Mapping and Identifying Two Types of Geological Features from Enhanced Derivatives of Aeromagnetic Data: A Case Study from Northwest Inner Mongolia

Mapping and Identifying Two Types of Geological Features from Enhanced Derivatives of Aeromagnetic Data: A Case Study from Northwest Inner Mongolia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping and Identifying Two Types of Geological Features from Enhanced Derivatives of Aeromagnetic Data: A Case Study from Northwest Inner Mongolia Chong Zhang, Chris Green, Jiayong Yan, Fan Luo, Jia Guo, Bo Xu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937737/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract With difficulties of finding outcrop mines increasing, mineral explorations go to sedimentary cover areas, however, geological features of rocks and structures which are the dominant control to ore deposits hidden deeply beneath sedimentary cover are difficult to identify especially in the absence of seismic or electrical methods. Magnetic edge detection methods help find structures and boundaries of anomalous geological features. Still, there are gaps between geophysical edge detection results and geological demands, that is all edge detection results sharpen boundaries without distinguishing the sub-circular boundaries of anomalous geological rocks and blocks from the sub-linear boundaries of anomalous geological layers and fault structures, as these two types of geological boundaries would determine and influence mineral systems differently. Trying to improve this issue, we present a combined analysis approach of edge detections to identify the two key types of geological boundaries. Firstly, we initiated the approach by applying four generally accepted methods to detect geological feature edges. Secondly, we analysed and interpreted edge detection results according to combining and integrating the characteristics of each employed algorithm. Finally, we present the geologic sketch map, which shows the distribution of inferred geological rocks and structures based on the combined analysis of aeromagnetic data in a thick sedimentary cover area of northwest Inner Mongolia. Our geologic sketch map may be helpful for further studies of Inner Mongolia and our combined analysis approach may be useful for other coverage geological boundary identifications. Geological Boundary Magnetic Anomaly Edge Detection Inner Mongolia Derivatives Analytic Signal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Article Highlights We proposed a scheme to identify both sub-circular and sub-linear geological features using aeromagnetic data. We demonstrate the approach which analyses multiple existing edge enhancement techniques. Results from a sedimentary cover part of Inner Mongolia provide important geological and mining insights. 1. Introduction The Mandula and Sonid Youqi region, located in west of the Erlian Basin of the northwest of the Inner Mongolia Autonomous Region of China, has been of interest to geoscientists due to its potential mineral resources, as this area is a significant component of the Daxing'anling Metallogenic Province, whose south part is adjacent to the North China Metallogenic Province, and it forms part of the Fe-Cu-Mo-Pb-Zn-Cr-(Au-Mn)-Ge-Coal mineralization zone of the Bainaimiao and Xilinhot areas (Xu et al. 2008 ). However, it is difficult to find any mineral deposits directly here as the majority of the study area is covered by sedimentary strata and it is hard to carry out geological fieldwork; only a few geological field studies have been carried out in the areas surrounding but outside the study area (Tang et al. 2022 ; Li et al. 2016 ). Previous regional tectonic frameworks of this area based on geological field studies were presented as similar, but the local geological rocks and structures which are critical to ore deposits were mapped as different from each other (Tang et al. 2022 ; Li et al. 2016 ; Zhang et al. 2014 ; Pang et al. 2017 ). These diverse understandings of local geological rocks and structures also aggravate a complicated situation of no discoveries of mineral deposits in the study area. Geophysical methods can aid the understanding of subsurface rocks and structures for local sedimentary cover areas (Zhang et al. 2014 ), however, no seismic or electrical field studies have been carried out in the study area of the Mandula and Sonid Youqi region. As one cheap, primary and key geophysical method, aeromagnetic surveys were implemented over the study area. Aeromagnetic surveys provide insights into subsurface rocks and structures and hence mineral exploration as magnetic anomalies are caused by secondary magnetic fields produced by various rocks and layers based on the Earth's past and present magnetic field (Isles and Rankin 2013 ). Additionally, aeromagnetic surveys provide subsurface information based on more than one line or profile as they are surface-coverage surveys (Isles and Rankin 2013 ). To better identify rocks and geological structures, enhancement methods have been introduced to interpret magnetic anomalies to detect structures and edges of anomalous geological bodies; this can aid in locating mineral deposits (Liu 2023; Cascone 2017; Pilkington 2007 ; Pilkington 2004 ; Blakely 1986 ). Derivative-based edge detection methods are major options for these enhancements as they are mostly high-pass filters to sharpen structures and boundaries of anomalous geological bodies which are represented by extreme (local high or low) or zero anomaly values (Liu et al. 2023 ; Cascone et al. 2017 ; Pilkington 2007 ; Pilkington and Keating 2004 ; Blakely and Simpson 1986 ). The sharpened edges indicate various structures and boundaries of anomalous geological bodies, including the closed-loop boundaries such as sub-circular (or sub-rectangular) features of discrete bodies of granites (or kimberlites) and fault blocks, and the curvilinear-segment edges such as sub-linear features caused by faults and anomalous layers. These two types of sub-circular and sub-linear geological boundaries have been proven effective in mineral resource discoveries (Isles and Rankin 2013 ), but these two types are related to different mineral systems, like the sub-linear feature boundaries relating to Granite Sn-W-Fe deposits controlled by linear trending fold and fault structures induced by the plate window in the upwelling area, and the sub-circular feature boundaries relating to porphyry Cu-Mo deposits controlled by anticlines, pipes and oval bodies caused by the tear-off of the subducting slab (Mao et al. 2012 ; Lowell and Guilbert 1970 ). For sub-circular feature boundary detection, Cooper and Cowan ( 2004 ) applied the Hough transform (Hough 1962; Wang and Howarth 1990 ) to detect circular features from potential field anomalies caused by kimberlite pipes or meteorite impact craters. Keating and Sailhac ( 2004 ) used the analytic signal to identify circular magnetic anomalies caused by kimberlite pipes. Cooper and Cowan ( 2005 ) used textural analysis to locate these features in geophysical data. Krøgli and Dypvik ( 2010 ) presented an automatic search algorithm to detect circular impact structures. Holden et al. ( 2011 ) presented an automatic image analysis method called radial symmetry transform to find circular magnetic anomalies of an idealised porphyry mineral system. For linear feature boundary enhancement, Wang and Howarth ( 1990 ) applied the Hough transform (Hough, 1962) to detect linear faults from Landsat TM images. Sykes and Das ( 2000 ) used a directional filter based on 2D Radon transforms to enhance lineaments. Dentith (2000) and Holden et al. ( 2008 ) presented an automated analysis of magnetic data using texture analysis based on a grey-level co-occurrence matrix to identify linear features of gold lode deposits. It is vital to identify the two types of geological boundaries for targeting exploration for different ore deposits. However, most methods are difficult to detect and distinguish like in highly active magnetic relief (Cowan et al. 2000 ). The sub-linear or sub-circular features for mineral systems are not purely linear or perfectly circular, but generally curvilinear-segment representing dykes and faults, or closed-loop (approximately circular or rectangular) outlines relating to isolated anomalous bodies. Additionally, most developments of derivative-based edge enhanced methods have been focused on improving algorithms to meet higher demands of detecting and sharping boundaries (Liu et al. 2023 ; Cascone et al. 2017 ; Pilkington 2007 ; Pilkington and Keating 2004 ; Blakely and Simpson 1986 ), instead of analyzing the results produced and combining them to increase the geological understanding of exploration for different ore-deposits. There are gaps between the pure algorithm development of edge enhanced methods and the practical geological understanding of edge detection results. Therefore, semi-automatic analysis, using integrated filters and combined analysis, rather than mathematical treatment, using purely specialized filters which could easily and directly identify sub-linear or sub-circular features which are not perfectly straight or circular, can provide objective information. Our study aims to enhance the identification of the two types of geological feature boundaries from a combined analysis of the edge enhanced results of the magnetic anomalies in the sedimentary cover Mandula and Sonid Youqi area of the Inner Mongolia Autonomous Region of China. By employing four edge enhanced techniques and integrating their boundary results, we seek to identify and characterize horizontal distributions of sub-linear and sub-circular features of anomalous geological rocks and structures under sedimentary cover. The remainder of this paper is structured as follows: Section 2 provides a geological review contextualizing our study within the existing geological knowledge. Section 3 details the methodology of the magnetic anomaly edge enhancement and outlines the steps taken in the combined analysis and interpretation of the results. Section 4 presents our enhancing results in horizontal distributions of anomalous geological rocks and structures. Section 5 offers a conclusion, summarizing the key insights gained from our study. 2. Geological Setting The majority of the study area is located in the Erlian Basin of Inner Mongolia. It is geographically to the west of Sunite Youqi of Xilingol League, to the south of Erenhot City of Xilingol League, to the north of Siwangziqi of Ulanqab City, and to the east of Darhan-Muminggan Lianheqi of Baotou City. 2.1. Lithostratigraphy and magmatic rocks In the study area, the Cenozoic Erathem are widely developed, with Paleogene, Neogene and Quaternary at the surface, covering almost 90% of the total exposed area. Additionally, only a small amount of Silurian and Cretaceous strata are exposed (Pang et al. 2017 ; Qi et al. 2015 ; Zhang et al. 2019 ). The details can be found in Fig. 1 and Table 1 . As regards highly magnetic rocks, igneous rocks such as andesite, allgovite, basalt, diorite, gabbro, and porphyrite likely have high magnetisation see Table 2 . Table 1 A lithological table of the study area (modified from Tang et al. 2022 ; Pang et al. 2019; Pang et al. 2017 ), in which the break lines ‘ํ’ represent discontinuity, and the jagged lines ‘๏’ represent stratigraphic unconformity. Erathem System Series Lithology Cenozoic Erathem (Cz) Quaternary System (Q) -- aeolian, modern alluvial, flood, and lacustrine sand, gravel and silt, sand and gravel layers, sandy clay and calcareous argillaceous sand layers, gravel-bearing coarse sandstone, silt, etc Neogene System (N) -- brownish-red silty mudstone, gravel-bearing coarse sandstone, greyish-white glutenite, conglomerate, and usually with visible calcareous concretion Paleogene System (E) Oligocene (E3) greyish-white gritstone, glutenite and yellowish-green mudstone Eocene (E2) greyish-green mudstone, greyish-white fine-grained sandstone, and gritstone Paleocene (E1) red and variegated sand-mudstone formations from rivers-lakes sedimentary, containing abundant mammalian fossils Mesozoic Erathem (Mz) Cretaceous System (K) Lower Cretaceous (K 1 ) mudstones and sandy conglomerates and locally interbedded with oil shale, marl, breccia and brown coal. Silurian System (S) -- metamorphic sandstone, phyllite, and quartzite, constituting a metamorphic rock series of greenschist facies The Silurian System (S) is exposed on a small scale in the southeast and mainly peripheral regions of the study area. Its lithology is characterized by metamorphosed sandstone, phyllite, and quartzite, constituting a metamorphic rock series of greenschist facies (Pang et al. 2017 ; Qi et al. 2015 ; Zhang et al. 2019 ). The Cretaceous System (K) is dominated by the Lower Cretaceous (K 1 ) and has limited distribution in the south of the study area with significant sedimentary thickness. The major outcrops mainly consist of a set of sedimentary strata of fluvial and lacustrine facies, composed of three sedimentary cycles of clastic rocks from bottom to top: coarse fine coarse. Its lithology mainly consists of mudstones and sandy conglomerates and locally is interbedded with oil shale, marl, breccia and brown coal (Pang et al. 2017 ; Qi et al. 2015 ; Zhang et al. 2019 ). The Paleogene System (E) is well-developed and widely distributed, with relatively small sedimentary thickness. The surface mainly exposes mudstone, siltstone, fine sandstone, gritstone, and glutenite. Overall, the sedimentary processes of the three sets of Paleogene strata are dominated by meandering rivers and floodplains, with a general sedimentary direction from south to north. The Paleocene (E1) of the Paleogene System (E) distributed in the central and northern region of the study area, with relatively wide exposure, is a set of red and variegated sand-mudstone formations from rivers and lakes, containing abundant mammalian fossils. The Eocene (E2) is exposed relatively little. The lithology is greyish-green mudstone, greyish-white fine-grained sandstone, and gritstone. The lithology of the widely distributed Oligocene (E3) consists of greyish-white gritstone, glutenite and yellowish-green mudstone (Pang et al. 2017 ; Qi et al. 2015 ; Zhang et al. 2019 ). The Neogene System (N) is widely exposed in the east of the study area, and the lithology mainly consists of brownish-red silty mudstone, gravel-bearing coarse sandstone, greyish-white glutenite, and conglomerate, usually with visible calcareous concretion (Pang et al. 2017 ; Qi et al. 2015 ; Zhang et al. 2019 ). The Quaternary System (Q) is mainly exposed in the northwest and east of the study area, and is dominated by aeolian, modern alluvial, flood, and lacustrine deposits (aeolian sand, sand, gravel, silt). It mainly consists of sand and gravel layers, sandy clay and calcareous argillaceous sand layers, gravel-bearing coarse sandstone, silt, etc (Jingwen et al. 2012; Pang et al. 2017 ; Qi et al. 2015 ). The intrusive rocks are acidic granite locally exposed in the southeast of the study area, mostly occurring as small intrusive bodies or rock stocks (Pang et al. 2017 ; Qi et al. 2015 ; Zhang et al. 2019 ). 2.2. Structural Setting Geographically, our study area is located in the southwest of the Erlian Basin and the Erlian Basin is situated on the Inner Mongolian Plateau, north of the Yinshan Mountains and west of the Greater Khingan Mountains. In the regional tectonic setting, the Erlian Basin where our study area is located is a Mesozoic continental basin with structural complexity developed in the Xingmeng Orogenic Belt. Our study area has undergone development of a series of northwest and west-northwest trending grabens and half-graben faults during the Mesozoic era, with lithofacies controlled by contemporaneous normal faults or strike-slip normal faults and significant lateral variations (Tang et al. 2022 ; Pang et al. 2017 ; Lowell and Guilbert 1970 ). From the perspective of basin structure and tectonic sedimentary evolution, our study area exhibits the characteristics of a rift basin and the Early Cretaceous was the primary period of its rifting (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). 