Gravity Anomaly and Basement Estimation Using Spectral Analysis

The stratigraphic and tectonic setting in the northwest part of Himalayan belt is complex and thrusted due to the collision of Indian plate and the Eurasian plate. During the past, the Himalaya is divided into four parts; these are Outer Himalaya, Lesser Himalaya, Greater or Higher Himalaya and Tethys Himalaya. The appearance of basement rocks played a significant role in the Himalayan periphery for stratigraphic, structural and tectonic movement. The deformation pattern of the crustal rocks causing the rise of basement rocks which constitutes an integral part of crustal configuration during the evolutionary stages of the Himalaya. In this study, an attempt has been made to estimate the basement depth configuration using spectral analysis and Euler deconvolution technique of gravity data in the northwest Himalaya region. The elevation increases continuously from 500 m to 5100 m in SW to NE direction, however, Bouguer gravity anomaly decreases continuously from -130 mGal to -390 mGal in SW to NE direction due to the isostaic adjustment. Gravity anomaly is very low near Harsil, Badrinath and Joshimath area and observed higher elevation due to the deep rooted basement. However, there are extrusion of crystalline basement in and around the Badrinath area due to the resettlement of geologic process which are overlaid to the top surface of the sedimentary layers. Euler deconvolution technique has been applied to detect the direct basement depth and results show a good correlation with the average depth of the spectral analysis and other works carried by different authors. Three gravity profiles are selected in appropriate places orienting SW-NE direction with a profile length of 160 km, 150 km and 140 km respectively in the study area for calculating the average depth of the basement rock. The average basement depth calculated is around 11.27 km using the spectral analysis technique and results are well correlated with the results of various workers. Euler deconvolution studies along the three selected profiles have been interpreted. It has been observed that there are more number of cluster points falling between depth ranges of 10 to 15 km, dipping in SW to NE direction. Euler’s study shows deep rooted connection near Main Frontal Thrust (MFT), Main Boundary Thrust (MBT), Main Central Thrust (MCT), Bearing Thrust (BT) and Vaikrita Thrust (VT) locations as per profile study. Based on these studies three geological models have been prepared along the profiles showing different tectonic resettlement and depth of crystalline basement. Crystalline rocks exposed at the surface may be due to uplift along the shear in the MCT zone by kinetic flow basically, Munsiayari Thrust (MT) and VT in the of NW-Himalaya region.

The multiwindow approach is a meaningful framework for nonparametric spectral estimation. It also encompasses several conventional methods as WOSA and frequency-averaged periodogram. Recently, some authors claimed that the Slepian windows of Thomson's method and other related optimal sets of windows show a better performance in terms of resolution, variance and leakage. In this paper, that claim is discussed by means of some simulation examples and by applying the various methods to speech recognition. In conclusion, frequency averaging of the periodogram is a computationally simple method that has a great flexibility for band specification and comparatively shows good performance. In fact, it is the spectral analysis technique most extensively employed for speech recognition.

Terrain methods on spectral analysis for paleoclimate interpretations: A novel visualization technique using python

