Error estimates for inverse modeling schemes using seismic traveltimes

Geophysics ◽  
1981 ◽  
Vol 46 (9) ◽  
pp. 1227-1234 ◽  
Author(s):  
Bjørn Ursin
Keyword(s):  
Covariance Matrix ◽  
Impulse Response ◽  
Additive White Gaussian Noise ◽  
Information Matrix ◽  
Measurement Data ◽  
Confidence Regions ◽  
Measurement Systems ◽  
Estimated Parameters ◽  
Layer Velocity ◽  
Seismic Measurement

Different methods for estimating parameters in a layered geologic model are discussed. Traveltime parameters, estimated from seismic data, are used to estimate the layer parameters defining the velocity function in each layer and the interfaces between the layers. Seismic measurement data are assumed to consist of a sum of nonoverlapping reflected pulses and additive white Gaussian noise. An estimate of the covariance of the traveltime parameters is then given by the inverse of Fischer’s information matrix. It is shown how the information matrix can be computed theoretically or directly from data. Expressions for the covariance matrix of the layer parameters are given. The results can be used to compute confidence regions for the estimated parameters. Optimal seismic measurement systems are discussed, resulting in a criterion for designing an optimal seismic pulse: The energy of the derivative of the received signal (the source pulse convolved with the impulse response of the earth and the impulse response of the instruments) should be maximized. Parameter estimation in a horizontally layered model is considered as an example, and the covariance matrix of the layer velocity and layer thickness is given explicitly.

scholarly journals Adaptive Stochastic Filtration Based on the Estimation of the Covariance Matrix of Measurement Noises Using Irregular Accurate Observations

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 10
Author(s):  
Sergey Sokolov ◽  
Arthur Novikov ◽  
Marianna Polyakova
Keyword(s):  
Covariance Matrix ◽  
Adaptive Estimation ◽  
Observation System ◽  
Mobile Object ◽  
New Approach ◽  
Measurement Systems ◽  
System Parameters ◽  
Traditional Scheme ◽  
Adaptive Filtration ◽  
Measurement Noises

In measurement systems operating under various disturbances the probabilistic characteristics of measurement noises are usually known approximately. To improve the observation accuracy, a new approach to the Kalman’s filter adaptation is proposed. In this approach, the Covariance Matrix of Measurement Noises (CMMN) is estimated by accurate measurements detected irregularly by the mobile object observation system (from radiofrequency identifiers, etalon reference, fixed points etc.). The problem of adaptive estimation of the observer’s noises covariance matrix in the Kalman filter is solved analytically for two cases: mutual noises correlation, and its absence. The numerical example for adaptive filtration of complexing navigation system parameters of a mobile object using irregular accurate measurements is given to illustrate the effectiveness of the proposed algorithm. Coordinate estimating errors have changed in comparison with the traditional scheme from 100 m to 2 m in latitude, and from 200 m to 1.5 m in longitude.

scholarly journals Methane Emission during Gas and Rock Outburst on the Basis of the Unipore Model

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1999 ◽  
Author(s):  
Skoczylas Norbert ◽  
Anna Pajdak ◽  
Katarzyna Kozieł ◽  
Leticia Teixeira Palla Braga
Keyword(s):  
Coal Mine ◽  
Effective Diffusion ◽  
Measurement Data ◽  
Sorption Isotherms ◽  
Sieve Analysis ◽  
Measurement Systems ◽  
Upper Silesian Coal Basin ◽  
Tectonic Disturbances ◽  
Concentration Changes ◽  
Time Courses

The goal of this paper is to analyze the phenomenon of gas emission during a methane and coal outburst based on the unipore Crank diffusion model for spherical grains and plane sheets. Two occurrences in the Upper Silesian Coal Basin were analyzed: an outburst in a Zofiówka coal mine in 2005 and an outburst in a Budryk coal mine in 2012. Those two outbursts differed considerably. The first one was connected with an unidentified tectonic disturbance in the form of a triple, interlocking fault, and the other one is an example of an outburst in an area free from tectonic disturbances. The model analysis required laboratory tests in order to determine the sorption properties of coals from post-outburst masses. Sorption isotherms and the values of the effective diffusion coefficient were specified. The post-outburst masses were subjected to sieve analysis and the grain composition curves were plotted. The researchers also used the measurement data provided by proper mine services, such as the methane content, the volume of post-outburst masses, and the time courses of CH4 concentration changes in excavations. They were recorded by methane measurement systems in the mines.