3. Data The vast majority of the study area is in a plain, with a small portion having low mountains and hills. Due to the flat terrain and little elevation change, field surveys should be relatively straightforward with the use of off-road vehicles, however, many areas are pastoral areas, which can only be entered by local residents. Thus, aeromagnetic surveys are much more convenient and efficient. 3.1. Aeromagnetic Data The aeromagnetic data were obtained from the Airborne Survey and Remote Sensing Center of the Nuclear Industry of China and initially collected by a CS-3 high-precision cesium magnetometer with a sensitivity of 0.0006 nT/(Hz) ^(1/2) mounted on the tail of a Cessna-208B fixed wing aircraft. The magnetic data compensation was performed by a real-time MMS-4 automatic magnetic digital compensation instrument. The base station equipment was a G-858SX cesium magnetometer with a sensitivity of 0.003 nT. The average height of the flight was 99 m and the average speed of the flight was 226 km/h (about 62.78 m/s). The sample rate was 10 times/s and line space was 500 m. Testing before equipment installation included consistency, stability, steering difference and step response rise time tests, and the testing and calibration after equipment installation included ground static noise testing and aircraft compensation, the total accuracy of the magnetic measurement was 1.70 nT after adjustment – based on repeat and crossing lines. The above measured magnetic data consist of the internal magnetic field caused by the geological rocks and structures above the Curie isotherm, the interior magnetic field originating in the Earth’s core, the external magnetic field due to the currents flowing in the ionosphere and errors from the measurement (Li and Nabighian 2015 ). As we are most interested in magnetic variations in the lithosphere, the relative variation of the total field magnetic intensity, \(\:{\Delta\:}\text{T}\) , called the magnetic anomaly hereon, which is the difference between the total field magnetic intensity and the total field normal geomagnetic intensity, reflecting the horizontally and vertically distributed magnetic geological bodies, is our objective data or anomaly to be used. To obtain the magnetic anomaly \(\:{\Delta\:}\text{T}\) , magnetic data corrections should be applied such as correction for the Earth’s normal magnetic field, correction of magnetic diurnal variation, correction of flight altitude, hysteresis correction and levelling (Isles and Rankin 2013 ). To facilitate processing and display, the magnetic anomaly data were gridded at a cell size of 125 m×125 m using the minimum curvature method in Geosoft’s Oasis Montaj. The magnetic anomaly in the Mandula and Sonid Youqi area of Inner Mongolia is shown in Fig. 2a. To better correlate the magnetic anomaly with causative geological bodies and structures, thus aiding the interpretation, the reduction-to-pole (RTP) of the magnetic anomaly (called RTP magnetic anomaly hereon) shown in Fig. 2b was generated using an inclination of and a declination of to remove the skewness of the magnetic anomaly in Fig. 2a using the MAGMAP menu of Oasis Montaj (Hinze et al. 2013 ). 3.2. Magnetic Susceptibility Statistics of Major Rock Types As most of the study area is under sediments, measurements of rock magnetic susceptibility include two groups: one is the on-site measurement of outcrop bedrock within the study area; the other is measurements made on similar rocks outside the study area. General values of rock magnetic susceptibility in the study area and its surrounding areas are shown in Table 2 . Table 2 Magnetic susceptibility statistics of major rock samples in the study and its surrounding areas. Strata Lithology Sample Number Maximum Susceptibility (×10 − 5 SI) Minimum Susceptibility (× 10 − 5 SI) Average Susceptibility (× 10 − 5 SI) Quaternary (Q) Sandy Clay and Gravel 360 77 1 34 Neogene (N) Mudstone, Silstone, Glutenite, Silty Mudstone, Conglomerate 330 80 1 21 Paleogene (E) Mudstone, Siltstone, Gritstone 1140 61 1 13 Cretaceous (K) Mudstone, Sandstone, Conglomerate 630 62 2 27 Jurassic (J) Tuff 240 125 6 31 Jurassic (J) Andesite 90 2300 621 1428 Permian (P) Limestone 480 130 1 10 Permian (P) Basalt 30 3260 1300 2053 Permian (P) Fluorite 30 10 1 4 Carboniferous (C) Limestone 90 42 1 12 Carboniferous (C) Iron-bearing Quartz Vein 30 696 131 299 Carboniferous (C) Basalt 30 1780 987 1251 Silurian (S) Slate, Schist, Metamorphic Siltstone 270 45 2 16 Proterozoic (Pt) Marble, Quartzite 270 29 1 7 Proterozoic (Pt) Marble 210 50 4 13 Proterozoic (Pt) Magnetite, Garnet Magnetite Quartzite 60 53600 6650 25270 Acidic Rock (γ) Granite 780 410 1 24 Acidic Rock (π) Granite Porphyry 120 32 11 17 Intermediate-felsic Rock (γδ) Granodiorite 150 47 3 15 Intermediate-felsic Rock (δο) Quartz Diorite 30 81 15 32 Intermediate Rock (δ) Altered Diorite, Diorite 270 516 17 119 Intermediate Rock (δµ) Diorite-porphyrite 60 198 33 112 Basic Rock (ν) Gabbro 60 3980 36 1401 Basic Rock (νδ) Gabbro Diorite 60 5340 965 3580 Basic Rock (βµ) Allgovite 30 1421 424 989 4. Methodology 4.1. Edge Detection Methods for Magnetic Anomaly In this section, we chose four magnetic anomaly edge enhanced methods which are all applied to the grid of RTP data: vertical derivative, total horizontal derivative, analytic signal amplitude and tilt derivative. All these methods are used in the early stages of interpretation – enhancing the magnetic grids to better understand the subsurface geology (Pilkington and Tschirhart 2017 ). 4.1.1. Vertical Derivative (VDR) The first-order vertical derivative (VDR) of the total field magnetic intensity has been proposed and used for decades (Li and Nabighian 2015 ; Hinze et al. 2013 ). Hood and McClure ( 1965 ) may be the first to identify the vertical contacts using the VDR; its definition for the RTP magnetic anomaly can be $$\:\:\text{V}\text{D}\text{R}=\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{z}}.\:\:\:\:\:\left(1\right)$$ Where, \(\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)\) is the RTP magnetic anomaly and \(\:\text{z}\) increases downwards. Generally, along the outward normal direction of the edge of a positive anomalous source, the VDR of the RTP magnetic anomaly changes from positive to zero to negative, while along the outward normal direction of the edge of a negative anomalous source, and vice versa. Therefore, when both positive and negative anomalous sources are adjacent to each other, there would be another zero value, which leads to the creation of an additional edge, zero values represent edges of anomalous geological bodies (Liu et al. 2023 ). Another advantage of using VDR is that it is potentially less sensitive to noise in the data compared to methods relying on higher-order derivatives. We used MAGMAP an FFT processing suite within Oasis Montaj for vertical derivative calculation. VDR tends to enhance shallower geological sources compared to deeper ones since signals from shallower sources attenuate faster with height. However, the VDR still can be used to sharpen boundaries of all anomalous sources and to approximately define the edges of sources by its zero contour R. 4.1.2. Total Horizontal Derivative (THDR) The total horizontal derivative (THDR) of the total field magnetic intensity also has been proposed and used for decades (Li and Nabighian 2015 ; Hinze et al. 2013 ; Dean 1958 ; Baranov 1957 ; Ravat 2007; Pilkington and Tschirhart 2017 ; Hood and McClure 1965 ; Grauch and Cordell 1987 ; Ekinci et al. 2013 ; Eldosouky et al. 2020 ). This is the square root of the sum of the squares of the first derivatives in the horizontal x and y directions. Its definition for the RTP magnetic anomaly can be $$\:\text{T}\text{H}\text{D}\text{R}=\sqrt{{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{x}}\right)}^{2}+{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{y}}\right)}^{2}}.\:\:\:\:\:\left(2\right)$$ Grauch and Cordell ( 1987 ) discussed the factors which affect the boundaries enhanced by the THDR. The THDR requires only the calculation of horizontal derivatives, which can be readily performed in the space domain. It is less likely to produce additional boundaries, and is less sensitive to noise than some filters (Grauch and Cordell 1987 ). We use GRIDGRAD.GRD in Oasis Montaj where a \(\:3\times\:3\) point convolution filter is applied to produce the results. THDR results in relatively wide-band peaks compared to some derivative operators and the detection resolution is quite low, but the positive extreme values of the THDR correspond to anomalous geological sources’ edges quite accurately (Ekinci et al. 2013 ). Maxima of THDR show edges at all depths, but the efficiency of THDR decreases when anomalies from geological sources with different intensities and burial depths overlap (Liu et al. 2023 ). 4.1.3. Analytic Signal Amplitude (ASA) For 2-D cases, Nabighian ( 1972 ) defined a complex analytic signal form and its corresponding amplitude of the analytic signal which is the square root of the sum of the squares of the derivatives in the x and z directions. This amplitude of the analytic signal is identical to the total gradient for given data and is independent of the magnetic inclination or remanent magnetism(Li et al. 2015). The formulation of the 3-D analytic signal is still debated (Li et al. 2015), but the formula presented by Roest et al. ( 1992 ) for the analytic signal amplitude is commonly used: $$\:\text{A}\text{S}\text{A}=\sqrt{{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{x}}\right)}^{2}+{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{y}}\right)}^{2}+{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{z}}\right)}^{2}}.\:\:\:\:\:\left(3\right)$$ This is not strictly the amplitude of the true complex analytic signal but the total gradient (TG) in three dimensions and uses the RTP magnetic anomaly to calculate the 3-D analytic signal amplitude (ASA). Though not perfect, this calculation of the ASA has been shown to be effective in identifying boundaries of anomalous geological bodies. The maximum values of the ASA correspond to anomalous geological sources’ edges, however, ASA results in relatively wide-band peaks and the detection resolution is relatively low compared to some other derivative techniques (Liu et al. 2023 ). Hence ASA is particularly suitable for highlighting deeper geological sources. We applied the GRIDASIG GX in Oasis Montaj to calculate the analytic signal of the RTP grid. 4.1.4. Tilt Derivative (TDR) For 3-D cases, Miller and Singh ( 1994 ) defined the tilt derivative (TDR, also called tilt angle (TA)) as the ratio between the vertical and the total horizontal derivatives of the RTP magnetic anomaly (Ferreira et al. 2013 ): $$\:\text{T}\text{D}\text{R}={\text{tan}}^{-1}\left(\frac{\text{V}\text{D}\text{R}}{\text{T}\text{H}\text{D}\text{R}}\right)={\text{tan}}^{-1}\left(\frac{\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{z}}}{\sqrt{{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y},\right)}{\partial\:\text{x}}\right)}^{2}+{\left(\frac{\partial\:\text{R}\text{T}\text{P}\left(\text{x},\text{y}\right)}{\partial\:\text{y}}\right)}^{2}}}\right),\:-{\pi\:}/2\le\:\text{T}\text{D}\text{R}\le\:{\pi\:}/2.\:\:\:\:\:\left(4\right)$$ Where the and are the first vertical and total horizontal derivatives of the RTP magnetic anomaly, respectively. The tilt derivative (TDR) can be simply understood as the amplitude-normalized first-order vertical derivative (VDR). We use the Tilt Derivative option (TILTDRV GX) in Oasis Montaj to calculate the tilt derivative of a grid. The TDR is useful for mapping shallow basement structures and mineral exploration targets as it acts like an automatic gain-control filter and deeper edges can thus be identified (Li and Nabighian 2015 ). Though the zero values of TDR may be sensitive to spurious boundaries due to complications of positive and negative anomaly superposition (Liu et al. 2023 ), the zero values of TDR correspond more accurately to boundaries of anomalous geological sources. The results inside the zero contours represent anomalous geological blocks; the positive results correspond to more magnetic sources and vice versa. 4.2. Combined Analysis Section 4.1 . would produce mixed interpretation of boundary results of the magnetic anomaly, as all responses of shallow and deep geological sources are merged in the anomalies and the overlap of both the sub-circular boundaries of anomalous geological rocks and blocks and the sub-linear boundaries of anomalous geological layer and fault are identified by the four edge detection methods. To refine the detection results for a better understanding of the geological bodies and structures for ore deposit exploration, we analyse the combined results of Section 4.1 according to the four methods’ discrepancy detection abilities. The corresponding steps are proposed: First step: Due to containing all three direction derivatives and an amplitude-normalized method, the TDR exhibits relatively sharp edges and the zero values of the TDR results represent both the sub-circular and sub-linear boundaries of anomalous geological features. The TDR is an effectively amplitude-normalized method which balances the amplitudes and differences of deep and shallow sources and this is crucial in revealing large-scale, deep geological variations at the same time as shallow edges. So we use the TDR to identify all edges from anomalous geological features, and also both deep and shallow edges. The width of features on the TDR map can also indicate the relative depths of different features. Second step: The zero values of the VDR also represent the sub-circular boundaries and the sub-linear boundaries. This is similar to the results of the TDR, however, as the VDR is not so sensitive to deep anomalous geological signals, the deep geological sources would not be so prominent in their results (Verduzco et al. 2004 ). Therefore, we use the results of VDR to verify the TDR results in Step 1 and to distinguish shallow and deep geological sources from the TDR results. Third step: Maximum values of ASA correspond to geological edges, but ASA exhibits relatively low resolution with wide-band peaks and the amplitudes of results vary with depths (Verduzco et al. 2004 ). So we use the ASA to identify the horizontal positions of the sub-circular boundaries and also distinguish the shallow geological edges from the deep ones. Combining with the TDR, we identify the sub-circular boundaries with different depth indications from the various lineament results. Fourth step: THDR exhibits relatively low resolution with wide-band peaks but its maximum values correspond to both the sub-circular boundaries and the sub-linear boundaries (Ekinci et al. 2013 ). As the THDR is less sensitive to noise which can produce additional spurious boundaries, we use the THDR to verify the above results. Final step: Combined with the geological map, the RTP magnetic anomaly and other useful information like the geography or morphology, we present the results and their enhancements of the horizontal distributions of subsurface rocks and structures. 