Control systems are used in a number of different industrial sectors and critical infrastructures, including manufacturing, distribution, and transportation. Failure of these control systems because of malware or other faults would be catastrophic for critical infrastructure and would result in significant losses. Adverse operating conditions, such as those found in networked control systems, exacerbate the likelihood of vulnerability to malware attacks. The prospect of severe adversarial assaults such as buffer overflow, conventional faults, and especially stealthy Ladder Logic Bombs is a serious cause of concern in Industrial Control Systems (ICS) with novel attacks such as Struxnet. Decoupled at-distance Electromagnetic spectrum-based monitoring of faults and malware is more robust and can likely be integrated into Industrial Control System frameworks. Our failure analysis and Machine Learning (ML) modeling approach is fundamentally different from traditional methods in that we propose a Hybrid Quantum Artificial Intelligence-based Electromagnetic (EM) Spectral domain-based analysis approach that is completely decoupled from the controller processor and can predict PLC fault/malware conditions, including stealth Ladder Logic Bombs and buffer overflow attacks. Further, robust Supervisory Control Systems (SCSs) that interface with programmable logic controllers (PLCs) are essential components of Industrial Control Systems (ICSs) that handle critical infrastructure. Modern industrial control systems, on the other hand, are made up of densely linked Internet of Things (IoT) nodes. Because of their networked nature, supervisory control systems are also susceptible to widespread cyberattacks. As a result, in the case of a coordinated attack on IoT control systems, the dependability of Supervisory control decision-making procedures is jeopardized and need to be decoupled from the attacks. This work also develops a Fuzzy inference Engine-based supervisory monitoring technology based on the characteristics of the Hybrid Quantum AI-based analysis of the EM spectrum to reduce industrial plant downtime and false alarms by localizing adversarial attacks/faults, risk severity levels, and impacted nodes, as well as having dependability and malware immunity in networked Industrial control systems. The Hybrid QAI technique improved prediction accuracy by up to 88 percent when identifying different control system viruses and defects in 6-stage pipelined processors. The Fuzzy inference engine in the Mamdani Inference System architecture has been found to reduce the risk of false alarms and downtime.

While attenuation zones (dim-spots) have been visually noted on seismic sections associated with some hydrocarbon accumulations for almost twenty years (Taner and Sheriff, 1977; Anstey, 1977; Dobrin, 1984), early attempts at measuring attenuation in the laboratory and from seismic data met limited success. Work during the mid eighties set the stage for further investigation and development of these ideas, including discussions of a frequency dependent attenuation model by Terry Jones (1986) and experimental work by Bourbie et al. (1986) demonstrating that gas attenuates more P wave energy than water in rock pores. Factors affecting the spectral character of the seismic data generally fit into two categories (Dilay and Eastwood, 1995). The first is a lithological factor where the spectral character changes in response to time-thickness variations within a formation or a group of formations. The second is a petrophysical factor where spectral attributes can also be used to estimate the attenuation characteristics of a certain formation. It has been experimentally established that fluid-bearing porous rock formations attenuate seismic waves preferentially (i.e. higher frequencies within the seismic band are more severely attenuated than lower frequencies). Generally, gas attenuates seismic energy more than oil, and oil attenuates more than water. Spectral analysis was used to measure the attenuation of seismic frequencies beneath the Badak oil and gas field, located in the Sanga-Sanga PSC in East Kalimantan, Indonesia. This study utilized recently developed techniques for spectral and attenuation analysis (Maher, 1988). The spectral analysis method employed in the study contains several proprietary algorithms to overcome some limitations of previous methods for measuring attenuation from stacked seismic data. Comparison with AVO attributes indicates that spectral analysis provides additional information necessary to characterize the deeper reservoirs.

Spectral and cross-spectral analysis of uneven time series with the smoothed Lomb–Scargle periodogram and Monte Carlo evaluation of statistical significance

A computer program for advance heart rate Variability analyzed is presented using Matlab6.5 The program calculates all the commonly used time-frequency domain measures of HRV using parametric spectrum estimates. HRV power spectral analysis shows two main components which have been interpreted as different physiological rhythms. Spectral analysis technique usually based on either FFT or Autoregressive modeling requires the stationary of data and for this reason should be a applied on a short time window. This paper represent An effective algorithm for PSD estimate using AR model based parametric method, which gives better result then classical nonparametric method when data length of the available signal is relatively short i.e. shirt term record of ECG signal.(⇐ 5 min.)Parametric AR spectral estimates is commonly used to analysis the HRV signal association with the power spectrum bands related to simpatho-vagal activities. This study aims to indicate and to evaluate the performance of amulet, recursive, time varying AR identification of HRV power spectral density.

