University of Tehran Press Journal of the Earth and Space Physics 2538-371X 50 4 2025 02 19 Gas Reservoir Detection Using Mixed Components Short Time Fourier Transform (MC-STFT) as a new attribute Gas Reservoir Detection Using Mixed Components Short Time Fourier Transform (MC-STFT) as a new attribute 71 79 100109 10.22059/jesphys.2025.375631.1007602 FA Ali Jalali Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran. Majid Bagheri Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran. Mostafa Abbasi National Iranian oil company (NIOC), Tehran, Iran. Journal Article 2024 05 06 Identification of gas reservoirs as a main natural resource due to their economic importance has always been one of the most important issues in research studies in the oil and gas fields. Accurate localization of a gas reservoir through seismic data has been broadly studied. The final destination of all seismic attributes is to distinguish a specific feature. Accordingly, many seismic attributes have been developed, among which short-time Fourier transform (STFT)-based methods play an important role. The location of gas reservoirs can be detected, taking advantage of its particular criteria in seismic data. Generally, seismic signals are non-stationary as their frequency responses vary with time. Thus we propose an attribute that utilizes mixed components of STFT (MC-STFT). The novelty about this method is that without altering STFT method or adding any complexity, MC-STFT is able to detect gas reservoirs at high resolution. Simplicity and time efficiency can make a method superior. In fact, this method takes advantage of extracting three frequency components obtained by STFT. In the next step, we can do the second iteration of the procedure, this will represent the degree of sharpness of reduction in amplitude and again do the same jobs as before and it leads to this, making it more specific. We apply this method to three data sets, first, Marmousi model and then two other real seismic zero-offset sections. To evaluate the proposed method compared with the Synchrosqueezing STFT (SSTFT). The results confirm the good performance of MC-STFT in high-resolution gas reservoir detection. Identification of gas reservoirs as a main natural resource due to their economic importance has always been one of the most important issues in research studies in the oil and gas fields. Accurate localization of a gas reservoir through seismic data has been broadly studied. The final destination of all seismic attributes is to distinguish a specific feature. Accordingly, many seismic attributes have been developed, among which short-time Fourier transform (STFT)-based methods play an important role. The location of gas reservoirs can be detected, taking advantage of its particular criteria in seismic data. Generally, seismic signals are non-stationary as their frequency responses vary with time. Thus we propose an attribute that utilizes mixed components of STFT (MC-STFT). The novelty about this method is that without altering STFT method or adding any complexity, MC-STFT is able to detect gas reservoirs at high resolution. Simplicity and time efficiency can make a method superior. In fact, this method takes advantage of extracting three frequency components obtained by STFT. In the next step, we can do the second iteration of the procedure, this will represent the degree of sharpness of reduction in amplitude and again do the same jobs as before and it leads to this, making it more specific. We apply this method to three data sets, first, Marmousi model and then two other real seismic zero-offset sections. To evaluate the proposed method compared with the Synchrosqueezing STFT (SSTFT). The results confirm the good performance of MC-STFT in high-resolution gas reservoir detection. https://jesphys.ut.ac.ir/article_100109_44f5897f1df51eaa791b624197c78957.pdf