Improved AIRS Temperature and Moisture Soundings with Local A Priori Information for the 1DVAR Method

AbstractA moving-window regression technique was developed for obtaining better a priori information for one-dimensional variational (1DVAR) physical retrievals. Using this technique regression coefficients were obtained for a specific geographical 10° × 10° window and for a given season. Then, regionally obtained regression retrievals over East Asia were used as a priori information for physical retrievals. To assess the effect of improved a priori information on the accuracy of the physical retrievals, error statistics of the physical retrievals from clear-sky Atmospheric Infrared Sounder (AIRS) measurements during 4 months of observation (March, June, September, and December of 2010) were compared; the results obtained using new a priori information were compared with those using a priori information from a global set of training data classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the moving-window regression method can successfully improve the accuracy of physical retrieval. For temperature, root-mean-square error (RMSE) improvements of 0.1–0.2 and 0.25–0.5 K were achieved over the 150–300- and 900–1000-hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5%–3.5% above the 300-hPa level and by 0.5%–1% within the 700–950-hPa layer.

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Substantiation of selection and dimension reduction of non-centered informative features of signals in information systems of short-range location

Information-measuring and Control Systems ◽

10.18127/j20700784-202008-02 ◽

2021 ◽

Author(s):

V.K. Khokhlov ◽

V.V. Glazkov ◽

A.K. Likhoedenko

Keyword(s):

Dimension Reduction ◽

Short Range ◽

A Priori ◽

Regression Coefficients ◽

A Priori Information ◽

Network Algorithms ◽

Informative Parameters ◽

Regression Algorithms ◽

Priori Information ◽

Detection And Recognition

In this paper, we consider the issues of selection of informative features, dimension reduction of feature vectors in regression algorithms of detection and recognition of signals and interference, as well as the issues of obtaining informative features using neural network algorithms with ill-conditioned data. The problem is considered in relation to the short-range location, with large dynamic ranges of informative features and small decision intervals, when it is impossible to estimate mathematical expectations, that is, it is impossible to use adaptive algorithms. Regression algorithms for processing non-centered random signals are presented, with a priori unknown mathematical expectations of informative parameters, which consider the specificity of short-range location and use a priori information about the initial regression characteristics of informative features – multiple initial regression coefficients. Unlike it is in traditional regression analysis, the coefficients are determined through the elements of the matrices that are inverse to the matrices of the initial correlation moments. In regression algorithms, it is necessary to calculate the square error of multiple initial regression representations. The residual mean of squares of the initial regression representations are used to justify the methods for selection and dimension reduction of informative features of signals in the problems of detection and recognition of signals and interference. We give examples of application of the proposed methods for the problems of detection and recognition of a helicopter and airplane by acoustic signals when processing histograms of the distributions of the durations of intervals between zeros, samples of envelopes and samples of local extrema of the power spectral density. Good separability of the {airplane} and {helicopter} classes in the space of non-centered parameters of signals (features) is shown. The issue of obtaining regression statistical characteristics with illconditioned data is considered. If the matrices of the correlation moments of the informative features of signals and noise are illconditioned, it becomes impossible to obtain a priori information about the multiple initial regression coefficients. The possibility of using neural network algorithms to obtain estimates of the residual mean squares of regression representations and multiple initial regression coefficients through the weight coefficients on the inputs of neurons with ill-conditioned data is shown. The results can be used in short-range location systems with a large dynamic range of non-centered informative parameters, when it is not possible to estimate the mathematical expectations of the signal parameters due to the limited observation interval.

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Deep learning spectral enhancement considering features of seismic field data

Geophysics ◽

10.1190/geo2020-0017.1 ◽

2021 ◽

pp. 1-60

Author(s):

Yonggyu Choi ◽

Yeonghwa Jo ◽

Soon Jee Seol ◽

Joongmoo Byun ◽

Young Kim

Keyword(s):

Machine Learning ◽

Seismic Data ◽

Input Data ◽

Field Data ◽

A Priori ◽

Resolution Enhancement ◽

Training Data ◽

A Priori Information ◽

Spectral Enhancement ◽

Priori Information

The resolution of seismic data dictates the ability to identify individual features or details in a given image, and the temporal (vertical) resolution is a function of the frequency content of a signal. To improve thin-bed resolution, broadening of the frequency spectrum is required; this has been one of the major objectives in seismic data processing. Recently, many researchers have proposed machine learning based resolution enhancement and showed their applicability. However, since the performance of machine learning depends on what the model has learned, output from training data with features different from the target field data may be poor. Thus, we present a machine learning based spectral enhancement technique considering features of seismic field data. We used a convolutional U-Net model, which preserves the temporal connectivity and resolution of the input data, and generated numerous synthetic input traces and their corresponding spectrally broadened traces for training the model. A priori information from field data, such as the estimated source wavelet and reflectivity distribution, was considered when generating the input data for complementing the field features. Using synthetic tests and field post-stack seismic data examples, we showed that the trained model with a priori information outperforms the models trained without a priori information in terms of the accuracy of enhanced signals. In addition, our new spectral enhancing method was verified through the application to the high-cut filtered data and its promising features were presented through the comparison with well log data.

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AN APPROACH TO PROCESSING ELECTROTOMOGRAPHY DATA FOR ESTIMATING THE VOLUMES OF SUBSTANCES IN TAILINGS

Interexpo GEO-Siberia ◽

10.33764/2618-981x-2021-2-2-62-66 ◽

2021 ◽

Vol 2 (2) ◽

pp. 62-66

Author(s):

Yuri Grigorievich Karin ◽

Natalia Victorovna Yurkevich

Keyword(s):

A Priori ◽

Software Tools ◽

Two Dimensional ◽

A Priori Information ◽

Electrical Tomography ◽

One Dimensional ◽

Model Section ◽

Priori Information ◽

Tomography Data

Methodical recommendations for processing the data of electrotomography are given. Taking into account a priori information, in particular the results of the study of pits, can be carried out by carrying out a limited inversion of the ET data, while it is possible to limit either the resistance of some layers of the model or the position of the boundaries of some layers [1]. In the software used for processing electrical tomography data Res2dinv [2], it is possible to set the boundary of the assumed layer or to limit the resistance of the model section (to introduce local inhomogeneities with a given resistance). But it is difficult to fix the resistance of a particular layer with the available software tools. The proposed approach makes it possible to take into account the parameters of the model built from the pitting data using a preliminary one-dimensional inversion of the electrical tomography data in the Ip2win [1] program, followed by the export of the obtained assumed layer boundaries to the Res2dinv software for carrying out a limited two-dimensional inversion.

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