Stable Downward Continuation of Airborne Potential Field Geophysical Data: an Investigation of Stabilizer Family
Publish place: Journal of Mining and Environment، Vol: 12، Issue: 2
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
View: 191
This Paper With 21 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JMAE-12-2_018
تاریخ نمایه سازی: 20 تیر 1400
Abstract:
< p>Attenuation of the signal received from sources causing anomalies and reduction of data resolution are the negative features of airborne measurements. Using a stable downward continuation method is a practical way to address these shortcomings. In this study, we investigated the efficiency of various stabilizers in achieving stable downward continued data. The purpose of this study is to select the most appropriate stabilizer(s) for this operation. We examined the various stabilizing functions by introducing them into the Tikhonov regularization problem. The results of research on synthetic airborne gravity and magnetic data showed that βL۱ (the other definition of L۱ norm) and SM (the smoothest model) stabilizers have the potential to be used in the stable implementation of the downward continuation method. These stabilizers performed better than the other in the three comparisons, including visual, quantitative (RMS error), and graphical comparisons. Also, by examining the airborne magnetic data related to the Esfordi district in Yazd province (Iran), it was found that in general the βL۱ stabilizer is more suitable than the other stabilizing functions studied in this research.
Keywords:
Authors
M. Azadi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
M. Abedi
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
Gh. H. Norouzi Baghkameh
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :