, MIEN can be thoroughly trained under the noisy scenario. We simulate noisy scenarios and conduct extensive experiments using three public RS scene data sets. The positive experimental results demonstrate that our MIEN and PLA can fully understand RS scenes and resist the negative influence of noisy samples.

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Liu, Di. "Research on Body Wave Extraction Technology of Mixed Active Source Ambient Noise." Journal of Physics: Conference Series 2651, no. 1 (December 1, 2023): 012149. http://dx.doi.org/10.1088/1742-6596/2651/1/012149.

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Abstract In complex surface environments, active source seismic exploration methods are difficult to stimulate and costly, while ambient noise detection technology is fast and convenient, with a little environmental damage and low cost. Therefore, it has aroused extensive research by scholars at home and abroad. At present, the use of passive source surface wave method for subsurface medium imaging has become mature. However, due to the low content and weak energy of the body wave signal in the actual ambient noise, it is particularly difficult to extract body waves using seismic interferometry. At the same time, body wave exploration has larger exploration depth and higher resolution. In this paper, we propose an ambient noise detection technology of mixed active sources, namely, imposing artificial seismic sources in the continuous ambient noise data acquisition process. We refer to the ambient noise of mixed active sources as "mixed ambient noise". It is found that the virtual refraction wave obtained by the ambient noise detection method of mixed active sources has high signal-to-noise ratio, strong anti-interference ability, and can obtain the real velocity information of the deeper subsurface medium, but cannot reflect the accurate time information. Supplementing the active source signal in the far field can obtain virtual refractions with higher signal-to-noise ratio, while effectively reducing the mixing phenomenon of surface wave and surface interference waves in the virtual common-shot record.

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24

Berger, Markus, and Ralf Bill. "Combining VR Visualization and Sonification for Immersive Exploration of Urban Noise Standards." Multimodal Technologies and Interaction 3, no. 2 (May 13, 2019): 34. http://dx.doi.org/10.3390/mti3020034.

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Urban traffic noise situations are usually visualized as conventional 2D maps or 3D scenes. These representations are indispensable tools to inform decision makers and citizens about issues of health, safety, and quality of life but require expert knowledge in order to be properly understood and put into context. The subjectivity of how we perceive noise as well as the inaccuracies in common noise calculation standards are rarely represented. We present a virtual reality application that seeks to offer an audiovisual glimpse into the background workings of one of these standards, by employing a multisensory, immersive analytics approach that allows users to interactively explore and listen to an approximate rendering of the data in the same environment that the noise simulation occurs in. In order for this approach to be useful, it should manage complicated noise level calculations in a real time environment and run on commodity low-cost VR hardware. In a prototypical implementation, we utilized simple VR interactions common to current mobile VR headsets and combined them with techniques from data visualization and sonification to allow users to explore road traffic noise in an immersive real-time urban environment. The noise levels were calculated over CityGML LoD2 building geometries, in accordance with Common Noise Assessment Methods in Europe (CNOSSOS-EU) sound propagation methods.

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Skurowski, Przemysław, and Magdalena Pawlyta. "On the Noise Complexity in an Optical Motion Capture Facility." Sensors 19, no. 20 (October 13, 2019): 4435. http://dx.doi.org/10.3390/s19204435.

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Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature. The works so far mostly employ a simple noise model, expressing the uncertainty as a simple variance. In the work, we demonstrate that it might be not sufficient and we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. Such a knowledge is especially important for using optical motion capture to calibrate other techniques, and for applications requiring very fine quality of recording. For the automated readout of the noise coefficients, we solve the multidimensional regression problem using sophisticated metaheuristics in the exploration-exploitation scheme. We identified in the laboratory the notable contribution to the overall noise from white noise and random walk, and a minor contribution from blue noise and flicker, whereas the violet noise is absent. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion. We analyzed also how the noise types scale with an increasing number of cameras. We had also the opportunity to observe the influence of camera failure on the overall performance.

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Zhou, Zi Ping, Yu Zhu, and Tian Hao Wang. "The Application of Cross-Correlation Algorithm in CSAMT Received Data Processing." Advanced Materials Research 989-994 (July 2014): 2278–82. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2278.

