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Abstract— <p>The work is related to environmentally friendly, nature-like methods of hydrocarbon deposit exploration based on passive detection of the natural microseismic noise background of oil and gas fluids. The fundamental problem of monitoring the accuracy and reliability of measuring random noise signals is considered. An approach is proposed using the Allan variance statistical method, well known in metrology. Using the example of microseismic background measurements in a real-life situation, it is shown that this approach allows for precise control and identification of all stages of statistical data accumulation right up to the manifestation of the nonstationarity factor. It is demonstrated that in the example under consideration, the maximum possible accuracy of approximately 0.005 is achieved and substantiated at a recording duration of 25&#xa0;min. Failure to adhere to the specified optimal time interval leads to a sharp increase in error. It is concluded that the proposed approach using the Allan variance method is extremely effective for solving the stated problem of noise measurements. The conducted review will be useful in the development of environmentally friendly noise methods for oil and gas exploration, as well as in the use of noise methods in electrochemistry.</p>

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Statistical Control of the Microseismic Noise Signal Measurement Process in Environmentally Friendly Seismic Exploration Methods

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • M. A. Abaturov,
  • V. N. Andreev

摘要

Abstract—

The work is related to environmentally friendly, nature-like methods of hydrocarbon deposit exploration based on passive detection of the natural microseismic noise background of oil and gas fluids. The fundamental problem of monitoring the accuracy and reliability of measuring random noise signals is considered. An approach is proposed using the Allan variance statistical method, well known in metrology. Using the example of microseismic background measurements in a real-life situation, it is shown that this approach allows for precise control and identification of all stages of statistical data accumulation right up to the manifestation of the nonstationarity factor. It is demonstrated that in the example under consideration, the maximum possible accuracy of approximately 0.005 is achieved and substantiated at a recording duration of 25 min. Failure to adhere to the specified optimal time interval leads to a sharp increase in error. It is concluded that the proposed approach using the Allan variance method is extremely effective for solving the stated problem of noise measurements. The conducted review will be useful in the development of environmentally friendly noise methods for oil and gas exploration, as well as in the use of noise methods in electrochemistry.