Evolution of the Role of Measurement Systems in Industrial Decision Support

2016 ◽  
pp. 560-580 ◽  
Author(s):  
Miroslaw Staron ◽  
Wilhelm Meding ◽  
Kent Niesel ◽  
Ola Söder
Keyword(s):  
Decision Support ◽  
Data Collection ◽  
Decision Support Systems ◽  
Information Needs ◽  
Support Systems ◽  
Measurement Data ◽  
Automated Measurement ◽  
Measurement Systems ◽  
Distributed Decision

Measurement data can be used for decision support in multiple ways – from one-time, manual data collection/presentation (reporting) through flexible business intelligence solutions to online, automated measurement systems. In centralized organizations, the measurement data is often collected through reporting, but the trends in modern organizations with empowered teams, globalized development, and needs to monitor continuously longer supply chains requires shift in the design and use of measurement systems. In this chapter, we present a study of evolving measurement systems at three companies with global businesses – Ericsson, Volvo Cars, and Axis Communications. The results of the study include the identification of the timeline of the evolution, distinct generations of measurement systems and information needs in the different phases of the evolution. The experiences show how to evolve centralized decision support systems to support global and distributed decision support.

Parameter Estimation of Co-Channel AIS Signal Using Ambiguity Function

2014 ◽  
Vol 644-650 ◽  
pp. 4035-4039
Author(s):  
Hao Su Zhou ◽  
Jian Xin Wang
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Reference Signal ◽  
Two Dimensions ◽  
Ambiguity Function ◽  
Weak Signal ◽  
Strong Signal ◽  
Estimated Parameters ◽  
Simulation Results

A new data-aided algorithm for parameter estimation of the co-channel AIS signal transmitted over the additive white Gaussian noise channel is proposed in this paper. The co-channel signal consists of a strong signal with high power and a weak signal with low power. The parameters of the strong signal are estimated by searching the ambiguity function of the co-channel signal in two dimensions. A reference signal is therefore reconstructed with the estimated parameters and the aided data. By removing the ambiguity function of the reconstructed reference signal from that of the original co-channel signal, a new co-channel signal ambiguity function is obtained, from which the parameters of the weak signal are estimated. The simulation results illustrate that the proposed algorithm can estimate the parameters of the co-channel AIS signal effectively.

Estimation of Uncertain Parameters for Thermal and Fluid Analysis in Engine Room of Construction Machinery Using Data Assimilation

2016 ◽  
Author(s):  
Kazuya Kusano ◽  
Hironobu Yamakawa ◽  
Kunihiko Ikeda
Keyword(s):  
Data Assimilation ◽  
Heat Exchangers ◽  
Stokes Equations ◽  
Measurement Data ◽  
Total Heat ◽  
Hydraulic Excavator ◽  
Uncertain Parameters ◽  
Fluid Analysis ◽  
Estimated Parameters ◽  
Engine Room

Recently, the cooling system of hydraulic excavator is often designed using the thermal and fluid analysis to improve the cooling performance. The reliability of the analysis results is important, since it directly influences on the efficiency of development. In the present study, the uncertain parameters were estimated using the data assimilation method to increase the reliability of the thermal and fluid analysis in an engine room of a hydraulic excavator. The ensemble Kalman filter (EnKF) was adapted as a data assimilation method, and the thermal and fluid analysis was conducted with the three-dimensional steady simulation based on the Reynolds-average Navier-Stokes equations. The estimated parameters were set to the total heat quantities released by heat exchangers and the flow rates of the coolants. The total heat quantity is a parameter used for the heat release calculation of a heat exchanger, and the flow rate of a coolant is specified at the inlet boundary. As measurement data, temperatures of coolants which were measured at the upstream and downstream of the heat exchangers were used. Initial parameters were generated by setting parameter values in a random manner. The simulation using estimated parameters successfully predicted temperatures at the heat exchangers, where the maximum error was 3K. In addition, the reductions of the standard deviations of the uncertain parameters were confirmed. That means the reliability of the simulation was increased.

scholarly journals Pemodelan Non-uniform Coded-Modulation pada Kanal Akustik Bawah Air di Lingkungan Perairan Dangkal

2019 ◽  
Vol 9 (1) ◽  
pp. 38
Author(s):  
Sholihah Ayu Wulandari ◽  
Tri Budi Santoso ◽  
I Gede Puja Astawa ◽  
Muhamad Milchan
Keyword(s):  
Error Rate ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Measurement Data ◽  
Coded Modulation ◽  
Bch Code ◽  
Ieee 802.11A ◽  
Shift Keying ◽  
Binary Information