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Controlled source audio-frequency magnetotelluric method (CSAMT) is an effective frequency domain detecting method in metallic ore exploration. In view of the weak response of a deep target signal and the serious measuring environmental noise,this paper proposes a method to deal with the noise of the CSAMT data based on the theory of cross-correlation algorithm. Emission signal current waveform recorder is designed to record signal parameters.According to cross-correlation technology, this paper uses the characteristics that correlation between emission and received signals is strong, yet the correlation between emission signals and random noise is weak, to deal with emission signals and received signals by cross-correlation ,in order to filter out random noise and other jamming signals.Field exploration test data processing contrast results show that the method can suppress noise signal, and improve the CSAMT measuring accuracy.

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Chen, Yijie, Zhenwei Guo, and Dawei Gao. "Marine Controlled-Source Electromagnetic Data Denoising Method Using Symplectic Geometry Mode Decomposition." Journal of Marine Science and Engineering 11, no. 8 (August 11, 2023): 1578. http://dx.doi.org/10.3390/jmse11081578.

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The marine controlled-source electromagnetic (CSEM) method is an efficient tool for hydrocarbon exploration. The amplitudes of signals decay rapidly with the increasing offset, so signals are easily contaminated by various kinds of noise. A denoising method is critical to improve the data quality, but the diversity of noise makes denoising difficult. Specific frequency signals are transmitted for exploration requirements, and thus traditional filtering methods are not suitable. Symplectic geometry mode decomposition (SGMD), a new method to decompose signals, has an outstanding decomposition performance and noise robustness. Furthermore, it can reduce multiple types of noise by reconstructing the single components. In this study, we introduced SGMD to reduce the noise of marine CSEM data and improved the data quality significantly. The experiments show that SGMD is better than variational mode decomposition and the sym4 wavelet method.

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Stanley, John M., Somsri Sertsrivanit, and Peter J. Clark. "Magnetic Exploration Beneath a Near-Surface Magnetic Noise Source." Exploration Geophysics 23, no. 1-2 (March 1992): 323–26. http://dx.doi.org/10.1071/eg992323.

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KARINO, Izumi, Kazutoshi TANAKA, Ryuma NIIYAMA, and Yasuo KUNIYOSHI. "Efficient exploration with parameter noise in deep reinforcement learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 1A1—C15. http://dx.doi.org/10.1299/jsmermd.2018.1a1-c15.

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30

Good, Kenneth. "Exploration of alternative means to evaluate exterior noise loss." Journal of the Acoustical Society of America 124, no. 4 (October 2008): 2573. http://dx.doi.org/10.1121/1.4783124.

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31

Tollefsen, Dag, and Hanne Sagen. "Seismic exploration noise reduction in the Marginal Ice Zone." Journal of the Acoustical Society of America 136, no. 1 (July 2014): EL47—EL52. http://dx.doi.org/10.1121/1.4885547.

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Dümpelmann, Sonja. "Noise Landscape: A Spatial Exploration of Airports and Cities." Journal of Landscape Architecture 14, no. 1 (January 2, 2019): 92–93. http://dx.doi.org/10.1080/18626033.2019.1623553.

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Melo, João Guilherme Mourão, and Frank Sill Torres. "Exploration of Noise Impact on Integrated Bulk Current Sensors." Journal of Electronic Testing 32, no. 2 (March 16, 2016): 163–73. http://dx.doi.org/10.1007/s10836-016-5579-z.

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34

Ramadhan, Desando Anugrah, and Aries Dwi Indriyanti. "Procedural Content Generation pada Game World Exploration Sandbox Menggunakan Alogoritma Perlin Noise." Journal of Informatics and Computer Science (JINACS) 4, no. 01 (July 22, 2022): 86–91. http://dx.doi.org/10.26740/jinacs.v4n01.p86-91.

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Perkembangan teknologi membuat industri game semakin bervariasi dalam mengembangkan konten dalam game. Dengan menggunakan Procedural Content Generation proses penciptaan dunia dalam game menjadi lebih dinamis dan cepat. Dalam implementasi Procedural Content Generation (PCG) untuk membangun konten dalam game memerlukan alogitma yang berbeda pada setiap kontennya. Dalam pembuatan sebuah bentuk daratan dapat dilakukan dengan menggunakan algoritma Perlin Noise. Perlin noise adalah salah satu algoritma yang dapat digunakan untuk menghasilkan konten konten dalam game secara otomatis, salah satu manfaatnya adalah dengan menciptakan bentuk peta dunia secara otomatis. Pembuatan game dilakukan secara 2 dimensi menggunakan orthogonal view sebagai perspektif utama, dengan menghasilkan sebuah bentuk bukit, gua, dan awan yang dapat dijelajahi olek karakter pemain, proses pembentukan konten bukit, gua, dan awan menggunakan perlin noise dengan memperhatikan nilai jangkauan noise pada tiap konten. Dari hasil uji yang dilakuakan perlin noise dapat menghasilkan bentuk bukit dan gua yang terlihat natural, sedangkan untuk bentuk awan yang tercipta lebih cocok digunakan pada perspektif isometric view.

