Effective and efficient arrival picking plays an important role in microseismic and earthquake
data processing and imaging. Widely used short-term-average long-term-average ratio …

Recorded seismic signals are often corrupted by noise. We have developed an automatic
noise-attenuation method for single-channel seismic data, based upon high-resolution time …

Low-frequency noise is one of the most common types of noise in seismic and microseismic
data. Conventional approaches, such as the high-pass filtering method, utilize the low …

As the signal-to-noise ratio (SNR) of surface microseismic monitoring data is generally low
and large, traditional detection and picking algorithms cannot satisfy the real-time and high …

Downhole microseismic data have the characteristics of low signal-to-noise ratio and high
frequency, which pose a major challenge to noise attenuation. In this paper, we propose a …

Microseismic monitoring is crucial for risk assessment in mining, fracturing and excavation.
In practice, microseismic records are often contaminated by undesired noise, which is an …

We perform the location of microseismic events generated in hydraulic fracturing monitoring
scenarios using two global optimization techniques: Very Fast Simulated Annealing (VFSA) …

Deep learning has been applied to microseismic event detection over the past few years.
However, it is still challenging to detect microseismic events from records with low signal-to …

Surface monitoring of microseismic monitoring events is generally challenging because
microseismic data have a low signal-to-noise ratio (SNR). Traditional event-detection …

Microseismic and seismic data with a low signal-to-noise ratio affect the accuracy and
reliability of processing results and their subsequent interpretation. Thus, denoising is of …