Estimating the Error in Solving the Inverse VES Problem for Precision Investigations of Time Variations in a Geoelectric Section with a Strong Seasonal Effectстатья
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Аннотация:Abstract—As part of studies on the search for earthquake precursors, the authors have conducted an experiment on long-term precision monitoring of variations in the resistivity of the Earth’s crust in a highly seismicregion of Tajikistan. The primary data of this experiment can be considered a special type of VES profile, inwhich, instead of a linear coordinate, the sounding date changes from marker to marker. When processingprecision monitoring data, it is necessary to solve the inverse VES problem with the highest possible accuracy.VES curve inversion programs commonly used in electric exploration do not allow this. The authors havepreviously developed a special method for regularizing the residual functional, which suppresses the effect ofresistivity buildup, due to which the error in reconstructing the resistivity of rocks for profiles with a strongseasonal variation in resistivity is reduced by an order of magnitude. However, in some cases, the regularizedalgorithm strongly biases estimation of the amplitude of the seasonal resistivity variation in the lower layersof the section. In this paper, the operation of the proposed algorithm is tested in detail for nine model profilessimulating a real geoelectric section. The considered profiles differed in the characteristics of the seasonalvariability of resistivity in the lower layers of the section (the phase and amplitude of seasonal effects varied).It is shown that resistivity buildup is effectively suppressed in all cases. For each model profile, the error insolving the inverse problem is estimated. The effect of a biased estimate of the amplitude of seasonal variationis studied. It is shown that in most cases, analysis of the solution makes it possible to reveal the presence ofsuch distortions and qualitatively assess their character. It is also shown that for profile options supposedlyclosest to the experimental profile, the estimates have minimal bias. For all profiles, the ratio of the averageand maximum errors in calculating the resistivity in different layers to the residual in the solution to theinverse problem was evaluated. This makes it possible to evaluate the actual error of the reconstructed resistivity values knowing only the selection residual. The paper also studied the possible effect of increasing theaccuracy in solving the inverse problem in the case of preliminary decomposition of the apparent resistivitycurves into seasonal and flicker noise components. It is shown that for small selection residuals, the resultschange insignificantly. According to the results obtained, the error in reconstructing the aperiodic (flickernoise) component of resistivity variations in the lower layers of the considered section can be decreased to0.4%. The accuracy in reconstructing the seasonal component of resistivity variations depends on the amplitude and phase of seasonal effects in the model profile. For the considered profiles, the error varies from 1to 2%
