DFSpy: a PyQt5-based data processing software for distributed fiber sensing

Aiming at the key problems of massive data compression, compatibility of multiple storage formats, low signal-to-noise ratio signal extraction and strain-velocity conversion in distributed fiber optic seismic data processing, this paper proposes a distributed optical fiber sensing data processing framework based on adaptive wavelet transform, coherent noise reduction and other signal processing algorithms, and forms a set of distributed optical fiber sensing data processing opensource software (DFSpy). The software uses Python as the development language, relies on mature third-party libraries such as PyQt5, NumPy, ObsPy, PyWavelets, etc., and adopts a modular and scalable architecture design, which is divided into four modules: data compression, format conversion, data noise reduction, and strain-particle velocity parametric conversion.