5. Results and Discussion 5.1. Initial Results In this section, we present the initial results of the automatic edge detection methods described in section 4.1 specifically the vertical derivative, total horizontal derivative, analytic signal amplitude and tilt derivative of the RTP magnetic anomaly in the Mandula and Sonid Youqi area of the Inner Mongolia Autonomous Region of China - see Fig. 3 (a)-(d). Compared with the RTP magnetic anomaly, though there are some influences from noise which are mainly caused by flight lines in the initial data and incomplete levelling, the VDR and THDR results present details on both the sub-circular boundaries and the sub-linear boundaries, the ASA result shows more sub-circular boundaries and the TDR result presents abundant information on both sub-circular and sub-linear boundaries. All edges are located at the northeast, the northwest, the central west, and the south parts of the study area. The edges at the central west part are dominated by sub-circular features geological sources and the central south is dominated by the sub-linear features. However, as the interpretation of all results is not immediately obvious, further analysis is needed to identify subsurface rocks and structures. To better understand the edge detection, we manually drew edge lines from the above auto edge detection and the results are shown in Fig. 4 (a)-(d). Of all the methods, the TDR (Fig. 4 (d)), whose edges are accurate and easy to track by zero values, presents the most complete edge detection results from all boundary shapes and all depths of sources. The VDR (Fig. 4 (a)), whose edges are also accurate and easy to track by zero values, identifies fewer features than the TDR. The ASA (Fig. 4 (c)) can identify sub-circular boundaries with different depth indications. The THDR (Fig. 4 (b)) presents different depth identification for both the sub-circular and sub-linear boundaries. 5.2. Final Results The results of the identification of the two types of geological feature boundaries shown in Fig. 5 have been obtained from combined analysis according to the steps of Sections 4 and 5.1 . We first overlapped all edge detection results in Fig. 5 (a) and from results in Fig. 4 (a)-(d), we can see faults are mostly northwest-southeast, northeast-southwest and east-west trending. Secondly, according to the first four steps of Section 5.1 , we retained inferred boundaries of anomalous geological sources on the geological map ignoring repeated boundaries and they are shown in Fig. 5 (b). In Fig. 5 (b), boundaries circled by red dash lines are isolated edges which are detected only once by the four methods thus they are ignored in the final results. Boundaries surrounded by red dash rectangles and thumbtacks are interpreted to be human “cultural” anomalies and as such are ignored. Boundaries circled by sky-blue solid lines are confidently interpreted as real edges based on the morphology of river changes and the outcrop of the lithology variation. Thirdly, we drew the accepted boundaries of geological rocks and structures on the RTP magnetic anomaly map -of Fig. 5 (c). For the sub-linear boundaries of anomalous geological layer and fault structures, considering the geological and tectonic background (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ), the anomalous geological fault structures of the study area obtained by our edge detection results can be divided into main structures and ultra-deep structures. The ultra-deep structures of this area need further study and confirmation. Most locations of highly magnetic geological structures are accompanied by faults, some of which are distributed in the middle south, west and north. The main structures, based on the basement tectonics, have gradually evolved and been modified through distinct stress field environments. The main structures can be specifically subdivided into basin-forming structures during the sedimentation of the faulted basins of the Early Cretaceous and new tectonic structures after the activities of the Paleogene according to the surrounding tectonic activities (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). Each basin or sub-basin has developed through specific structures (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). Thus the directions of the depressions align with the directions of the syndepositional faults, predominantly northeast-southwest. The structural forms of the depressions can be divided into two types: half-grabens and more symmetric grabens, often with faults as their boundaries on both sides. The new tectonic structures of the study area were manifested as wide and gentle pleated structures and fault structures (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). There are notable variations in the direction and manifestation of the structures in our results - see Fig. 5 (c). In the southeastern part of the study area, wide and gentle pleated structures are locally developed, which have a nearly east-west direction; The northwest part is mainly characterized by northwest-southeast trending fault structures. For the sub-circular boundaries of anomalous geological rocks and blocks, apart from rocks of magnetite, garnet magnetite quartzite and iron-bearing quartz veins, there are various highly magnetic igneous rocks including andesite, basalt, diorite, altered diorite, diorite-porphyrite, gabbro, gabbro diorite, allgovite. The former rocks can not be easily determined by our magnetic anomaly (a 125 m×125 m grid) as their thicknesses are commonly less than 100 metres, considering the general geometries of these high magnetic rocks ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ) and their formation periods (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). Andesite forms volcanic cones, thick lava flows and domes ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ), which may be identifiable. The blocky andesite in the Jurassic of the Erlian Basin is the main volcanic rock and may alternate with other igneous rocks such as tuff and basalt, reflecting the complexity of the volcanic activity (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). Basalt forms thin lava flows, intrusive dikes and sills ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ), which may be hard to identify. The basalt of the Erlian Basin mainly formed during the Permian and Carboniferous periods and may be related to the activities of the basin tectonics and mantle hotspots; it may alternate with sedimentary layers, reflecting the interactive influence of volcanic activity and sedimentation ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). Diorite and altered diorite (not granodiorite or quartz diorite) rarely form independent blocks but are often associated with basic, acidic or alkaline rocks ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). If independent rocks of diorite, altered diorite and gabbro diorite are formed, they are only rock stocks, sills, veins or irregular small intrusive bodies ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). It may be hard to identify diorite and altered diorite. Diorite-porphyrite often presents as rock sills and walls, or as the edge phase of diorite ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). The formation of diorite porphyry in the Erlian Basin is related to magmatic activity in the shallow crust (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). Gabbro can be found in various tectonic environments and often forms intrusive bodies of varying sizes, such as lopolith, rock caps and sills ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). The lateral scale of gabbro varies greatly, ranging from a few kilometres to hundreds of kilometres in size, appearing in various geological periods ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). The gabbro in the Erlian Basin may have formed during periods of crustal extension or tectonic activity, which provided favorable conditions for the uplift and accumulation of basic magma (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). The large scale of gabbro can be identifiable in the magnetic data. Gabbro diorite often presents as rock stocks and droplets ( Hollocher 2017 ; Dorrik 2005 ; Jerram and Petford 2011 ; Philpotts and Ague 2022 ). The gabbro diorite in the Erlian Basin is generally penetrated and transformed by intrusions. The surface outcrop is limited, and the overall exposed areas range from 1 to 30 km 2 , which may be identifiable in the magnetic data (Qi et al. 2015 ; Zhang et al. 2019 ; Zhao et al. 2011 ). Allgovite also called diabase porphyrite is mostly in the form of rock sills or walls, which also may be hard to identify. The formation of allgovite is related to basic magmatic activity in the shallow crust, and the allgovite in the Erlian Basin may be closely related to regional tectonic activity and basic magmatic activity. Combined with the above information, interpretation and analysis, we obtained the final result of the identification of the sub-circular boundaries of anomalous geological discrete bodies of rocks and blocks and the sub-linear boundaries of anomalous geological structures of layers and faults. The result is shown in Fig. 5 (d). 5.3. Discussion As thin and tiny anomalous geological layer structures can not be determined by our 125 m×125 m magnetic anomaly grid, we mainly discuss the anomalous geological structures of thick layers or magnetic faults for the sub-linear boundaries. The results and information obtained from the aforementioned critical analysis were integrated with the known geologic features to construct the predominant tectonic elements affecting the basement configuration in the study area (Fig. 6 ). 5.3.1. Sub-circular boundaries of anomalous geological rocks and blocks The study area is located within a sedimentary rock-covered region, where extensive Mesozoic and Cenozoic sedimentary layers are widely distributed. Field investigations revealed that the exposed Mesozoic and Cenozoic rocks and strata show no or very low magnetic susceptibility. Therefore, we conclude that the cause of the sub-circular magnetic anomalies is the concealed magnetic geological discrete bodies beneath the sedimentary layers. According to the magnetic susceptibility statistics table, we believe that the concealed magnetic geological sources in this area can be roughly divided into three categories: magnetic basement, concealed intrusive rock, and volcanic rock. Field surveys found that the Proterozoic magnetite quartzite has the strongest magnetic susceptibility, however, magnetite quartzite is mostly deeply buried as basement rock and only appears sporadically outside the study area. Therefore, the concealed magnetic geological sources consisting of magnetite quartzite cannot cause local high magnetic anomalies and they are considered not to be the primary cause of the sub-circular boundaries of magnetic anomalies discussed in this paper within the study area. Field surveys found multiple phases of magmatic intrusion along channels formed by intrusive rocks within the strata. These studies revealed high magnetic susceptibility intrusive rocks in the study area, including gabbro, diabase, diabase porphyrite, and gabbro-diorite. These four types of intrusive rocks are considered likely to constitute the concealed magnetic geological discrete bodies within the study area. Field investigations also found that gabbro and gabbro diorite, as plutonic rocks, typically occur as small-scale rock sills and laccoliths, characterized by deep burial and limited scale. Therefore, we infer that they are unlikely to be the main cause of sub-circular feature boundaries of magnetic anomalies. In contrast, diabase and diabase porphyrite, being hypabyssal rocks, are commonly produced in the form of rock sills or dikes, with shallow burial depths and large scales. They generally have unconformable contact with the surrounding rock and their spatial distribution patterns are often controlled by fault structures. Field surveys found basalt and basaltic andesite within the non-magnetic or weakly magnetic Mesozoic strata exhibit high magnetic susceptibility. The magnetic susceptibility contrast between these volcanic rocks and sedimentary strata can cause intense local magnetic anomalies, potentially generating strip-like or block-like sub-circular boundaries of magnetic anomalies. We consider that these two types of volcanic rocks are likely to constitute the concealed magnetic geological bodies within the study area. 5.3.2. Sub-linear boundaries of anomalous geological layer and faults Based on the spatial distribution of our results, we can categorize the sub-linear magnetic anomalies in the study area into three groups of fault structures: NE (northeast) trending (F2, F3, F4, F5, F6, F7, F13), ENE (east-northeast) trending (F8, F9, F10, F11, F12), and NW (northwest) trending (F1). Previous studies have suggested that the NW trending F1 fault marks the boundary between the NE trending and NEE trending structures in the region. Our results identified the F1 fault and show that the magnetic anomaly characteristics associated with the F1 fault exhibit abrupt changes and displacements in the middle to eastern segment of the fault, while the western segment acts as a boundary line and linear gradient zone between distinct magnetic fields. The orientation of the sub-linear magnetic anomalies changes significantly between the southeastern and northwestern parts of the fault, with the southwestern part dominated by ENE trends and the northwestern part by NE trends. We further infer that the fault F1 has left-lateral displacement. Further, according to the magnetic anomalies and geological information, we believe that this fault formed most recently, as it shows displacement of other faults in the study area. The F2 and F3 faults are located on both sides of the Jiang’an ranch. It is obvious that the F2 fault forms a distinct boundary between different magnetic features. The area between the F2 and F3 faults shows local magnetic anomalies superimposed on a quiet positive background field, corresponding to the uplift zone in the regional structural framework. Therefore, we infer that the local anomalies between the F2 and F3 faults may be caused by concealed basic dikes and subvolcanic rocks. The map shows that the F3 fault is influenced by concealed basic intrusive rocks near Baiyan Aobao, with its extension becoming less apparent toward the southwest. The northeastern part of the F3 fault exhibits a quiet negative background field and we infer this to be a deeply buried non-magnetic Late Paleozoic basement, consistent with the depression structure in the regional structural framework. Based on our results, we conclude that the fault group of F2 and F3 represents boundaries between different structures, and the F3 fault is likely to control the orientation of the depression. The F4 fault represents a boundary between different magnetic field patterns and a linear gradient zone. The area between F4 and F3 faults, also is similar to the area northwest of the northeastern part of the F3 fault, which we have interpreted as a deeply buried non-magnetic Late Paleozoic basement. The region between the F4 and F5 faults is the northern margin of the Sunite Uplift, characterized by positive magnetic anomalies superimposed on a slightly negative background field. Therefore, we infer this region to be composed of a Paleozoic basement and shallow concealed basic intrusive rocks. Similar to the F3 fault, we suggest that the F4 fault is likely a depression-controlling fault, governing both the orientation of the depression and its sedimentation patterns. The F5 and F6 faults are NE trending, located on either side of Yihewusu, respectively. The area between the F5 and F6 faults exhibits a slightly negative background field. We interpret this area to be a deeply buried non-magnetic Late Paleozoic basement. The F5 and F6 faults and the area between them correspond to the depression structure in the regional structural framework. Therefore, we conclude that the F5 and F6 faults control the formation and evolution of local depressions within the uplift. Based on the aforementioned tectonic setting, the Erlian Basin exhibits a "two-depression-one-uplift" structural pattern striking NE, with the Sonid Uplift in the central part flanked by depression zones on both sides. This regional "alternating uplift-depression" structural pattern corresponds well with our interpreted F2, F3, F4, F5, and F6 faults. This correlation not only validates our lineament identification results but also indicates that such structural patterns are distinctly reflected in the magnetic anomaly of the study area. The area between F6 and F7 faults is characterized by positive magnetic anomalies superimposed on a negative background field, suggesting a composition of Paleozoic basement and shallow-buried basic intrusive rocks. We interpret that the NE trending F7 fault, together with F6, controls the spatial configuration of the uplift structure. The F8, F9, F10, F11, and F12 faults within the southern part of the F1 fault constitute a set of sub-parallel fractures striking ENE. The magnetic characteristics of the region containing F8-F12 faults manifest as a series of parallel ENE trending sub-circular and sub-linear magnetic anomalies superimposed on a negative background field. We interpret this pattern to indicate the presence of concealed basalt or basaltic andesite within non-magnetic Paleozoic basement and fault-controlled shallow non-magnetic strata. The F13 fault also trends NE, and its magnetic characteristics exhibit a weak positive magnetic anomaly within a negative background field. We interpret that the F13 fault has an inherited relationship with the F7 fault and represents a later-formed shallow fault. While the F13 fault has displaced the concealed magnetic geological discrete bodies within the strata, it has not altered the regional tectonic framework. Apart from the F1 fault, all other identified lineaments (F2-F13 faults) exhibit NE or ENE trends. This finding further confirms that the depression structures in the study area are controlled by NE-ENE trending faults, forming a structural framework characterized by parallel or en echelon arrangements. We infer that these fault structures, from F2 to F13, faults also control the orientation of most concealed magnetic geological discrete bodies within the area. From a tectonic evolution perspective, this also validates that the majority of small to medium-sized depression structures in the study area display prominent inheritance patterns, with limited neotectonic development. 