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Heinecke, Shelby, and Lev Reyzin. "Crowdsourced PAC Learning under Classification Noise." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (October 28, 2019): 41–49. http://dx.doi.org/10.1609/hcomp.v7i1.5279.

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In this paper, we analyze PAC learnability from labels produced by crowdsourcing. In our setting, unlabeled examples are drawn from a distribution and labels are crowdsourced from workers who operate under classification noise, each with their own noise parameter. We develop an end-to-end crowdsourced PAC learning algorithm that takes unlabeled data points as input and outputs a trained classifier. Our three-step algorithm incorporates majority voting, pure-exploration bandits, and noisy-PAC learning. We prove several guarantees on the number of tasks labeled by workers for PAC learning in this setting and show that our algorithm improves upon the baseline by reducing the total number of tasks given to workers. We demonstrate the robustness of our algorithm by exploring its application to additional realistic crowdsourcing settings.

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Fan, Li Hua, and Guo Huang Cai. "Exploration on Aerodynamic Noise Characteristics for Control Valve of Steam Turbine." Applied Mechanics and Materials 224 (November 2012): 395–400. http://dx.doi.org/10.4028/www.scientific.net/amm.224.395.

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The flow within the control valve is rather complicated, which is induced by a variety of pneumatic sound sources, including turbulent mixing, turbulence and boundary layer interaction, shock wave, etc. Through presenting the aerodynamic noise generation mechanism, we introduce several classic prediction methods for control valve of steam turbine, and then give the suppression and elimination advises of the aerodynamic noise of the control valve. It mainly involves two approaches, the direct method - sound source approach method and the indirect method–the acoustic path approachment method, and it can provide important guidance to control the noise level for steam turbine control valve.

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37

Ramirez Rochac, Juan F., Nian Zhang, Lara A. Thompson, and Tolessa Deksissa. "A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification." Computational Intelligence and Neuroscience 2021 (July 6, 2021): 1–17. http://dx.doi.org/10.1155/2021/9923491.

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Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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38

deAndrés-Galiana, Enrique J., Juan Luis Fernández-Martínez, Lucas Fernández-Brillet, Ana Cernea, and Andrzej Kloczkowski. "Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space." International Journal of Molecular Sciences 23, no. 21 (October 26, 2022): 12975. http://dx.doi.org/10.3390/ijms232112975.

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Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equations to have an imperfect solution. In addition, in the case of highly underdetermined parameterization, noise can be absorbed by the model, generating spurious solutions. This is a very undesirable situation that might lead to incorrect conclusions. We presented mathematical formalism based on the inverse problem theory combined with artificial intelligence methodologies to perform an enhanced sampling of noisy biomedical data to improve the finding of meaningful solutions. Random sampling methods fail for high-dimensional biomedical problems. Sampling methods such as smart model parameterizations, forward surrogates, and parallel computing are better suited for such problems. We applied these methods to several important biomedical problems, such as phenotype prediction and a problem related to predicting the effects of protein mutations, i.e., if a given single residue mutation is neutral or deleterious, causing a disease. We also applied these methods to de novo drug discovery and drug repositioning (repurposing) through the enhanced exploration of huge chemical space. The purpose of these novel methods that address the problem of noise and uncertainty in biomedical data is to find new therapeutic solutions, perform drug repurposing, and accelerate and optimize drug discovery, thus reestablishing homeostasis. Finding the right target, the right compound, and the right patient are the three bottlenecks to running successful clinical trials from the correct analysis of preclinical models. Artificial intelligence can provide a solution to these problems, considering that the character of the data restricts the quality of the prediction, as in any modeling procedure in data analysis. The use of simple and plain methodologies is crucial to tackling these important and challenging problems, particularly drug repositioning/repurposing in rare diseases.

