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تفکیک بی‌هنجاری‌های ناحیهای و محلی در داده‌های گرانی‌سنجی دو بعدی با استفاده از تحلیل طیفی تکینی دو بعدی | ||
| فیزیک زمین و فضا | ||
| مقاله 3، دوره 50، شماره 3، مهر 1403، صفحه 573-594 اصل مقاله (3.63 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2024.367608.1007574 | ||
| نویسندگان | ||
| امین روشندل کاهو* ؛ رسول انوری | ||
| گروه نفت و ژئوفیزیک، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، شاهرود، ایران. | ||
| چکیده | ||
| تفکیک بی‌هنجاری‌های ناحیهای و محلی در مطالعات میدان پتانسیل و به‌خصوص در داده‌های گرانی‌سنجی، پایه و مبنای تفسیر آنها است. نتایج مدل‌سازی معکوس داده‌های میدان پتانسیل به‌عنوان اصلی‌ترین مرحله تفسیر به‌شدت تحت‌تأثیر داده‌های ورودی است که از مرحله تفکیک بی‌هنجاری‌های ناحیهای و محلی به دست می‌آید. تاکنون روشهای متعددی برای تفکیک بی‌هنجاری ناحیهای از محلی در داده‌های میدان پتانسیل ارائه شده است که هر کدام دارای مزایا و معایبی هستند. اغلب روشهای تفکیک بی‌هنجاری مبتنی‌بر تفکیک مؤلفه‌های عدد موج مربوط به هر کدام از بی‌هنجاری‌های محلی و ناحیهای از یکدیگر می‌باشند. مطالعات پیشین نشان داده است که میان دامنه مؤلفه‌های عدد موج در طیف دامنه دو بعدی و مقادیر تکین ماتریس مسیر به‌دست‌آمده از داده میدان پتانسیل ارتباط مستقیم و نظیر به نظیر وجود دارد. بنابراین، می‌توان بی‌هنجاری‌های ناحیهای و محلی در داده‌های گرانی‌سنجی را با استفاده از روش تجزیه ماتریس مسیر داده گرانی به مؤلفه رتبه – پایین یا روش کاهش رتبه ماتریس مسیر تفکیک کرد. در این مقاله، الگوریتم تحلیل طیفی تکینی برای کاهش رتبه ماتریس مسیر داده گرانی به‌منظور تفکیک بی‌هنجاری‌های ناحیهای و محلی در داده‌های مدل مصنوعی و واقعی استفاده شد و نتایج آن با روشهای تفکیک فیلتر داده‌مبنا، برازش چندجمله‌ای و ادامه فراسو مقایسه شد. نتایج به‌دست‌آمده در مدل مصنوعی و واقعی نشان داد که روش پیشنهادی نسبت به سایر روشهای مورد مقایسه در این مقاله، دقت بیشتری در تفکیک بی‌هنجاری‌های ناحیهای و محلی دارد و اثرات کاذب کمتری در نتیجه حاصل ایجاد می‌کند. | ||
| کلیدواژه‌ها | ||
| تفکیک بی‌هنجاری ناحیهای و محلی؛ تحلیل طیفی تکینی؛ ماتریس رتبه – پایین؛ ماتریس مسیر | ||
| عنوان مقاله [English] | ||
| Separation of regional-residual anomaly in 2D gravity data using the 2D singular spectrum analysis | ||
| نویسندگان [English] | ||
| Amin Roshandel Kahoo؛ Rasoul Anvari | ||
| Department of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| چکیده [English] | ||
| The measured potential field data can be considered as the result of the superposition of the anomalies from sources with various depths. Regional anomalies due to the origin of deep structures and residual anomalies due to the origin of shallow structures form the long and short parts of the total measured field wavelength, respectively. Therefore, one of the most important steps in the potential field data processing is the regional-residual anomalies separation which is used as the basis for inversion and interpretation. The process of separating regional and residual anomalies in potential field data is usually performed in the measured or frequency domain. Methods such as moving averaging, polynomial fitting, and minimum curvature are some of the well-known methods in the potential field separation in the measuring domain. Methods that perform the separation process in the frequency domain have superior performance compared to other methods, making them more common and widely used. Methods such as simple wavenumber filtering, matched filters, preferential filters, and Wiener filters are some of the common methods in the frequency domain to separate regional and residual anomalies. Various researches have shown that the rank of trajectory matrix obtained from measured potential field data depends on the depth of the anomaly source, and the rank of trajectory matrix of the deep sources are lower than that of the shallow sources. In this paper, the spectral analysis of singular values (SSA) was used to reduce the rank of the trajectory matrix obtained from gravity data in order to separate the regional and residual anomalies. Based on the theory of the SSA method, the following method was proposed to separate regional and regional anomalies in 2D gravity data. At the first step, the trajectory matrix is calculated from the Henkel matrices obtained from the measured data. Then, the obtained trajectory matrix is decomposed to eigen triples by employing the SVD and the eigenimages of it are calculated. The optimal value of rank is obtained from the elbow point of the cumulative contribution chart for eigenimages and the trajectory matrix related to regional anomaly is constructed using optimal rank. Finally, the separated regional anomaly is obtained by averaging along anti-diagonals element of the reconstructed trajectory matrix. The efficiency of the proposed method is investigated on both synthetic and real field data examples. Investigating the relationship between the depth of origin of the anomaly and the rank of the trajectory matrix calculated from the measured data showed that there is an inverse relationship between them. The obtained results of synthetic and real data showed that the technique of reducing the rank of the trajectory matrix using SSA can be used as a method of separating anomalies with different depths of origin in potential field data. Also, comparing the results of the proposed method with the results of polynomial fitting and matched filtering methods showed that the proposed method has a better performance in the separation of residual and regional anomalies and can produce better results in environments with high geological complexity. | ||
| کلیدواژه‌ها [English] | ||
| Residual-regional anomaly separation, singular spectrum analysis, low-rank matrix, trajectory matrix | ||
| مراجع | ||
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