P-Wave detection using deep learning in time and frequency domain for imbalanced dataset

Sugondo, Rhesa Aditya; Machbub, Carmadi

doi:10.1016/j.heliyon.2021.e08605

Cited by 1 publication

(1 citation statement)

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“…Limited information richness, cannot capture complex patterns Frequency domain analysis [55] Extracts frequency features using spectrum analysis, such as dominant frequency and bandwidth, for classification.…”

Section: Simple and Intuitivementioning

confidence: 99%

Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques

Shu,

Dawod

2023

Applied Sciences

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Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal identification methods and techniques over the past decade. The advantages and limitations of commonly used identification methods are systematically analyzed and summarized. Extensive discussions have been conducted on cutting-edge machine learning models, such as convolutional neural networks (CNNs), and their applications in waveform image processing. These models exhibit the ability to automatically extract relevant features and achieve precise event classification, surpassing traditional methods. Building upon existing research, a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) of deep learning in microseismic event analysis is presented. While emphasizing the potential of deep learning techniques in microseismic event waveform image recognition and classification, we also acknowledge the future challenges associated with data availability, resource requirements, and specialized knowledge. As machine learning continues to advance, the integration of deep learning with microseismic analysis holds promise for advancing the monitoring and early warning of geological engineering disasters.

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Section: Simple and Intuitivementioning

confidence: 99%