5.3.2. Uncertainty of our identification results We may infer that some highly magnetic geological blocks composed of rocks such as andesite, gabbro and gabbro diorite can be identified by the edge detection of the sub-circular feature boundaries using our 125 m×125 m magnetic anomaly grid. However, as the formation geometries of the andesite (volcanic cones and domes), gabbro (basins and caps) and gabbro diorite (stocks) all can be the sub-circular feature boundaries of anomalous geological blocks and there is a lack of evidence from local outcrop to identify thin rock layers and less magnetic rocks, further studies are needed to recognize other specific rock types. Additionally, as no methods are perfect, the four methods we used here may have disadvantages, which may make our results imprecise. For example, the VDR is affected by the burial depth and inclination of the geological bodies and the magnetization direction (Wang et al. 2010 ); The THDR is influenced by the burial depth and inclination of the geological discrete bodies (Wang et al. 2010 ); The TDR produces redundant edges when both positive and negative anomalies exist (Liu et al. 2023 ); Though the ASA appears to be less susceptible to false edges, if the ratio of the depth to the width of the geological discrete bodies is high, the ASA’s maximum values lie on the top of the body instead of the edges, which may cause misinterpretation (Liu et al. 2023 ). 6. Conclusions The study region of the Mandula and Sonid Youqi in Inner Mongolia, which is situated in the centre of the Daxing'anling multi-metallogenic zone is a potential site of ore deposits, however, it is located in the south of the Erlian Basin and largely covered by Cenozoic sediment. Through the aeromagnetic survey and our rigorous integrated interpretations and combined analysis, we have unraveled some high magnetic susceptibility bodies and the subsurface structures which would dominate mineral deposits. The geological discrete bodies and structures discovered are predominantly aligned east-west and northeast-southwest and partly northwest-southeast. Through our identifications of geological discrete bodies and structures which stand as a pivotal facet of investigation and comparison with the geological background, we trace these lineaments, unravelling their spatial distribution characteristics to potentially help elucidate the local geological evolution. Moreover, the identification of sub-circular features of anomalous geological rocks and blocks and sub-linear features of geological layers and fault structures by our combined analysis provides a unique window into the subsurface architecture. It should be particularly useful in the early stages of exploration in greenfield areas where geological and geophysical information is sparse. Different edge enhanced methods have various resolutions. Some methods only respond well to large-scale and deep-buried structures, but others can resolve small-scale structures and are more tolerant of noise. We suggest a comparative analysis, combining the similarities and differences between the results of different edge detection methods: VDR, THDR, ASA and TDR. These robust methods are used to depict the two key types of boundaries, the sub-circular and sub-linear features, the low-resolution methods are used to identify deep or large-scale boundaries, and the high-resolution methods are used for secondary confirmation to exclude possible spurious boundaries and to further determine the shallow or small-scale boundaries. Our comprehension of the geological framework in the Mandula and Sonid Youqi region makes a difference in targeting geological bodies and structures which are meaningful for the future exploration of mineral resources. High-resolution and multi-approach geophysical surveys, harmonized with pinpoint geological sampling, have the potential to provide even finer interpretations of subsurface geological and lithological variations, which would greatly improve the finding of ore deposits. Declarations Conflicts of Interest: The authors declare no conflict of interest. Funding: This research was funded by the National Natural Science Foundation of China, grant numbers 42374172, the Fund from the SinoProbe Laboratory, grant number JKYZD202303, and the China Scholarship Council, grant number 202004180027. Author Contribution Conceptualization, C. Z. and J. G.; methodology, C. Z., C. G., J. Y. B. X. and F. L.; software, C. Z., J. G., J. J. and F. L.; validation, C. Z., C. G., J. J., M. C., B. X. and Z. Y.; formal analysis, C. Z., C. G., J. J., M. C. and Z. Y.; investigation, J. G., M. C. and Z. Y.; resources, J. G.; data curation, J. G. and B. X.; writing—original draft preparation, C. Z., C. G., J. Y., M. C., J. G., F. L., Z. Y. and F. L.; writing—review and editing, C. Z., C. G., B. X., J. Y. and F. L.; visualization, C. Z. and J. Y.; supervision, C. Z. and J. G.; project administration, C. Z., J. G. and J. Y.; funding acquisition, C. Z., J. G. and J. Y. All authors have read and agreed to the published version of the manuscript. Acknowledgement The authors extend their gratitude to all those who contributed to this research, especially to the data collection team. The authors also express their deepest appreciation to Genesis Obero and the other editors for their invaluable assistance, and thank the anonymous reviewers for their constructive comments and valuable suggestions. Data Availability Statement: The data are not applicable to this paper. References Baranov, V. 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Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Nov, 2025 Editor assigned by journal 23 Sep, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 20 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":136059,"visible":true,"origin":"","legend":"\u003cp\u003e(A) A simplified geological map of outcrop rocks in the Mandula and Sonid Youqi area (modified from Tang et al. 2022; Pang et al. 2019; Pang et al. 2017); (B) A tectonic map in which the location of the study area is represented as a red box.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/f2c062c49d4712390dfff811.jpg"},{"id":97346609,"identity":"f7f4d41b-5be4-4f85-a980-0d983c16a8db","added_by":"auto","created_at":"2025-12-03 11:49:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191566,"visible":true,"origin":"","legend":"\u003cp\u003eMagnetic anomalies of the study area: (\u003cstrong\u003ea\u003c/strong\u003e) The original magnetic anomaly after processing; (\u003cstrong\u003eb\u003c/strong\u003e) The RTP magnetic anomaly from (a).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/640e3971a4fa88480c32f7b7.jpg"},{"id":97346603,"identity":"f4c0ed91-4fe5-4689-b4af-fe00e95d2fc1","added_by":"auto","created_at":"2025-12-03 11:49:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":338170,"visible":true,"origin":"","legend":"\u003cp\u003eAutomatic edge detection of the RTP magnetic anomaly of the study area: (\u003cstrong\u003ea\u003c/strong\u003e) The vertical derivative; (\u003cstrong\u003eb\u003c/strong\u003e) The total horizontal derivative; (\u003cstrong\u003ec\u003c/strong\u003e) The analytic signal amplitude; (\u003cstrong\u003ed\u003c/strong\u003e) The tilt derivative.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/9683b8bd94de51324e8cf5af.jpg"},{"id":97346605,"identity":"d283efaf-ff77-40a1-99c2-611704e7d61c","added_by":"auto","created_at":"2025-12-03 11:49:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177715,"visible":true,"origin":"","legend":"\u003cp\u003eManual edges from results of Figure 4: (\u003cstrong\u003ea\u003c/strong\u003e) The zero value lines of the vertical derivative represented by black lines; (\u003cstrong\u003eb\u003c/strong\u003e) The positive extreme line of the total horizontal derivative, the magenta lines represent large values and the green lines represent small values; (\u003cstrong\u003ec\u003c/strong\u003e) The positive extreme block circle of the analytic signal amplitude, the magenta lines represent large values and the green lines represent small values; (\u003cstrong\u003ed\u003c/strong\u003e) The zero value lines of the tilt derivativerepresented by black lines. Magenta lines are expected to represent shallower sources than the green lines.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/67a87e1bc2bc38e8ebb0bc7a.jpg"},{"id":97369993,"identity":"828fa10e-941d-4a6f-94e0-b2c85901669a","added_by":"auto","created_at":"2025-12-03 16:26:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":262101,"visible":true,"origin":"","legend":"\u003cp\u003eFinal edge identification results: (a) All edge detection results overlapped from results in Figure 4 (a)-(d); (b) Retained inferred boundaries of anomalous geological sources on the geological map ignoring repeated boundaries; (c) Accepted boundaries of geological rocks and structures on the RTP magnetic anomaly map; (d) A final result of the identification of the circular boundaries of anomalous geological blocks and the linear boundaries of anomalous geological layer and fault structures.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/0f9ef2c0d0a1fabcd074c44e.jpg"},{"id":97346611,"identity":"21f5773e-97ea-4493-b6d6-9a7afe3139a4","added_by":"auto","created_at":"2025-12-03 11:49:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":197500,"visible":true,"origin":"","legend":"\u003cp\u003eAn inferred geological map with edge detection combined analysis.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/2378423a9a1afdde3c097f9a.jpg"},{"id":97892822,"identity":"4459c97e-ff32-4497-953f-84ec202ab26a","added_by":"auto","created_at":"2025-12-10 15:22:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2376592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6937737/v1/faef39de-19b7-4ba7-83c4-94978ec34409.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping and Identifying Two Types of Geological Features from Enhanced Derivatives of Aeromagnetic Data: A Case Study from Northwest Inner Mongolia","fulltext":[{"header":"Article Highlights","content":"\u003cp\u003eWe proposed a scheme to identify both sub-circular and sub-linear geological features using aeromagnetic data.\u003c/p\u003e\n\u003cp\u003eWe demonstrate the approach which analyses multiple existing edge enhancement techniques.\u003c/p\u003e\n\u003cp\u003eResults from a sedimentary cover part of Inner Mongolia provide important geological and mining insights.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe Mandula and Sonid Youqi region, located in west of the Erlian Basin of the northwest of the Inner Mongolia Autonomous Region of China, has been of interest to geoscientists due to its potential mineral resources, as this area is a significant component of the Daxing'anling Metallogenic Province, whose south part is adjacent to the North China Metallogenic Province, and it forms part of the Fe-Cu-Mo-Pb-Zn-Cr-(Au-Mn)-Ge-Coal mineralization zone of the Bainaimiao and Xilinhot areas (Xu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). However, it is difficult to find any mineral deposits directly here as the majority of the study area is covered by sedimentary strata and it is hard to carry out geological fieldwork; only a few geological field studies have been carried out in the areas surrounding but outside the study area (Tang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous regional tectonic frameworks of this area based on geological field studies were presented as similar, but the local geological rocks and structures which are critical to ore deposits were mapped as different from each other (Tang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These diverse understandings of local geological rocks and structures also aggravate a complicated situation of no discoveries of mineral deposits in the study area.\u003c/p\u003e\u003cp\u003eGeophysical methods can aid the understanding of subsurface rocks and structures for local sedimentary cover areas (Zhang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), however, no seismic or electrical field studies have been carried out in the study area of the Mandula and Sonid Youqi region. As one cheap, primary and key geophysical method, aeromagnetic surveys were implemented over the study area. Aeromagnetic surveys provide insights into subsurface rocks and structures and hence mineral exploration as magnetic anomalies are caused by secondary magnetic fields produced by various rocks and layers based on the Earth's past and present magnetic field (Isles and Rankin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, aeromagnetic surveys provide subsurface information based on more than one line or profile as they are surface-coverage surveys (Isles and Rankin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo better identify rocks and geological structures, enhancement methods have been introduced to interpret magnetic anomalies to detect structures and edges of anomalous geological bodies; this can aid in locating mineral deposits (Liu 2023; Cascone 2017; Pilkington \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pilkington \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Blakely \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Derivative-based edge detection methods are major options for these enhancements as they are mostly high-pass filters to sharpen structures and boundaries of anomalous geological bodies which are represented by extreme (local high or low) or zero anomaly values (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cascone et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pilkington \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pilkington and Keating \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Blakely and Simpson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The sharpened edges indicate various structures and boundaries of anomalous geological bodies, including the closed-loop boundaries such as sub-circular (or sub-rectangular) features of discrete bodies of granites (or kimberlites) and fault blocks, and the curvilinear-segment edges such as sub-linear features caused by faults and anomalous layers. These two types of sub-circular and sub-linear geological boundaries have been proven effective in mineral resource discoveries (Isles and Rankin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but these two types are related to different mineral systems, like the sub-linear feature boundaries relating to Granite Sn-W-Fe deposits controlled by linear trending fold and fault structures induced by the plate window in the upwelling area, and the sub-circular feature boundaries relating to porphyry Cu-Mo deposits controlled by anticlines, pipes and oval bodies caused by the tear-off of the subducting slab (Mao et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lowell and Guilbert \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1970\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor sub-circular feature boundary detection, Cooper and Cowan (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) applied the Hough transform (Hough 1962; Wang and Howarth \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) to detect circular features from potential field anomalies caused by kimberlite pipes or meteorite impact craters. Keating and Sailhac (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) used the analytic signal to identify circular magnetic anomalies caused by kimberlite pipes. Cooper and Cowan (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) used textural analysis to locate these features in geophysical data. Kr\u0026oslash;gli and Dypvik (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) presented an automatic search algorithm to detect circular impact structures. Holden et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) presented an automatic image analysis method called radial symmetry transform to find circular magnetic anomalies of an idealised porphyry mineral system.