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39

Sarvandani, Mohamadhasan Mohamadian, Emanuel Kästle, Lapo Boschi, Sylvie Leroy, and Mathilde Cannat. "Seismic Ambient Noise Imaging of a Quasi-Amagmatic Ultra-Slow Spreading Ridge." Remote Sensing 13, no. 14 (July 17, 2021): 2811. http://dx.doi.org/10.3390/rs13142811.

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Passive seismic interferometry has become very popular in recent years in exploration geophysics. However, it has not been widely applied in marine exploration. The purpose of this study is to investigate the internal structure of a quasi-amagmatic portion of the Southwest Indian Ridge by interferometry and to examine the performance and reliability of interferometry in marine explorations. To reach this goal, continuous vertical component recordings from 43 ocean bottom seismometers were analyzed. The recorded signals from 200 station pairs were cross-correlated in the frequency domain. The Bessel function method was applied to extract phase–velocity dispersion curves from the zero crossings of the cross-correlations. An average of all the dispersion curves was estimated in a period band 1–10 s and inverted through a conditional neighborhood algorithm which led to the final 1D S-wave velocity model of the crust and upper mantle. The obtained S-wave velocity model is in good agreement with previous geological and geophysical studies in the region and also in similar areas. We find an average crustal thickness of 7 km with a shallow layer of low shear velocities and high Vp/Vs ratio. We infer that the uppermost 2 km are highly porous and may be strongly serpentinized.

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40

Tang, Shibin, Shun Ding, Jiaming Li, Chun Zhu, and Leyu Cao. "An Improved Microseismic Signal Denoising Method of Rock Failure for Deeply Buried Energy Exploration." Energies 16, no. 5 (February 27, 2023): 2274. http://dx.doi.org/10.3390/en16052274.

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Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results. We proposed an improved microseismic denoising method based on different wavelet coefficients of useful signal and noise components. First, according to the selection of an appropriate wavelet threshold and threshold function, the useful signal part of original microseismic signal was decomposed many times and reconstructed to achieve denoising. Subsequently, synthetic signals of different types (microseismic noise, microseismic current, microseismic noise current) and with various signal-to-noise ratios (SNRs, −10~10) were used as test data. Evaluation indicators (mean absolute error μ and standard deviation error σ) were established to compare the denoising effect of different denoising methods and verify that the improved method is more effective than the traditional denoising methods (wavelet global threshold, empirical mode decomposition and wavelet transform–empirical mode decomposition). Finally, the proposed method was applied to actual field microseismic data. The results showed that the microseismic signal (with different types of noise) could be fully denoised (car honk, knock, current and construction noise, etc.) without losing useful signals (pure microseismic), suggesting that the proposed approach provides a good basis for the subsequent evaluation and classification of rock burst disasters.

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41

Zhang, Shan, and Yue Li. "Seismic exploration desert noise suppression based on complete ensemble empirical mode decomposition with adaptive noise." Journal of Applied Geophysics 180 (September 2020): 104055. http://dx.doi.org/10.1016/j.jappgeo.2020.104055.

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42

Qian, Hongfei, Xiangchun Wang, Xuelei Chen, and Zhu Yang. "Research on Noise Suppression Technology of Marine Optical Fiber Towed Streamer Seismic Data Based on ResUNet." Energies 15, no. 9 (May 5, 2022): 3362. http://dx.doi.org/10.3390/en15093362.

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Optical fiber seismic exploration technology has been widely used in marine oil and gas hydrate exploration due to its wide frequency band and high sensitivity. However, there are more types of noise in the collected data by optical fiber hydrophone than by a conventional piezoelectric seismic exploration system. Considering that the conventional denoising method is time-consuming, this paper proposes a convolutional neural network (CNN) and a ResUNet network based on deep learning to suppress the noises. ResUNet is improved on the basis of CNN; it is composed of a feature extraction part, a feature reconstruction part and a residual block. Both CNN and ResUNet networks achieved obvious denoising effects on optical fiber towed streamer seismic data and improved the signal-to-noise ratio of data effectively. The ResUNet network has better denoising effects than CNN, even better than conventional denoising methods. The ResUNet network can solve the problem of gradient disappearance caused by network deepening; it recovered edge data well, and it has high efficiency compared with conventional denoising methods. Two evaluation indexes, relative error (RE) and similarity structure degree (SSIM), were introduced to compare the denoising effect of the ResUNet network with that of CNN. The experimental results showed that the performance of the ResUNet network in these two aspects is better than that of CNN.

