# Iterative interferometry-based method for picking microseismic events

## TL;DR

This paper introduces an interferometry-based iterative algorithm for accurately picking microseismic first breaks in noisy data, improving signal detection with minimal iterations.

## Contribution

The study proposes a novel seismic interferometry and time-frequency analysis method for microseismic first-break picking, reducing noise influence and requiring only two iterations for convergence.

## Key findings

- Significantly improves first-break detection in noisy microseismic data
- Converges within two iterations, avoiding noise correlation issues
- Effective on both synthetic and real datasets

## Abstract

Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.