\u003c/p\u003e\u003cp\u003eFor linear feature boundary enhancement, Wang and Howarth (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) applied the Hough transform (Hough, 1962) to detect linear faults from Landsat TM images. Sykes and Das (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) used a directional filter based on 2D Radon transforms to enhance lineaments. Dentith (2000) and Holden et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) presented an automated analysis of magnetic data using texture analysis based on a grey-level co-occurrence matrix to identify linear features of gold lode deposits.\u003c/p\u003e\u003cp\u003eIt is vital to identify the two types of geological boundaries for targeting exploration for different ore deposits. However, most methods are difficult to detect and distinguish like in highly active magnetic relief (Cowan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The sub-linear or sub-circular features for mineral systems are not purely linear or perfectly circular, but generally curvilinear-segment representing dykes and faults, or closed-loop (approximately circular or rectangular) outlines relating to isolated anomalous bodies. Additionally, most developments of derivative-based edge enhanced methods have been focused on improving algorithms to meet higher demands of detecting and sharping boundaries (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cascone et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pilkington \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pilkington and Keating \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Blakely and Simpson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), instead of analyzing the results produced and combining them to increase the geological understanding of exploration for different ore-deposits. There are gaps between the pure algorithm development of edge enhanced methods and the practical geological understanding of edge detection results. Therefore, semi-automatic analysis, using integrated filters and combined analysis, rather than mathematical treatment, using purely specialized filters which could easily and directly identify sub-linear or sub-circular features which are not perfectly straight or circular, can provide objective information.\u003c/p\u003e\u003cp\u003eOur study aims to enhance the identification of the two types of geological feature boundaries from a combined analysis of the edge enhanced results of the magnetic anomalies in the sedimentary cover Mandula and Sonid Youqi area of the Inner Mongolia Autonomous Region of China. By employing four edge enhanced techniques and integrating their boundary results, we seek to identify and characterize horizontal distributions of sub-linear and sub-circular features of anomalous geological rocks and structures under sedimentary cover. The remainder of this paper is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a geological review contextualizing our study within the existing geological knowledge. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the methodology of the magnetic anomaly edge enhancement and outlines the steps taken in the combined analysis and interpretation of the results. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents our enhancing results in horizontal distributions of anomalous geological rocks and structures. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5\u003c/span\u003e offers a conclusion, summarizing the key insights gained from our study.\u003c/p\u003e"},{"header":"2. Geological Setting","content":"\u003cp\u003eThe majority of the study area is located in the Erlian Basin of Inner Mongolia. It is geographically to the west of Sunite Youqi of Xilingol League, to the south of Erenhot City of Xilingol League, to the north of Siwangziqi of Ulanqab City, and to the east of Darhan-Muminggan Lianheqi of Baotou City.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Lithostratigraphy and magmatic rocks\u003c/h2\u003e\u003cp\u003eIn the study area, the Cenozoic Erathem are widely developed, with Paleogene, Neogene and Quaternary at the surface, covering almost 90% of the total exposed area. Additionally, only a small amount of Silurian and Cretaceous strata are exposed (Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The details can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As regards highly magnetic rocks, igneous rocks such as andesite, allgovite, basalt, diorite, gabbro, and porphyrite likely have high magnetisation see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eA lithological table of the study area\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(modified from Tang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pang et al. 2019; Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), in which the break lines \u0026lsquo;ํ\u0026rsquo; represent discontinuity, and the jagged lines \u0026lsquo;๏\u0026rsquo; represent stratigraphic unconformity.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErathem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSystem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSeries\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLithology\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCenozoic Erathem (Cz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuaternary System (Q)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaeolian, modern alluvial, flood, and lacustrine sand, gravel and silt, sand and gravel layers, sandy clay and calcareous argillaceous sand layers, gravel-bearing coarse sandstone, silt, etc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeogene System (N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebrownish-red silty mudstone, gravel-bearing coarse sandstone, greyish-white glutenite, conglomerate, and usually with visible calcareous concretion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePaleogene System (E)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOligocene (E3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egreyish-white gritstone, glutenite and yellowish-green mudstone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEocene (E2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003egreyish-green mudstone, greyish-white fine-grained sandstone, and gritstone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePaleocene (E1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ered and variegated sand-mudstone formations from rivers-lakes sedimentary, containing abundant mammalian fossils\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMesozoic Erathem (Mz)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCretaceous System (K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLower Cretaceous (K\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emudstones and sandy conglomerates and locally interbedded with oil shale, marl, breccia and brown coal.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSilurian System (S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emetamorphic sandstone, phyllite, and quartzite, constituting a metamorphic rock series of greenschist facies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Silurian System (S) is exposed on a small scale in the southeast and mainly peripheral regions of the study area. Its lithology is characterized by metamorphosed sandstone, phyllite, and quartzite, constituting a metamorphic rock series of greenschist facies (Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Cretaceous System (K) is dominated by the Lower Cretaceous (K\u003csub\u003e1\u003c/sub\u003e) and has limited distribution in the south of the study area with significant sedimentary thickness. The major outcrops mainly consist of a set of sedimentary strata of fluvial and lacustrine facies, composed of three sedimentary cycles of clastic rocks from bottom to top: coarse fine coarse. Its lithology mainly consists of mudstones and sandy conglomerates and locally is interbedded with oil shale, marl, breccia and brown coal (Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Paleogene System (E) is well-developed and widely distributed, with relatively small sedimentary thickness. The surface mainly exposes mudstone, siltstone, fine sandstone, gritstone, and glutenite. Overall, the sedimentary processes of the three sets of Paleogene strata are dominated by meandering rivers and floodplains, with a general sedimentary direction from south to north. The Paleocene (E1) of the Paleogene System (E) distributed in the central and northern region of the study area, with relatively wide exposure, is a set of red and variegated sand-mudstone formations from rivers and lakes, containing abundant mammalian fossils. The Eocene (E2) is exposed relatively little. The lithology is greyish-green mudstone, greyish-white fine-grained sandstone, and gritstone. The lithology of the widely distributed Oligocene (E3) consists of greyish-white gritstone, glutenite and yellowish-green mudstone (Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Neogene System (N) is widely exposed in the east of the study area, and the lithology mainly consists of brownish-red silty mudstone, gravel-bearing coarse sandstone, greyish-white glutenite, and conglomerate, usually with visible calcareous concretion (Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Quaternary System (Q) is mainly exposed in the northwest and east of the study area, and is dominated by aeolian, modern alluvial, flood, and lacustrine deposits (aeolian sand, sand, gravel, silt). It mainly consists of sand and gravel layers, sandy clay and calcareous argillaceous sand layers, gravel-bearing coarse sandstone, silt, etc (Jingwen et al. 2012; Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The intrusive rocks are acidic granite locally exposed in the southeast of the study area, mostly occurring as small intrusive bodies or rock stocks (Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Structural Setting\u003c/h2\u003e\u003cp\u003eGeographically, our study area is located in the southwest of the Erlian Basin and the Erlian Basin is situated on the Inner Mongolian Plateau, north of the Yinshan Mountains and west of the Greater Khingan Mountains. In the regional tectonic setting, the Erlian Basin where our study area is located is a Mesozoic continental basin with structural complexity developed in the Xingmeng Orogenic Belt. Our study area has undergone development of a series of northwest and west-northwest trending grabens and half-graben faults during the Mesozoic era, with lithofacies controlled by contemporaneous normal faults or strike-slip normal faults and significant lateral variations (Tang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lowell and Guilbert \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). From the perspective of basin structure and tectonic sedimentary evolution, our study area exhibits the characteristics of a rift basin and the Early Cretaceous was the primary period of its rifting (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Data","content":"\u003cp\u003eThe vast majority of the study area is in a plain, with a small portion having low mountains and hills. Due to the flat terrain and little elevation change, field surveys should be relatively straightforward with the use of off-road vehicles, however, many areas are pastoral areas, which can only be entered by local residents. Thus, aeromagnetic surveys are much more convenient and efficient.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Aeromagnetic Data\u003c/h2\u003e\u003cp\u003eThe aeromagnetic data were obtained from the Airborne Survey and Remote Sensing Center of the Nuclear Industry of China and initially collected by a CS-3 high-precision cesium magnetometer with a sensitivity of 0.0006 nT/(Hz)\u003csup\u003e^(1/2)\u003c/sup\u003e mounted on the tail of a Cessna-208B fixed wing aircraft. The magnetic data compensation was performed by a real-time MMS-4 automatic magnetic digital compensation instrument. The base station equipment was a G-858SX cesium magnetometer with a sensitivity of 0.003 nT. The average height of the flight was 99 m and the average speed of the flight was 226 km/h (about 62.78 m/s). The sample rate was 10 times/s and line space was 500 m. Testing before equipment installation included consistency, stability, steering difference and step response rise time tests, and the testing and calibration after equipment installation included ground static noise testing and aircraft compensation, the total accuracy of the magnetic measurement was 1.70 nT after adjustment \u0026ndash; based on repeat and crossing lines.\u003c/p\u003e\u003cp\u003eThe above measured magnetic data consist of the internal magnetic field caused by the geological rocks and structures above the Curie isotherm, the interior magnetic field originating in the Earth\u0026rsquo;s core, the external magnetic field due to the currents flowing in the ionosphere and errors from the measurement (Li and Nabighian \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As we are most interested in magnetic variations in the lithosphere, the relative variation of the total field magnetic intensity, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\text{T}\\)\u003c/span\u003e\u003c/span\u003e, called the magnetic anomaly hereon, which is the difference between the total field magnetic intensity and the total field normal geomagnetic intensity, reflecting the horizontally and vertically distributed magnetic geological bodies, is our objective data or anomaly to be used. To obtain the magnetic anomaly \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\text{T}\\)\u003c/span\u003e\u003c/span\u003e, magnetic data corrections should be applied such as correction for the Earth\u0026rsquo;s normal magnetic field, correction of magnetic diurnal variation, correction of flight altitude, hysteresis correction and levelling (Isles and Rankin \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo facilitate processing and display, the magnetic anomaly data were gridded at a cell size of 125 m\u0026times;125 m using the minimum curvature method in Geosoft\u0026rsquo;s Oasis Montaj. The magnetic anomaly in the Mandula and Sonid Youqi area of Inner Mongolia is shown in Fig.