In this paper, presented an OFDM performance evaluation with the Non-uniform Coded-Modulation in the underwater acoustic channel in shallow water. A row of binary information is encoded by BCH code (7.4) for error correction and combined with Non-uniform modulation which is the result of modification of the subcarrier arrangement of the OFDM standard IEEE 802.11a. Modeling uses 52 subcarriers consisting of 4 pilots and 48 subcarrier data which are divided into three parts, i.e.: 24 subcarrier data with 16-Quadrature Amplitude Modulation (16-QAM) modulation, 12 subcarrier data with Quadrature phase-shift keying (QPSK) modulation and 12 other data subcarriers with Binary key-shift keying (BPSK) modulation. The channel type used describes the Additive White Gaussian Noise (AWGN) condition and is the result of measurement data. The analysis is done in terms of Signal-to-Noise-Ratio (SNR) and Bit Error Rate (BER) show that the value of the error rate of 0.001, modulation of BPSK, QPSK, 16-QAM, and Non-uniform modulation required the power each 5 dB, 8.5 dB, 10.3 dB, and 7.9 dB. However, the proposed system is able to suppress the required power up to 6 dB. The proposed system also shows better performance than fixed modulation and Non-uniform Modulation, which in this case with low power to achieve the same error rate. In addition, the proposed system has a coding gain of 1.9 dB compared to a non-uniform modulation system. Real testing is also done with measurement data at Mangrove estuary, Surabaya. The results show performance similar to simulations performed on Gaussian noise channels.

scholarly journals A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

2020 ◽  
Author(s):  
Jin Lu ◽  
Ming Huang ◽  
Jingjing Yang
Keyword(s):  
Covariance Matrix ◽  
Fading Channels ◽  
Stochastic Resonance ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Additive White Gaussian Noise ◽  
Ratio Test ◽  
Sample Covariance ◽  
Simulation Results ◽  
Sensing Performance

Abstract Cognitive radio (CR) is a dynamic spectrum sharing technology designed to reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

scholarly journals A Covariance Matrix-based Spectrum Sensing Technology Exploiting Stochastic Resonance and Filters

2020 ◽  
Author(s):  
Jin Lu ◽  
Ming Huang ◽  
Jingjing Yang
Keyword(s):  
Covariance Matrix ◽  
Fading Channels ◽  
Stochastic Resonance ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Additive White Gaussian Noise ◽  
Ratio Test ◽  
Sample Covariance ◽  
Simulation Results ◽  
Sensing Performance

Abstract Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low signal-to-noise ratio (SNR) is still a challenge in CR systems. This paper proposes a novel covariance matrix-based spectrum sensing method by using stochastic resonance (SR) and filters. SR is implemented to enforce the detection signal of multiple antennas in low SNR conditions. The filters are equipped in the receiver to reduce the interference segment of noise frequency. Then, two test statistics computed by the likelihood ratio test (LRT) or the maximum eigenvalues detector (MED) are constructed by the sample covariance matrix of the processed signals. The simulation results exhibit the spectrum sensing performance of the proposed algorithms under various channel conditions, namely, additive white Gaussian noise (AWGN) and Rayleigh fading channels. The energy detector (ED) is also compared with LRT and MED. The simulation results demonstrate that SR and filter implementation can achieve a considerable improvement in spectrum sensing performance under a strong noise background.

scholarly journals The Use of Decomposition SVD to Approximate a Surface

2017 ◽  
Author(s):  
Edward Preweda
Keyword(s):  
Least Squares ◽  
Covariance Matrix ◽  
Classical Method ◽  
Interval Estimation ◽  
Point Of View ◽  
Method Of Least Squares ◽  
Practical Point ◽  
Estimated Parameters ◽  
Main Emphasis ◽  
Special Value

This paper describes the procedure of determining the parameters of the approximating function of the surface using a distribution of special value. From a practical point of view, an important issue is to determine the covariance matrix of the estimated parameters. Interval estimation was carried out and a methodology to obtain an optimal equation of approximating surface was presented. The main emphasis was given to the elimination of these parameters functions that generate unnecessary disturbance. The results obtained using the approximate decomposition SVD were compared with those obtained by a classical method of least squares. They have been used a variety of software, including software written by author, also packages Matlab and Statistica. The main purpose of discussion is to solve sample tasks for better understanding and expand the use of decomposition SVD in geodetic issues.

Sign in / Sign up

Export Citation Format

Share Document