The spectral analysis techniques has been widely used to extract information about certain signals and the process in which those are generated. Ultrasonic (US) signal acquired from biological tissues are a particular case of that. Spectral analysis has been used to extract information from US signal related to biological tissue condition, mainly related with the internal scattering structure distribution and regularity. High-resolution spectral analysis techniques are applied to determine pathological characteristics that could indicate the presence of disease in the tissue. Some small changes in harmonics location could be drastically affected by the presence of noise in the analyzed signal. In this work, an evaluation of two parametric HR spectral analysis techniques is performed, first applied to signals acquired from a biological tissue phantom in a temperature range from 21°C to 45°C. A second evaluation was performed, considering simulated US signals, which were added with three different levels of SNR, and then they were analyzed with two parametric methods, which present variations in the error estimated for different noise level conditions. But the general behavior is a smaller error when noise decreases.

One of the paramount goals in petroleum exploration is the identification of the reservoir. The foremost objective of reservoir characterization is for prediction of reservoir precisely. Nowadays, geophysical characterization is used to characterize a reservoir; mainly, seismic data is being used for understanding the reservoir’s properties and internal structure. The reservoir can cause anomalies in the frequency of seismic signals. Over the past few decades, various transforms such as Fourier transform (FT) and wavelet transform (WT) have been used to extract the concealed attributes in the data. In the case of the Fourier transform (FT), it is the global transform that cannot reflect local specialty. This transform is the best applicable for stationary and linear data, but most of the geophysical data are non-stationary, nonlinear, and aperiodic in nature. Therefore, FT is good for spectral analysis but not fit for elucidating the temporal characteristics of the data. Wavelet transform (WT) overcomes the problem faced by Fourier transform as it is one of the efficient techniques for both spectral and temporal analysis of the data. The main problem with WT is that it does not work well with nonlinear data and suitable mother wavelet is needed to be assumed. This study focuses on highlighting the anomalies using the Hilbert–Huang Transform, which comprises two techniques, i.e., empirical mode decomposition (EMD) technique and Hilbert spectral analysis (HSA). EMD will decompose a signal into mono-components of frequency termed as intrinsic mode functions (IMFs). In this study, the EMD technique followed by HSA is applied to the data set which highlights the anomalies in the data set and makes facile to characterize a reservoir. It is difficult to characterize a reservoir because of varying properties such as porosity and permeability within small sections of the reservoir. This method shows its effectiveness in characterizing a reservoir.

Spectral analysis of EEG's by autoregressive decomposition of time series

Mapping sparse vegetation communities is routinely applied using techniques such as band ratios, the normalized difference vegetation index (NDVI) and spectral mixture analysis (SMA). The uncertainty of these vegetative mapping techniques is examined using the soil spectral variability defined by the spectral reference endmembers from three Landsat Thematic Mapper images: Owens Valley, California, USA; Gran Desierto, Sonora, Mexico, and Fayyum, Egypt. We find that band ratios and NDVI are not optimized for detecting vegetation given soil spectral variability. For SMA, the detection of sparse vegetation is optimized when it is detected as a residual component. Depending on the uncertainty model utilized from two to four fold improvement in mapping sparse vegetation is possible compared to NDVI and band ratios.