\u0026nbsp;2a.\u003c/p\u003e\u003cp\u003eTo better correlate the magnetic anomaly with causative geological bodies and structures, thus aiding the interpretation, the reduction-to-pole (RTP) of the magnetic anomaly (called RTP magnetic anomaly hereon) shown in Fig.\u0026nbsp;2b was generated using an inclination of and a declination of to remove the skewness of the magnetic anomaly in Fig.\u0026nbsp;2a using the MAGMAP menu of Oasis Montaj (Hinze et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Magnetic Susceptibility Statistics of Major Rock Types\u003c/h2\u003e\u003cp\u003eAs most of the study area is under sediments, measurements of rock magnetic susceptibility include two groups: one is the on-site measurement of outcrop bedrock within the study area; the other is measurements made on similar rocks outside the study area. General values of rock magnetic susceptibility in the study area and its surrounding areas are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMagnetic susceptibility statistics of major rock samples in the study and its surrounding areas.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrata\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLithology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample Number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMaximum Susceptibility (\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5 SI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum Susceptibility (\u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5 SI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage Susceptibility (\u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5 SI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuaternary (Q)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSandy Clay and Gravel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeogene (N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMudstone, Silstone, Glutenite, Silty Mudstone, Conglomerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaleogene (E)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMudstone, Siltstone, Gritstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCretaceous (K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMudstone, Sandstone, Conglomerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJurassic (J)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTuff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJurassic (J)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAndesite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePermian (P)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimestone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePermian (P)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBasalt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePermian (P)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluorite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarboniferous (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimestone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarboniferous (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIron-bearing Quartz Vein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarboniferous (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBasalt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSilurian (S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlate, Schist, Metamorphic Siltstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProterozoic (Pt)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarble, Quartzite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProterozoic (Pt)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProterozoic (Pt)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMagnetite, Garnet Magnetite Quartzite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcidic Rock (γ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGranite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcidic Rock (π)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGranite Porphyry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate-felsic Rock (γδ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGranodiorite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate-felsic Rock (δο)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuartz Diorite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate Rock (δ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAltered Diorite, Diorite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntermediate Rock (δ\u0026micro;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiorite-porphyrite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Rock (ν)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGabbro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Rock (νδ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGabbro Diorite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Rock (β\u0026micro;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAllgovite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Edge Detection Methods for Magnetic Anomaly\u003c/h2\u003e\u003cp\u003eIn this section, we chose four magnetic anomaly edge enhanced methods which are all applied to the grid of RTP data: vertical derivative, total horizontal derivative, analytic signal amplitude and tilt derivative. All these methods are used in the early stages of interpretation \u0026ndash; enhancing the magnetic grids to better understand the subsurface geology (Pilkington and Tschirhart \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1. Vertical Derivative (VDR)\u003c/h2\u003e\u003cp\u003eThe first-order vertical derivative (VDR) of the total field magnetic intensity has been proposed and used for decades (Li and Nabighian \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hinze et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Hood and McClure (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) may be the first to identify the vertical contacts using the VDR; its definition for the RTP magnetic anomaly can be\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:\\text{V}\\text{D}\\text{R}=\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{z}}.\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the RTP magnetic anomaly and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{z}\\)\u003c/span\u003e\u003c/span\u003e increases downwards. Generally, along the outward normal direction of the edge of a positive anomalous source, the VDR of the RTP magnetic anomaly changes from positive to zero to negative, while along the outward normal direction of the edge of a negative anomalous source, and vice versa. Therefore, when both positive and negative anomalous sources are adjacent to each other, there would be another zero value, which leads to the creation of an additional edge, zero values represent edges of anomalous geological bodies (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another advantage of using VDR is that it is potentially less sensitive to noise in the data compared to methods relying on higher-order derivatives. We used MAGMAP an FFT processing suite within Oasis Montaj for vertical derivative calculation.\u003c/p\u003e\u003cp\u003eVDR tends to enhance shallower geological sources compared to deeper ones since signals from shallower sources attenuate faster with height. However, the VDR still can be used to sharpen boundaries of all anomalous sources and to approximately define the edges of sources by its zero contour R.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2. Total Horizontal Derivative (THDR)\u003c/h2\u003e\u003cp\u003eThe total horizontal derivative (THDR) of the total field magnetic intensity also has been proposed and used for decades (Li and Nabighian \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hinze et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dean \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Baranov \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Ravat 2007; Pilkington and Tschirhart \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hood and McClure \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Grauch and Cordell \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Ekinci et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Eldosouky et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is the square root of the sum of the squares of the first derivatives in the horizontal x and y directions. Its definition for the RTP magnetic anomaly can be\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{H}\\text{D}\\text{R}=\\sqrt{{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{x}}\\right)}^{2}+{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{y}}\\right)}^{2}}.\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGrauch and Cordell (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) discussed the factors which affect the boundaries enhanced by the THDR. The THDR requires only the calculation of horizontal derivatives, which can be readily performed in the space domain. It is less likely to produce additional boundaries, and is less sensitive to noise than some filters (Grauch and Cordell \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). We use GRIDGRAD.GRD in Oasis Montaj where a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:3\\times\\:3\\)\u003c/span\u003e\u003c/span\u003e point convolution filter is applied to produce the results. THDR results in relatively wide-band peaks compared to some derivative operators and the detection resolution is quite low, but the positive extreme values of the THDR correspond to anomalous geological sources\u0026rsquo; edges quite accurately (Ekinci et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Maxima of THDR show edges at all depths, but the efficiency of THDR decreases when anomalies from geological sources with different intensities and burial depths overlap (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.1.3. Analytic Signal Amplitude (ASA)\u003c/h2\u003e\u003cp\u003eFor 2-D cases, Nabighian (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) defined a complex analytic signal form and its corresponding amplitude of the analytic signal which is the square root of the sum of the squares of the derivatives in the x and z directions. This amplitude of the analytic signal is identical to the total gradient for given data and is independent of the magnetic inclination or remanent magnetism(Li et al. 2015).\u003c/p\u003e\u003cp\u003eThe formulation of the 3-D analytic signal is still debated (Li et al. 2015), but the formula presented by Roest et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) for the analytic signal amplitude is commonly used:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{S}\\text{A}=\\sqrt{{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{x}}\\right)}^{2}+{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{y}}\\right)}^{2}+{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{z}}\\right)}^{2}}.\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis is not strictly the amplitude of the true complex analytic signal but the total gradient (TG) in three dimensions and uses the RTP magnetic anomaly to calculate the 3-D analytic signal amplitude (ASA). Though not perfect, this calculation of the ASA has been shown to be effective in identifying boundaries of anomalous geological bodies.\u003c/p\u003e\u003cp\u003eThe maximum values of the ASA correspond to anomalous geological sources\u0026rsquo; edges, however, ASA results in relatively wide-band peaks and the detection resolution is relatively low compared to some other derivative techniques (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence ASA is particularly suitable for highlighting deeper geological sources. We applied the GRIDASIG GX in Oasis Montaj to calculate the analytic signal of the RTP grid.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.1.4. Tilt Derivative (TDR)\u003c/h2\u003e\u003cp\u003eFor 3-D cases, Miller and Singh (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) defined the tilt derivative (TDR, also called tilt angle (TA)) as the ratio between the vertical and the total horizontal derivatives of the RTP magnetic anomaly (Ferreira et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e):\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{D}\\text{R}={\\text{tan}}^{-1}\\left(\\frac{\\text{V}\\text{D}\\text{R}}{\\text{T}\\text{H}\\text{D}\\text{R}}\\right)={\\text{tan}}^{-1}\\left(\\frac{\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{z}}}{\\sqrt{{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y},\\right)}{\\partial\\:\\text{x}}\\right)}^{2}+{\\left(\\frac{\\partial\\:\\text{R}\\text{T}\\text{P}\\left(\\text{x},\\text{y}\\right)}{\\partial\\:\\text{y}}\\right)}^{2}}}\\right),\\:-{\\pi\\:}/2\\le\\:\\text{T}\\text{D}\\text{R}\\le\\:{\\pi\\:}/2.\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere the and are the first vertical and total horizontal derivatives of the RTP magnetic anomaly, respectively. The tilt derivative (TDR) can be simply understood as the amplitude-normalized first-order vertical derivative (VDR). We use the Tilt Derivative option (TILTDRV GX) in Oasis Montaj to calculate the tilt derivative of a grid.\u003c/p\u003e\u003cp\u003eThe TDR is useful for mapping shallow basement structures and mineral exploration targets as it acts like an automatic gain-control filter and deeper edges can thus be identified (Li and Nabighian \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Though the zero values of TDR may be sensitive to spurious boundaries due to complications of positive and negative anomaly superposition (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the zero values of TDR correspond more accurately to boundaries of anomalous geological sources.\u003c/p\u003e\u003cp\u003eThe results inside the zero contours represent anomalous geological blocks; the positive results correspond to more magnetic sources and vice versa.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Combined Analysis\u003c/h2\u003e\u003cp\u003eSection \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e. would produce mixed interpretation of boundary results of the magnetic anomaly, as all responses of shallow and deep geological sources are merged in the anomalies and the overlap of both the sub-circular boundaries of anomalous geological rocks and blocks and the sub-linear boundaries of anomalous geological layer and fault are identified by the four edge detection methods. To refine the detection results for a better understanding of the geological bodies and structures for ore deposit exploration, we analyse the combined results of Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e according to the four methods\u0026rsquo; discrepancy detection abilities. The corresponding steps are proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFirst step: Due to containing all three direction derivatives and an amplitude-normalized method, the TDR exhibits relatively sharp edges and the zero values of the TDR results represent both the sub-circular and sub-linear boundaries of anomalous geological features. The TDR is an effectively amplitude-normalized method which balances the amplitudes and differences of deep and shallow sources and this is crucial in revealing large-scale, deep geological variations at the same time as shallow edges. So we use the TDR to identify all edges from anomalous geological features, and also both deep and shallow edges. The width of features on the TDR map can also indicate the relative depths of different features.