Sea Spectral Estimation Using ARMA Models.

Area of Science:

• Oceanography
• Time Series Analysis
• Signal Processing

Background:

• Accurate spectral estimation of sea wave elevation is crucial for understanding ocean dynamics.
• Autoregressive Moving Average (ARMA) models offer a robust framework for time series analysis.

Purpose of the Study:

• To present a methodology for spectral estimation of sea wave elevation time series using ARMA models.
• To analyze the impact of key parameters on the accuracy of ARMA-based spectral estimates.
• To provide practical guidelines for optimizing ARMA model parameters for improved spectral estimation.

Main Methods:

• Utilized Prony's method applied to the auto-covariance series for estimating ARMA coefficients.
• Investigated the influence of signal time length, number of poles, and data points on spectral estimates.
• Focused on mono-modal sea states, with discussions extending to bi-modal sea states.

Main Results:

• Demonstrated the significant effect of ARMA reconstruction parameters on spectral estimation accuracy.
• Established clear relationships between parameter choices and the fidelity of spectral estimates.
• Validated the methodology for both mono-modal and bi-modal sea state conditions.

Conclusions:

• The proposed ARMA modeling approach provides an effective tool for sea wave spectral estimation.
• Optimizing ARMA parameters is essential for achieving accurate and reliable spectral predictions.
• The findings offer valuable guidance for researchers and engineers working with ocean wave data.