# Prony Filtering Of Seismic Data

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### Statistical Digital Signal Processing And Modeling

image and data compression, etc. Engineers who develop DSP applications today, and in the future, will need to address many implementation issues including mapping algorithms to computational structures, computational efficiency, power dissipation, the effects of finite precision arithmetic,

### Digital Signal Processing For Measurement Systems Theory And

which introduces readers to periodic and non-periodic signals. The second part is devoted to filtering, which is an important and commonly used application. The third part addresses more advanced topics, including the analysis of real-world non-stationary signals and data, e.g. structural fatigue, earthquakes, electro-encephalograms, birdsong, etc.

### Azimuthal offset‐dependent attributes applied to fracture

(Lynn et al., 1995). Seismic data have wider spatial coverage than well data; thus, fracture detection using seismic data be- comes important for practicality. Shear-wave splitting is very sensitive to fracture orientation and density. However, the high cost of acquiring multicomponent data makes this method ex- pensive to apply on a regular

### Analysis of the Prony method resolution with spatial

strong interference [1]. For example, the Prony method is widely used for solving problems in such areas of science and technology as seismic, medicine, radiolocation [2-4]. In presented study, we carried out a comparative analysis of the Prony method and spatial filtering methods having high [5] and ultra-high resolution [6].

### A new adaptive algorithm for automated feature extraction in

Jul 06, 2019 decomposition to seismic random noise reduction Wei Liu, Siyuan Cao and Zhiming Wang-A quantified self-adaptive filtering method: effective IMFs selection based on CEEMD Hongmei Zheng, Chunlei Dang, Senmao Gu et al.-Recent citations A combined method for instantaneous frequency identification in low frequency structures Jingliang Liu et al-

### Seismic data decomposition into spectral components using

possible decompositions applicable to seismic data. A fundamental characteristic of seismic data is non-stationarity. In 1-D (time dimension), seismic data are nonstationary because of wave-attenuation effects. In 2-D and 3-D (time and space dimensions), non-stationarity is manifested by variable slopes of seismic events.

### Development of a New Method of Crack Modeling and Prediction

Median Filtering: Median filtering operation replaces a pixel by the median of all the neighboring pixels in an NxN window (N is always odd number). This is a nonlinear process and it reduces impulsive noise from an image without distorting the edges too much. 3. Illumination Equalization: This involve the application of Global-Local Adaptive

### Signal Processing: A Mathematical Approach

Charles L. Byrne Department of Mathematical Sciences University of Massachusetts Lowell Book Introduction and Sample Chapter ISBN 978-1-4822-4184-6 CRC Press

### Spring 2017 Newsletter - University of Texas at Austin

lapse seismic images using spectral decomposition. Time-lapse time shifts are difficult to measure from seismic data in the presence of tuning effects caused by low frequencies or thin beds. They can be measured more accurately by first decomposing time-lapse seismic images into discrete frequency components using the local time-frequency

### Potential of Prony and Phase Decompositions for Reservoir

Prony Decomposition (Prony, 1795) is a unique signal decomposition that takes into account seismic wave attenuation/damping (Q-1) as well as phase, frequency and time. The Q-factor retrieved from the seismic data through Prony Decomposition can thus be used to map seismic attenuations. Since seismic waves

### Acoustic properties of a crack containing magmatic or

Sassa [1935], it has been well recognized that seismic signals associated with volcanic activity are character- ized by a wide variety of signatures. Attempts at clas- sifying these signals have been based on their wave- form signatures [e.g., Minakami, 1974]. Although de- tailed classification is possible, these types of signals

### Statistical Digital Signal Processing And Modeling Solution

Advanced Digital Signal Processing of Seismic Data This book is intended as a manual on modern advanced statistical methods for signal processing. The objectives of signal processing are the analysis, synthesis, and modification of signals measured from different natural phenomena, including engineering applications as well.

### ДНІПРОВСЬКИЙ НАЦІОНАЛЬНИЙ УНІВЕРСИТЕТ ІМЕНІ ОЛЕСЯ ГОНЧАРА

This paper reviews Prony s method in relation to signal filtering and approximation, V. Prony filtering of seismic data. ActaGeophys. 2015; 63, 652-678, doi:10

### PRONY FILTRATION OF SEISMIC DATA: THEORETICAL BACKGROUND

the Prony decomposition and selection of components of this decomposition in order to obtain images of seismic data corresponding to a narrow band of frequencies. Therefore, the method can be called Prony filtration. Its closest analogue is a bandpass filtration. However, Prony filtration method provides higher resolution of

### Analysis of echo-pulse images of layered structures. The

The equipment, work methods, processing and interpretation of seismic data are described. Considered the use of seismic prospecting in solving geological problems, the organization and planning of work. A resource [21] provides complete coverage of signal modeling, optimum filtering, ratios of the acoustic impedances, and adaptive filtering.

### Array Processing Underground - UDC

Prototype Seismic Mine Detection System Interaction of Rayleigh wave with mines can be used for detection and localization of mines W. R. Scott Jr., J. S. Martin, and G. D. Larson, Experimental model for a seismic landmine detection system, IEEE Trans. Geoscience and Remote Sensing, vol. 39, pp. 1155 1164, June 2001.

### TOPICAL MEETING ON SIGNAL RECOVERY AND SYNTHESIS WITH

ing of Noisy Data, C. B. Chittineni, Conoco, Inc. Frequency-domain optimal inverse filtering algorithms are developed and are applied for seismic deconvolu-tion. A blind restoration technique is formulated, and the simulation results are presented.

### ANALYSIS OF ALGEBRAIC ESTIMATES OF ELECTROCARDIOGRAPHIC AND

and more difficult to carry out adequate data analysis and generalization of the accrued volume of information. Problems occur with new signals and the information stored inside; these problems are particularly acute in the areas where signals, such as biomedical, seismic, ultrasonic and other sources, are

### Spectrum estimation using frequency shifting and decimation

starting point for classical Prony's method [22]. Unfortunately, H is an ill-conditioned matrix, and solving (3) for q could be very sensitive to noise in the data. Moreover, obtaining zi as the roots of q(z) is also a challenging task to perform when (1) is perturbed by noise. More robust approaches have been proposed by exploiting the

### Fall 2017 Newsletter

G. Wu, S. Fomel, and Y. Chen, 2017, Data-driven time-frequency analysis of seismic data using nonstationary Prony method: Geophysical Prospecting. Z. Xue, S. Fomel, and J. Sun, 2017, Increasing resolution of reverse-time migration using time-shift gathers: Geophysical Prospecting. Published 2017

### Name (Nombre) Contact Information (Información de Universidad

processing software (noise filtering, spectral balancing), designed and implemented 3-D graphics of seismic signals. 1983 - 1984: Seiscom Delta United, Houston, Texas. Software Engineer, Responsibilities: Designed and implemented geophysical signal modeling software (Maxwell equations). Professional Participation (IEEE)