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43

Li, Suyi, Chunying Gu, Jiayu Yang, Yi Zhang, Shu Diao, and Yanju Ji. "A review of marine controlled-source electromagnetic data preprocessing technology." AIP Advances 12, no. 9 (September 1, 2022): 090701. http://dx.doi.org/10.1063/5.0090082.

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The marine controlled-source electromagnetic (MCSEM) method is an important geophysical technique for seabed resource exploration. During the MCSEM exploration process, distortion and noise will inevitably appear in the original data collected by the receiver due to various interferences and geological factors. The data preprocessing technology, therefore, is essential to improve the data quality for further inversion explanation of the seabed’s geological structure. Through tracing and analyzing the relevant literature published over two decades on MCSEM data preprocessing technology, including data correction and noise suppression methods, this Review first introduces the basic principle of MCSEM exploration and then analyzes the advances, limitations, and challenges of its current application. Ultimately, this Review discusses the prospects of this technology, providing a reference to relevant industries and researchers.

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44

GOBASHY, Mohamed, Maha ABDELAZEEM, and Mohamed ABDRABOU. "Minerals and ore deposits exploration using meta-heuristic based optimization on magnetic data." Contributions to Geophysics and Geodesy 50, no. 2 (July 29, 2020): 161–99. http://dx.doi.org/10.31577/congeo.2020.50.2.1.

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The difficulties in unravelling the tectonic structures, in some cases, prevent the understanding of the ore bodies' geometry, leading to mistakes in mineral exploration, mine planning, evaluation of ore deposits, and even mineral exploitation. For that reason, many geophysical techniques are introduced to reveal the type, dimension, and geometry of these structures. Among them, electric methods, self-potential, electromagnetic, magnetic and gravity methods. Global meta-heuristic technique using Whale Optimization Algorithm (WOA) has been utilized for assessing model parameters from magnetic anomalies due to a thin dike, a dipping dike, and a vertical fault like/shear zone geological structure. These structures are commonly associated with mineralization. This modern algorithm was firstly applied on a free-noise synthetic data and to a noisy data with three different levels of random noise to simulate natural and artificial anomaly disturbances. Good results obtained through the inversion of such synthetic examples prove the validity and applicability of our algorithm. Thereafter, the method is applied to real case studies taken from different ore mineralization resembling different geologic conditions. Data are taken from Canada, United States, Sweden, Peru, India, and Australia. The obtained results revealed good correlation with previous interpretations of these real field examples.

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45

Corciulo, Margherita, Philippe Roux, Michel Campillo, and Dominique Dubucq. "Instantaneous phase variation for seismic velocity monitoring from ambient noise at the exploration scale." GEOPHYSICS 77, no. 4 (July 1, 2012): Q37—Q44. http://dx.doi.org/10.1190/geo2011-0363.1.

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Recent studies in geophysics have investigated the use of seismic-noise correlations to measure weak-velocity variations from seismic-noise recordings. However, classically, the existing algorithms used to monitor medium velocities need extensive efforts in terms of computation time. This implies that these techniques are not appropriate at smaller scales in an exploration context when continuous data sets on dense arrays of sensors have to be analyzed. We applied a faster technique that allows the monitoring of small velocity changes from the instantaneous phase measurement of the seismic-noise crosscorrelation functions. We performed comparisons with existing algorithms using synthetic signals. The results we have obtained for a real data set show that the statistical distribution of the velocity-change estimates provides reliable measurements, despite the low signal-to-noise ratio obtained from the noise-correlation process.

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46

Wang, Jun Qiu, Jun Lin, Xiang Bo Gong, and Ran Zeng. "Study on Vibroseis Data Preprocessing Method of Metal Mining Seismic Exploration." Applied Mechanics and Materials 448-453 (October 2013): 3751–56. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3751.

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In order to improve the resolution of seismic exploration, this paper mainly studied the vibroseis data preprocessing method of metal mining seismic exploration. With the characteristics of vibroseis seismic data, we studied the correlation algorithm of detecting shot gather records, system analyzed the source and classification of noise in copper-nickel detection with Hydraulic sweep source in Jinchang, and chose the denoising method according to the characteristics of noise in the shot gather records. After preprocessing, the SNR of vibroseis seismic data is effectively improved, and then the resolution of seismic section is enhanced.

