# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 1 Introduction  
In site selection for aquifer underground gas storage, assessing geological conditions via trap evaluation is a key factor. In particular, the evaluation of the sedimentary thickness of the reservoir and caprock is directly related to the gas storage capacity and sealing performance of the aquifer trap [1]. The geological conditions in southern China are complex, and time-frequency analysis has become an important technology in research on strata sedimentary characteristics. A high-precision time-frequency decomposition method based on seismic identification is critical to the reliability of sedimentary layer thickness prediction. Since [PERSON] et al. (1979) utilized complex seismic trace analysis techniques for seismic signal analysis [2], the possibility that low-frequency anomalies are related to oil and gas reservoirs has become a widely acknowledged notion. Subsequently, several scholars have proposed various methods to extract frequency-related attributes for the interpretation of seismic data. Among these, the spectral decomposition method introduced by [PERSON] et al. (1999) has garnered widespread attention in reservoir characterization [3]. By utilizing seismicdata and the Discrete Fourier Transform (DFT), the spectral decomposition method can reveal more stratigraphic details, allowing for the analysis of small-scale structures and seismic attenuation anomaly zones that are often closely associated with oil and gas reservoirs [4]. Time-frequency analysis is a technique that transforms seismic data from the time domain to the frequency domain through certain mathematical transformations. It decomposes seismic records into different frequency components and uses the varying response characteristics of different frequencies on geological bodies at various scales to interpret these geological bodies, thereby conducting seismic interpretation (a frequency-based reservoir interpretation technology) [5]. This technique allows for the analysis of amplitude corresponding to each frequency in the frequency domain, eliminating mutual interference between different frequency components in the time domain. Not only does it enhance the predictive capability of seismic data for thin reservoirs, but it also extracts richer geological information from conventional seismic datasets, improving the ability to interpret and identify special geological bodies. [PERSON] et al. explored small-scale geological issues of carbonate reservoirs using spectral decomposition through the establishment of a positive modeling model [6]. [PERSON] et al. P proposed exploring corresponding time-frequency patterns for different geological conditions [7]; [PERSON] et al. introduced a spectral imaging method based on physical wavelet transform to finely characterize high-frequency energy [8]. [PERSON] et al. [9] predicted the gas content of coal seams in a specified study area by utilizing spectral decomposition based on the high-frequency absorption and attenuation characteristics caused by coalbed methane (methane) enrichment. These studies demonstrate that spectral techniques have advantages over traditional seismic attribute methods in identifying structural fracture systems and predicting thin-layer thicknesses, and actively promote the application of spectral decomposition technology in coalfield seismic data analysis. Spectrum decomposition is an earthquake attribute technology based on time-frequency analysis [10; 11; 12]. It processes seismic data into frequency slices, enabling thin-layer reflection systems to produce complex resonant reflections. Fundamentally, spectrum decomposition algorithms are methods for processing seismic reflection data by breaking down seismic information into frequency information. By selecting specific frequencies, it helps interpretation personnel effectively identify and extract hidden geological layers and structural features by browsing selected frequency data, fully tapping into the implicit geological information.