We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Q cc (m) test in conjunction with the statistical test-the χ 2 (m) distribution-and the cross-wavelet transform (XWT) and wavelet coherency. The Q cc (m) test and MF-DXA are used to identify and quantify the strength of long-range cross correlations between the porosities derived based on the NPHI, DT, and RHOB logs on the one hand, and the GR log that is indicative of the presence of clay minerals in reservoir rocks, on the other hand. The Q cc (m) test describes qualitatively the presence of such cross correlations between the porosity and GR logs. Analysis by the MF-DXA method also indicates that the various porosity logs, the GR log, and the cross correlations between them are multifractal, hence confirming the long-range nature of the correlations. The results are confirmed further by the XWT, and indicate that the porosities estimated based on the NPHI logs are only weakly, if at all, affected by the natural GR radioactivity of reservoir rock and are, therefore, most reliable. The effect of length scale on the correlations and cross correlations 123 446 H. Dashtian et al. was studied in detail. It is shown that such correlations exist at all length scales, and that they are of multifractal type that must be characterized by a spectrum of exponents.

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In this paper, we focus on the critical periods in the economy that are characterized by unusual and large fluctuations in macroeconomic indicators, like those measuring inflation and unemployment. We analyze U.S. data for 70 years from 1948 until 2018. To capture their fluctuation essence, we concentrate on the non-Gaussianity of their distributions. We investigate how the non-Gaussianity of these variables affects the coupling structure of them. We distinguish “regular” from “rare” events, in calculating the correlation coefficient, emphasizing that both cases might lead to a different response of the economy. Through the “multifractal random wall” model, one can see that the non-Gaussianity depends on time scales. The non-Gaussianity of unemployment is noticeable only for periods shorter than one year; for longer periods, the fluctuation distribution tends to a Gaussian behavior. In contrast, the non-Gaussianities of inflation fluctuations persist for all time scales. We observe through the “bivariate multifractal random walk” that despite the inflation features, the non-Gaussianity of the coupled structure is finite for scales less than one year, drops for periods larger than one year, and becomes small for scales greater than two years. This means that the footprint of the monetary policies intentionally influencing the inflation and unemployment couple is observed only for time horizons smaller than two years. Finally, to improve some understanding of the effect of rare events, we calculate high moments of the variables’ increments for various q orders and various time scales. The results show that coupling with high moments sharply increases during crises.

The aim here is to study the concept of pairing multifractality between time series possessing nonGaussian distributions. The increasing number of rare events creates "criticality". We show how the pairing between two series is affected by rare events, which we call "coupled criticality". A method is proposed for studying the coupled criticality born out of the interaction between two series, using the bivariate multifractal random walk (BiMRW). This method allows studying dependence of the coupled criticality on the criticality of each individual system. This approach is applied to data sets of gold & oil markets, and inflation & unemployment.

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