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSecond step: The zero values of the VDR also represent the sub-circular boundaries and the sub-linear boundaries. This is similar to the results of the TDR, however, as the VDR is not so sensitive to deep anomalous geological signals, the deep geological sources would not be so prominent in their results (Verduzco et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Therefore, we use the results of VDR to verify the TDR results in Step 1 and to distinguish shallow and deep geological sources from the TDR results.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThird step: Maximum values of ASA correspond to geological edges, but ASA exhibits relatively low resolution with wide-band peaks and the amplitudes of results vary with depths (Verduzco et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). So we use the ASA to identify the horizontal positions of the sub-circular boundaries and also distinguish the shallow geological edges from the deep ones. Combining with the TDR, we identify the sub-circular boundaries with different depth indications from the various lineament results.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFourth step: THDR exhibits relatively low resolution with wide-band peaks but its maximum values correspond to both the sub-circular boundaries and the sub-linear boundaries (Ekinci et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As the THDR is less sensitive to noise which can produce additional spurious boundaries, we use the THDR to verify the above results.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFinal step: Combined with the geological map, the RTP magnetic anomaly and other useful information like the geography or morphology, we present the results and their enhancements of the horizontal distributions of subsurface rocks and structures.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Results and Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Initial Results\u003c/h2\u003e\u003cp\u003eIn this section, we present the initial results of the automatic edge detection methods described in section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e specifically the vertical derivative, total horizontal derivative, analytic signal amplitude and tilt derivative of the RTP magnetic anomaly in the Mandula and Sonid Youqi area of the Inner Mongolia Autonomous Region of China - see Fig.\u0026nbsp;3 (a)-(d).\u003c/p\u003e\u003cp\u003eCompared with the RTP magnetic anomaly, though there are some influences from noise which are mainly caused by flight lines in the initial data and incomplete levelling, the VDR and THDR results present details on both the sub-circular boundaries and the sub-linear boundaries, the ASA result shows more sub-circular boundaries and the TDR result presents abundant information on both sub-circular and sub-linear boundaries.\u003c/p\u003e\u003cp\u003eAll edges are located at the northeast, the northwest, the central west, and the south parts of the study area. The edges at the central west part are dominated by sub-circular features geological sources and the central south is dominated by the sub-linear features. However, as the interpretation of all results is not immediately obvious, further analysis is needed to identify subsurface rocks and structures.\u003c/p\u003e\u003cp\u003eTo better understand the edge detection, we manually drew edge lines from the above auto edge detection and the results are shown in Fig.\u0026nbsp;4 (a)-(d). Of all the methods, the TDR (Fig.\u0026nbsp;4 (d)), whose edges are accurate and easy to track by zero values, presents the most complete edge detection results from all boundary shapes and all depths of sources. The VDR (Fig.\u0026nbsp;4 (a)), whose edges are also accurate and easy to track by zero values, identifies fewer features than the TDR. The ASA (Fig.\u0026nbsp;4 (c)) can identify sub-circular boundaries with different depth indications. The THDR (Fig.\u0026nbsp;4 (b)) presents different depth identification for both the sub-circular and sub-linear boundaries.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Final Results\u003c/h2\u003e\u003cp\u003eThe results of the identification of the two types of geological feature boundaries shown in Fig.\u0026nbsp;5 have been obtained from combined analysis according to the steps of Sections \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5.1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eWe first overlapped all edge detection results in Fig.\u0026nbsp;5 (a) and from results in Fig.\u0026nbsp;4 (a)-(d), we can see faults are mostly northwest-southeast, northeast-southwest and east-west trending. Secondly, according to the first four steps of Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5.1\u003c/span\u003e, we retained inferred boundaries of anomalous geological sources on the geological map ignoring repeated boundaries and they are shown in Fig.\u0026nbsp;5 (b). In Fig.\u0026nbsp;5 (b), boundaries circled by red dash lines are isolated edges which are detected only once by the four methods thus they are ignored in the final results. Boundaries surrounded by red dash rectangles and thumbtacks are interpreted to be human \u0026ldquo;cultural\u0026rdquo; anomalies and as such are ignored. Boundaries circled by sky-blue solid lines are confidently interpreted as real edges based on the morphology of river changes and the outcrop of the lithology variation. Thirdly, we drew the accepted boundaries of geological rocks and structures on the RTP magnetic anomaly map -of Fig.\u0026nbsp;5 (c).\u003c/p\u003e\u003cp\u003eFor the sub-linear boundaries of anomalous geological layer and fault structures, considering the geological and tectonic background (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), the anomalous geological fault structures of the study area obtained by our edge detection results can be divided into main structures and ultra-deep structures. The ultra-deep structures of this area need further study and confirmation. Most locations of highly magnetic geological structures are accompanied by faults, some of which are distributed in the middle south, west and north. The main structures, based on the basement tectonics, have gradually evolved and been modified through distinct stress field environments. The main structures can be specifically subdivided into basin-forming structures during the sedimentation of the faulted basins of the Early Cretaceous and new tectonic structures after the activities of the Paleogene according to the surrounding tectonic activities (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Each basin or sub-basin has developed through specific structures (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus the directions of the depressions align with the directions of the syndepositional faults, predominantly northeast-southwest. The structural forms of the depressions can be divided into two types: half-grabens and more symmetric grabens, often with faults as their boundaries on both sides. The new tectonic structures of the study area were manifested as wide and gentle pleated structures and fault structures (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). There are notable variations in the direction and manifestation of the structures in our results - see Fig.\u0026nbsp;5 (c). In the southeastern part of the study area, wide and gentle pleated structures are locally developed, which have a nearly east-west direction; The northwest part is mainly characterized by northwest-southeast trending fault structures.\u003c/p\u003e\u003cp\u003eFor the sub-circular boundaries of anomalous geological rocks and blocks, apart from rocks of magnetite, garnet magnetite quartzite and iron-bearing quartz veins, there are various highly magnetic igneous rocks including andesite, basalt, diorite, altered diorite, diorite-porphyrite, gabbro, gabbro diorite, allgovite. The former rocks can not be easily determined by our magnetic anomaly (a 125 m\u0026times;125 m grid) as their thicknesses are commonly less than 100 metres, considering the general geometries of these high magnetic rocks ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and their formation periods (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Andesite forms volcanic cones, thick lava flows and domes ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may be identifiable. The blocky andesite in the Jurassic of the Erlian Basin is the main volcanic rock and may alternate with other igneous rocks such as tuff and basalt, reflecting the complexity of the volcanic activity (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Basalt forms thin lava flows, intrusive dikes and sills ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may be hard to identify. The basalt of the Erlian Basin mainly formed during the Permian and Carboniferous periods and may be related to the activities of the basin tectonics and mantle hotspots; it may alternate with sedimentary layers, reflecting the interactive influence of volcanic activity and sedimentation ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Diorite and altered diorite (not granodiorite or quartz diorite) rarely form independent blocks but are often associated with basic, acidic or alkaline rocks ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). If independent rocks of diorite, altered diorite and gabbro diorite are formed, they are only rock stocks, sills, veins or irregular small intrusive bodies ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It may be hard to identify diorite and altered diorite. Diorite-porphyrite often presents as rock sills and walls, or as the edge phase of diorite ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The formation of diorite porphyry in the Erlian Basin is related to magmatic activity in the shallow crust (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Gabbro can be found in various tectonic environments and often forms intrusive bodies of varying sizes, such as lopolith, rock caps and sills ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The lateral scale of gabbro varies greatly, ranging from a few kilometres to hundreds of kilometres in size, appearing in various geological periods ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The gabbro in the Erlian Basin may have formed during periods of crustal extension or tectonic activity, which provided favorable conditions for the uplift and accumulation of basic magma (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The large scale of gabbro can be identifiable in the magnetic data. Gabbro diorite often presents as rock stocks and droplets ( Hollocher \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dorrik \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jerram and Petford \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Philpotts and Ague \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The gabbro diorite in the Erlian Basin is generally penetrated and transformed by intrusions. The surface outcrop is limited, and the overall exposed areas range from 1 to 30 km\u003csup\u003e2\u003c/sup\u003e, which may be identifiable in the magnetic data (Qi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Allgovite also called diabase porphyrite is mostly in the form of rock sills or walls, which also may be hard to identify. The formation of allgovite is related to basic magmatic activity in the shallow crust, and the allgovite in the Erlian Basin may be closely related to regional tectonic activity and basic magmatic activity.\u003c/p\u003e\u003cp\u003eCombined with the above information, interpretation and analysis, we obtained the final result of the identification of the sub-circular boundaries of anomalous geological discrete bodies of rocks and blocks and the sub-linear boundaries of anomalous geological structures of layers and faults. The result is shown in Fig.\u0026nbsp;5 (d).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Discussion\u003c/h2\u003e\u003cp\u003eAs thin and tiny anomalous geological layer structures can not be determined by our 125 m\u0026times;125 m magnetic anomaly grid, we mainly discuss the anomalous geological structures of thick layers or magnetic faults for the sub-linear boundaries. The results and information obtained from the aforementioned critical analysis were integrated with the known geologic features to construct the predominant tectonic elements affecting the basement configuration in the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1. Sub-circular boundaries of anomalous geological rocks and blocks\u003c/h2\u003e\u003cp\u003eThe study area is located within a sedimentary rock-covered region, where extensive Mesozoic and Cenozoic sedimentary layers are widely distributed. Field investigations revealed that the exposed Mesozoic and Cenozoic rocks and strata show no or very low magnetic susceptibility. Therefore, we conclude that the cause of the sub-circular magnetic anomalies is the concealed magnetic geological discrete bodies beneath the sedimentary layers. According to the magnetic susceptibility statistics table, we believe that the concealed magnetic geological sources in this area can be roughly divided into three categories: magnetic basement, concealed intrusive rock, and volcanic rock.\u003c/p\u003e\u003cp\u003eField surveys found that the Proterozoic magnetite quartzite has the strongest magnetic susceptibility, however, magnetite quartzite is mostly deeply buried as basement rock and only appears sporadically outside the study area. Therefore, the concealed magnetic geological sources consisting of magnetite quartzite cannot cause local high magnetic anomalies and they are considered not to be the primary cause of the sub-circular boundaries of magnetic anomalies discussed in this paper within the study area.\u003c/p\u003e\u003cp\u003eField surveys found multiple phases of magmatic intrusion along channels formed by intrusive rocks within the strata. These studies revealed high magnetic susceptibility intrusive rocks in the study area, including gabbro, diabase, diabase porphyrite, and gabbro-diorite. These four types of intrusive rocks are considered likely to constitute the concealed magnetic geological discrete bodies within the study area. Field investigations also found that gabbro and gabbro diorite, as plutonic rocks, typically occur as small-scale rock sills and laccoliths, characterized by deep burial and limited scale. Therefore, we infer that they are unlikely to be the main cause of sub-circular feature boundaries of magnetic anomalies. In contrast, diabase and diabase porphyrite, being hypabyssal rocks, are commonly produced in the form of rock sills or dikes, with shallow burial depths and large scales. They generally have unconformable contact with the surrounding rock and their spatial distribution patterns are often controlled by fault structures.\u003c/p\u003e\u003cp\u003eField surveys found basalt and basaltic andesite within the non-magnetic or weakly magnetic Mesozoic strata exhibit high magnetic susceptibility. The magnetic susceptibility contrast between these volcanic rocks and sedimentary strata can cause intense local magnetic anomalies, potentially generating strip-like or block-like sub-circular boundaries of magnetic anomalies. We consider that these two types of volcanic rocks are likely to constitute the concealed magnetic geological bodies within the study area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2. Sub-linear boundaries of anomalous geological layer and faults\u003c/h2\u003e\u003cp\u003eBased on the spatial distribution of our results, we can categorize the sub-linear magnetic anomalies in the study area into three groups of fault structures: NE (northeast) trending (F2, F3, F4, F5, F6, F7, F13), ENE (east-northeast) trending (F8, F9, F10, F11, F12), and NW (northwest) trending (F1).