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47

Barclay, David R. "Passive acoustic exploration of the ocean." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A133. http://dx.doi.org/10.1121/10.0018410.

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Encoded in the spectral density, spatial variability, and directionality (spatial coherence) of the ambient sound field is information on the generation mechanisms of sound and the properties of the ocean propagation environment and its boundaries. Through field and observatory measurements, and analytical and computational models of the underwater sound field, a research program has been pursued that asks, “What can we learn about the ocean by listening?” Large acoustic data sets have been exploited to develop an estimate of the effective source level per unit area of surface generated noise. In complement, a methodology for precisely partitioning the sound field into ship generated and wind generated components by exploiting the vertical noise directionality has been demonstrated. Models of the spatial properties of wind driven and ship generated sound have further been used to estimate the geoacoustic properties of the seabed, the depth of mix layer, depth-averaged pH, and localize a source in bearing and azimuth using only a pair of vertically oriented omnidirectional hydrophones. An autonomous passive acoustic profiler, The Deep Acoustic Lander (DAL), recently made measurements of the ambient sound field from the surface to the bottom of the Challenger Deep, Mariana Trench, precisely determining the mixing of a locally- and distantly generated contributions to the sound field. Meanwhile, DAL measurements at the Endeavour hydrothermal vent field have revealed components of the sound field generated by vent activity.

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48

Bei, Cheng Xun, Dan Dan Wu, and Jian Xin Peng. "Exploration in Adjustment of AC Electric Bridge." Applied Mechanics and Materials 241-244 (December 2012): 623–26. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.623.

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One method called “low-order digit decided by high-order digit” is introduced to be applied to equilibrate AC electric bridge with two adjustable parameters frequently encountered in college physics experiment. The full verification of this method is discussed intuitionally. It is indicated that one can eliminate some disturbance of noise and improve measurement precision by using this method.

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49

Ikeda, Tatsunori, Takeshi Tsuji, Chisato Konishi, and Hideki Saito. "Spatial autocorrelation method for reliable measurements of two-station dispersion curves in heterogeneous ambient noise wavefields." Geophysical Journal International 226, no. 2 (April 16, 2021): 1130–47. http://dx.doi.org/10.1093/gji/ggab150.

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SUMMARY The microtremor survey method (MSM) is used to estimate S-wave velocity profiles from microtremors or ambient noise. Although array-based MSM analyses are usually used for shallow exploration purposes because of their robustness, the extraction of numerous phase-velocity dispersion curves by two-station microtremor analysis is attractive because those dispersion curves can be used to construct high-resolution phase-velocity maps by solving a least-squares problem. However, in exploration studies (>1 Hz), the reliability of two-station microtremor analysis can be affected by short data acquisition times and heterogeneous noise distributions mainly caused by anthropogenic noises. In this study, we propose a new approach to estimate surface wave dispersion curves between station pairs considering a heterogeneous ambient noise distribution based on the spatial autocorrelation method. We first estimated azimuthal variations of noise energy from the complex coherencies between all station pairs in a receiver array, and then estimated dispersion curves between station pairs. Our field example demonstrates that modelling the azimuthal noise energy distribution allows us to use not only the real parts of complex coherencies, but also the imaginary parts, which are usually neglected when assuming a homogeneous noise field. The simultaneous use of the real and imaginary parts of complex coherencies improves the reliability and continuity of phase-velocity estimations between station pairs. Because the stability of phase-velocity estimations depends on the azimuths between station pairs, we carefully selected between-station azimuths that produce stable phase velocities. Selected phase velocities at 8 Hz can be used to construct high-resolution phase-velocity maps with least-squares inversion. Because our approach does not require a regular receiver interval for two-station analysis, it allows for more flexible seismic array geometries. This is particularly important for MSM analyses in urban areas, where limited space is available to install seismic stations. We conclude that our proposed approach is effective in reconstructing high-resolution shallow structures in heterogeneous ambient noise fields.

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50

Chávez-García, Francisco José, and Toshiaki Yokoi. "High lateral resolution exploration using surface waves from noise records." Exploration Geophysics 47, no. 2 (June 2016): 123–32. http://dx.doi.org/10.1071/eg15020.

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