B.V. Emelyanov1, O.N. Sherstyukov2, V.A. Ryzhov3

1,2 Kazan Federal University (Kazan, Russia)

3 PrizmaGeo LLC (Kazan, Russia)

1 emelyanov.bulat.91@mail.ru; 2 Oleg.Sherstyukov@kpfu.ru; 3 v.ryzhov@prisma-geo.com

The work is devoted to the urgent problem of automatic detection of microseismic events in Distributed Acoustic Sensing (DAS) data. In real downhole monitoring conditions, data are characterized by a low signal-to-noise ratio (SNR), background non-stationarity, and the presence of intense coherent noise, such as tube waves. Traditional threshold methods operating in the time domain often prove ineffective, allowing weak events to be missed due to the masking effect by stronger signals or generating false positives. The paper proposes and tests a new integrated approach to DAS data processing. The methodology includes the formation of a hybrid dataset (synthetic responses with known parameters embedded in real noise records), the use of cascade filtering (amplitude-phase stabilization, bandpass filtering 10–300 Hz, and suppression of quasi-harmonic noise), as well as source location using the Maximum Likelihood Estimation (MLE) method. To eliminate the non-stationarity of the decision function, a normalization procedure using a three-dimensional median filter was applied. A key feature of the developed algorithm is the transition from the amplitude detection criterion to topological analysis using the unsupervised machine learning method DBSCAN in the three-dimensional feature space "depth–radius–time". This made it possible to cluster events based on probability density distribution. Comparative analysis on the test sample showed that the proposed method allows successful identification of events occurring simultaneously at different offsets from the well, which are indistinguishable for standard algorithms. Quantitative efficiency assessment demonstrated a significant advantage of the approach: the modified Matthews Correlation Coefficient (MCC) increased from 0.125 (for the standard threshold detector) to 0.37.

Emelyanov B.V., Sherstyukov O.N., Ryzhov V.A. Increasing the noise immunity of microseismic event detection in DAS data by combining the likelihood method and DBSCAN clustering // Radiotekhnika. 2026. V. 90. № 6. P. 90−101. DOI: https://doi.org/10.18127/j00338486-202606-09

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