\u003c/p\u003e\u003cp\u003ePrevious studies have suggested that the NW trending F1 fault marks the boundary between the NE trending and NEE trending structures in the region. Our results identified the F1 fault and show that the magnetic anomaly characteristics associated with the F1 fault exhibit abrupt changes and displacements in the middle to eastern segment of the fault, while the western segment acts as a boundary line and linear gradient zone between distinct magnetic fields. The orientation of the sub-linear magnetic anomalies changes significantly between the southeastern and northwestern parts of the fault, with the southwestern part dominated by ENE trends and the northwestern part by NE trends. We further infer that the fault F1 has left-lateral displacement. Further, according to the magnetic anomalies and geological information, we believe that this fault formed most recently, as it shows displacement of other faults in the study area.\u003c/p\u003e\u003cp\u003eThe F2 and F3 faults are located on both sides of the Jiang\u0026rsquo;an ranch. It is obvious that the F2 fault forms a distinct boundary between different magnetic features. The area between the F2 and F3 faults shows local magnetic anomalies superimposed on a quiet positive background field, corresponding to the uplift zone in the regional structural framework. Therefore, we infer that the local anomalies between the F2 and F3 faults may be caused by concealed basic dikes and subvolcanic rocks. The map shows that the F3 fault is influenced by concealed basic intrusive rocks near Baiyan Aobao, with its extension becoming less apparent toward the southwest. The northeastern part of the F3 fault exhibits a quiet negative background field and we infer this to be a deeply buried non-magnetic Late Paleozoic basement, consistent with the depression structure in the regional structural framework. Based on our results, we conclude that the fault group of F2 and F3 represents boundaries between different structures, and the F3 fault is likely to control the orientation of the depression.\u003c/p\u003e\u003cp\u003eThe F4 fault represents a boundary between different magnetic field patterns and a linear gradient zone. The area between F4 and F3 faults, also is similar to the area northwest of the northeastern part of the F3 fault, which we have interpreted as a deeply buried non-magnetic Late Paleozoic basement. The region between the F4 and F5 faults is the northern margin of the Sunite Uplift, characterized by positive magnetic anomalies superimposed on a slightly negative background field. Therefore, we infer this region to be composed of a Paleozoic basement and shallow concealed basic intrusive rocks. Similar to the F3 fault, we suggest that the F4 fault is likely a depression-controlling fault, governing both the orientation of the depression and its sedimentation patterns.\u003c/p\u003e\u003cp\u003eThe F5 and F6 faults are NE trending, located on either side of Yihewusu, respectively. The area between the F5 and F6 faults exhibits a slightly negative background field. We interpret this area to be a deeply buried non-magnetic Late Paleozoic basement. The F5 and F6 faults and the area between them correspond to the depression structure in the regional structural framework. Therefore, we conclude that the F5 and F6 faults control the formation and evolution of local depressions within the uplift.\u003c/p\u003e\u003cp\u003eBased on the aforementioned tectonic setting, the Erlian Basin exhibits a \"two-depression-one-uplift\" structural pattern striking NE, with the Sonid Uplift in the central part flanked by depression zones on both sides. This regional \"alternating uplift-depression\" structural pattern corresponds well with our interpreted F2, F3, F4, F5, and F6 faults. This correlation not only validates our lineament identification results but also indicates that such structural patterns are distinctly reflected in the magnetic anomaly of the study area.\u003c/p\u003e\u003cp\u003eThe area between F6 and F7 faults is characterized by positive magnetic anomalies superimposed on a negative background field, suggesting a composition of Paleozoic basement and shallow-buried basic intrusive rocks. We interpret that the NE trending F7 fault, together with F6, controls the spatial configuration of the uplift structure.\u003c/p\u003e\u003cp\u003eThe F8, F9, F10, F11, and F12 faults within the southern part of the F1 fault constitute a set of sub-parallel fractures striking ENE. The magnetic characteristics of the region containing F8-F12 faults manifest as a series of parallel ENE trending sub-circular and sub-linear magnetic anomalies superimposed on a negative background field. We interpret this pattern to indicate the presence of concealed basalt or basaltic andesite within non-magnetic Paleozoic basement and fault-controlled shallow non-magnetic strata.\u003c/p\u003e\u003cp\u003eThe F13 fault also trends NE, and its magnetic characteristics exhibit a weak positive magnetic anomaly within a negative background field. We interpret that the F13 fault has an inherited relationship with the F7 fault and represents a later-formed shallow fault. While the F13 fault has displaced the concealed magnetic geological discrete bodies within the strata, it has not altered the regional tectonic framework.\u003c/p\u003e\u003cp\u003eApart from the F1 fault, all other identified lineaments (F2-F13 faults) exhibit NE or ENE trends. This finding further confirms that the depression structures in the study area are controlled by NE-ENE trending faults, forming a structural framework characterized by parallel or en echelon arrangements. We infer that these fault structures, from F2 to F13, faults also control the orientation of most concealed magnetic geological discrete bodies within the area. From a tectonic evolution perspective, this also validates that the majority of small to medium-sized depression structures in the study area display prominent inheritance patterns, with limited neotectonic development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2. Uncertainty of our identification results\u003c/h2\u003e\u003cp\u003eWe may infer that some highly magnetic geological blocks composed of rocks such as andesite, gabbro and gabbro diorite can be identified by the edge detection of the sub-circular feature boundaries using our 125 m\u0026times;125 m magnetic anomaly grid. However, as the formation geometries of the andesite (volcanic cones and domes), gabbro (basins and caps) and gabbro diorite (stocks) all can be the sub-circular feature boundaries of anomalous geological blocks and there is a lack of evidence from local outcrop to identify thin rock layers and less magnetic rocks, further studies are needed to recognize other specific rock types.\u003c/p\u003e\u003cp\u003eAdditionally, as no methods are perfect, the four methods we used here may have disadvantages, which may make our results imprecise. For example, the VDR is affected by the burial depth and inclination of the geological bodies and the magnetization direction (Wang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); The THDR is influenced by the burial depth and inclination of the geological discrete bodies (Wang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); The TDR produces redundant edges when both positive and negative anomalies exist (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Though the ASA appears to be less susceptible to false edges, if the ratio of the depth to the width of the geological discrete bodies is high, the ASA\u0026rsquo;s maximum values lie on the top of the body instead of the edges, which may cause misinterpretation (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe study region of the Mandula and Sonid Youqi in Inner Mongolia, which is situated in the centre of the Daxing'anling multi-metallogenic zone is a potential site of ore deposits, however, it is located in the south of the Erlian Basin and largely covered by Cenozoic sediment. Through the aeromagnetic survey and our rigorous integrated interpretations and combined analysis, we have unraveled some high magnetic susceptibility bodies and the subsurface structures which would dominate mineral deposits. The geological discrete bodies and structures discovered are predominantly aligned east-west and northeast-southwest and partly northwest-southeast. Through our identifications of geological discrete bodies and structures which stand as a pivotal facet of investigation and comparison with the geological background, we trace these lineaments, unravelling their spatial distribution characteristics to potentially help elucidate the local geological evolution.\u003c/p\u003e\u003cp\u003eMoreover, the identification of sub-circular features of anomalous geological rocks and blocks and sub-linear features of geological layers and fault structures by our combined analysis provides a unique window into the subsurface architecture. It should be particularly useful in the early stages of exploration in greenfield areas where geological and geophysical information is sparse. Different edge enhanced methods have various resolutions. Some methods only respond well to large-scale and deep-buried structures, but others can resolve small-scale structures and are more tolerant of noise. We suggest a comparative analysis, combining the similarities and differences between the results of different edge detection methods: VDR, THDR, ASA and TDR. These robust methods are used to depict the two key types of boundaries, the sub-circular and sub-linear features, the low-resolution methods are used to identify deep or large-scale boundaries, and the high-resolution methods are used for secondary confirmation to exclude possible spurious boundaries and to further determine the shallow or small-scale boundaries.\u003c/p\u003e\u003cp\u003eOur comprehension of the geological framework in the Mandula and Sonid Youqi region makes a difference in targeting geological bodies and structures which are meaningful for the future exploration of mineral resources. High-resolution and multi-approach geophysical surveys, harmonized with pinpoint geological sampling, have the potential to provide even finer interpretations of subsurface geological and lithological variations, which would greatly improve the finding of ore deposits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was funded by the National Natural Science Foundation of China, grant numbers 42374172, the Fund from the SinoProbe Laboratory, grant number JKYZD202303, and the China Scholarship Council, grant number 202004180027.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, C. Z. and J. G.; methodology, C. Z., C. G., J. Y. B. X. and F. L.; software, C. Z., J. G., J. J. and F. L.; validation, C. Z., C. G., J. J., M. C., B. X. and Z. Y.; formal analysis, C. Z., C. G., J. J., M. C. and Z. Y.; investigation, J. G., M. C. and Z. Y.; resources, J. G.; data curation, J. G. and B. X.; writing\u0026mdash;original draft preparation, C. Z., C. G., J. Y., M. C., J. G., F. L., Z. Y. and F. L.; writing\u0026mdash;review and editing, C. Z., C. G., B. X., J. Y. and F. L.; visualization, C. Z. and J. Y.; supervision, C. Z. and J. G.; project administration, C. Z., J. G. and J. Y.; funding acquisition, C. Z., J. G. and J. Y. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their gratitude to all those who contributed to this research, especially to the data collection team. The authors also express their deepest appreciation to Genesis Obero and the other editors for their invaluable assistance, and thank the anonymous reviewers for their constructive comments and valuable suggestions.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e\u003cp\u003eThe data are not applicable to this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaranov, V. A New Method for Interpretation of Aeromagnetic Maps: Pseudo-gravimetric Anomalies. GEOPHYSICS 1957, 22 (2), 359\u0026ndash;382.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlakely, R. J.; Simpson, R. W. Approximating Edges of Source Bodies from Magnetic or Gravity Anomalies. GEOPHYSICS 1986, 51 (7), 1494\u0026ndash;1498.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCascone, L.; Green, C.; Campbell, S.; Salem, A.; Fairhead, D. 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Acta Petrolei Sinica 2011, 1, 18\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"geomechanics-and-geophysics-for-geo-energy-and-geo-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gggg","sideBox":"Learn more about [Geomechanics and Geophysics for Geo-Energy and Geo-Resources](http://link.springer.com/journal/40948)","snPcode":"40948","submissionUrl":"https://submission.nature.com/new-submission/40948/3","title":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Geological Boundary, Magnetic Anomaly, Edge Detection, Inner Mongolia, Derivatives, Analytic Signal","lastPublishedDoi":"10.21203/rs.3.rs-6937737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6937737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith difficulties of finding outcrop mines increasing, mineral explorations go to sedimentary cover areas, however, geological features of rocks and structures which are the dominant control to ore deposits hidden deeply beneath sedimentary cover are difficult to identify especially in the absence of seismic or electrical methods. Magnetic edge detection methods help find structures and boundaries of anomalous geological features. Still, there are gaps between geophysical edge detection results and geological demands, that is all edge detection results sharpen boundaries without distinguishing the sub-circular boundaries of anomalous geological rocks and blocks from the sub-linear boundaries of anomalous geological layers and fault structures, as these two types of geological boundaries would determine and influence mineral systems differently. Trying to improve this issue, we present a combined analysis approach of edge detections to identify the two key types of geological boundaries. Firstly, we initiated the approach by applying four generally accepted methods to detect geological feature edges. Secondly, we analysed and interpreted edge detection results according to combining and integrating the characteristics of each employed algorithm. Finally, we present the geologic sketch map, which shows the distribution of inferred geological rocks and structures based on the combined analysis of aeromagnetic data in a thick sedimentary cover area of northwest Inner Mongolia. Our geologic sketch map may be helpful for further studies of Inner Mongolia and our combined analysis approach may be useful for other coverage geological boundary identifications.\u003c/p\u003e","manuscriptTitle":"Mapping and Identifying Two Types of Geological Features from Enhanced Derivatives of Aeromagnetic Data: A Case Study from Northwest Inner Mongolia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 11:49:23","doi":"10.21203/rs.3.rs-6937737/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T02:40:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T01:03:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T11:56:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","date":"2025-06-20T09:40:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"geomechanics-and-geophysics-for-geo-energy-and-geo-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gggg","sideBox":"Learn more about [Geomechanics and Geophysics for Geo-Energy and Geo-Resources](http://link.springer.com/journal/40948)","snPcode":"40948","submissionUrl":"https://submission.nature.com/new-submission/40948/3","title":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9dccf968-aef8-40ef-8bf1-9df25b5b8293","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T22:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 11:49:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6937737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6937737","identity":"rs-6937737","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}