A large but conflicting literature exists that suggests that the scattering properties of the Earth in seismogenic areas may be temporally variable and that this variability could be correlated with the nucleation times of moderate to large earthquakes. To search for temporal variations in coda Q in the vicinity of an expected moderate earthquake nucleation region along the San Jacinto fault zone, pairs and clusters of earthquakes recorded by the Anza Seismic Network between 1982 and 1992 are examined. To minimize differences arising from path and source variability, only the most similar examples of microearthquake wave fields are intercompared. To assess coda Q differences between similar microearthquake signals, we search for systematic temporal changes in moving-window spectral amplitude ratios. For a single-scattering coda model with an envelope decay function of the form t−me−πft/Qc, the relationship between the natural log amplitude spectral ratio and ΔQ−1 = Q−1c2 − Q−1c1 is linear; i.e., ln r(f, t) = πftΔQ−1(f) + W(f), where W(f) is a time-independent relative source term. To obtain robust uncertainty estimates of ΔQ−1(f) and W(f), we utilize multi-taper spectral analysis coupled with a nonparametric Monte Carlo confidence interval estimation procedure. For the most similar events in the Anza region (coda cross-correlation values of approximately 0.7 for 16 sec of coda), we find that coda Q as a function of time is stable at a typical 1σ resolution of only between −25% and +50% of the reference coda Q value for the area, even in the best-constrained frequency bands. Comparison of similar microearthquake coda signals with both short- and long-temporal separations indicates that all of this variability can be reasonably attributed to random fluctuations in the coda that are driven by source variability and is not indicative of any systematic temporal variability in coda Q. To obtain estimates of relative coda Q that are more precise than this level, the spectral ratio analysis technique requires significantly more similar earthquakes than are produced by seismogenic processes in the Anza region. Such earthquakes have been observed to occur in other regions of the San Andreas Fault system and are analyzed in Antolik et al. (1996).

Petroleum prospects in deep water (300 to 3,000 m) in the U.S. Atlantic continental slope and rise occur in three major trends: shelf-edge/slope carbonate complex, Blake Plateau-Carolina Trough, and the upper continental rise. Data supporting the potential of these trends come from COST wells, DSDP core holes, USGS multichannel seismic profiles, single-channel seismic surveys, and aeromagnetic and gravity surveys. The carbonate complex parallels the present shelf-slope boundary in 300 to 2,000 m of water along much of the U.S. Atlantic margin and consists of a series of discontinuous barrier-reef structures ranging in thickness from 1,000 to perhaps 6,000 m. Associated with this complex, on the basis of seismic interpretations and COST B-3 well data, are thick, west-dipping, porous sandstone wedges, east-dipping forereeftalus wedges, and elongate anticlinal closures in strata overlying the reefs. The Blake Plateau-Carolina Trough trend contains a series of large salt domes and swells together with low-relief anticlines, fault traps, and patch reefs. Prospects in the upper rise include east-dipping fan deposits, buried slump deposits, channeled unconformities, fault traps, buried sea-mounts, and oceanic basement highs. Jurassic and Lower Cretaceous strata contain the most promising potential reservoirs in the carbonate complex and Blake Plateau trends; Cretaceous and Tertiary turbidites, contourites, and chalks may also be prospective in the upper rise. Low and discontinuous porosities and permeabilities may limit some reservoirs, although faults and fracture zones could provide local permeability enhancement. Hydrocarbon maturation depths in the rise are estimated to be greater than 2,000 m below sea floor and as much as 4,000 m in the other trends. Potential source rocks are Lower Cretaceous black shales as well as Jurassic shales and carbonates that may have been deposited in an oxygen minimum zone on a paleoslope. End_of_Article - Last_Page 1658------------

This research aims to detect spectral properties under thermal and current variations for power cables. Therefore, spectral diversities are exposed under current unbalances and different load conditions through the spectral analysis techniques. Also, huge load variations are easily detected from the current signals in the time-frequency plane using the short-time frequency analysis. Hence, this study presents the determination of the frequency characteristics and spectral similarities between the phase currents and thermal variations.

Despite the potential benefits of autonomous vehicles (AVs) of reducing human driver errors and enhancing traffic safety, a comprehensive evaluation of recent AV collision data reveals a concerning trend of rear-end collisions caused by following vehicles. This study aimed to address this issue by developing a methodology that identifies the relationship between driving patterns and the risk of collision between leading and following vehicles using spectral analysis. Specifically, we propose a process for computing three indices: reaction time, stimulus compliance index, and collision-risk aversion index. These indices consistently produced reliable results under various traffic conditions. Our findings align with existing research on the driving patterns of following vehicles. Given the consistency and robustness of these indices, they can be effectively utilized in advanced driver assistance systems or incorporated into AVs to assess the likelihood of collision risk posed by following vehicles and develop safer driving strategies accordingly.