Uncertainty In Interpretation Research Articles

Интерпретация результатов георадарного зондирования скального массива пород с использованием цифрового образа

A methodological approach to interpreting the results of georadar sounding of a rock mass using a digital image is considered. The studies carried out have established that during the georadar probing of the rock masses, distortion of the induced electromagnetic field is observed in areas with different physical properties of the rocks (such as the rock mass heterogeneity, rock fracturing, zones of increased water saturation). The creation of a digital image of the electromagnetic field caused by ground penetrating radar sounding using computer modeling made it possible to significantly reduce the uncertainty in interpretation of the obtained field data through understanding the mechanism of formation of the wave characteristics and comparing synthetic data with the in-situ data. Regularities in formation of the wave patterns of the ground penetrating radar model data (changes in the in-phase axes and amplitude characteristics of the signal) of a rock mass containing structural heterogeneities have been identified. When sounding such a rock mass with the georadar, reflections from the edges of heterogeneities appear in the wave patterns in the form of hyperbola branches. The intensity of the wave field distortion is determined by the contrast of the physical properties of rocks, as well as by the spatial orientation and depth of the heterogeneity, which is a key parameter of georadar sensing. It has been established that the variability of the physical properties of rock masses near disturbances introduces significant distortions into the parameters determined by georadar sounding, including the angle of incidence of disturbances. The considered methodological approach provides the basis for creating a digital image of georadar probing of a rock mass.

Using random forest for brain tissue identification by Raman spectroscopy

The traditional definitive diagnosis of brain tumors is performed by needle biopsy under the guidance of imaging-based exams. This paradigm is based on the experience of radiogolists, and accuracy could be affected by uncertainty in imaging interpretation and needle placement. Raman spectroscopy has the potential to improve needle biopsy by providing fingerprints of different materials and performing in situ tissue identification. In this paper, we present the development of a supervised machine learning algorithm using random forest (RF) to distinguish the Raman spectrum of different types of tissue. An integral process from raw data collection and preprocessing to model training and evaluation is presented. To illustrate the feasibility of this approach, viable animal tissues were used, including ectocinerea (grey matter), alba (white matter) and blood vessels. Raman spectra were acquired using a custom-built Raman spectrometer. The hyperparameters of the RF model were determined by combining a cross-validation-based algorithm and manually adjusting. The experimental results show the ability of our approach to discriminate different types of tissues with high accuracy.

Trans-Dimensional Bayesian Inversion of Rayleigh Wave Dispersion Curves for Shallow Surface Two-Dimensional Imaging

In this study, we achieve 2-D spatial structure directly from dispersion measurements using the reversible jump Markov chain Monte Carlo (rj-McMC) algorithm with a fully 2-D model parameterization. New parsimonious parameterization with low dimensional representation is introduced, which enable us to accurately reproduce the wave velocity field and the associated uncertainties. Field data test demonstrates that the proposed method estimates directly the shear-wave velocity profile with better lateral resolution. The associated uncertainties provide directly interpretable uncertainty on structures of interest.

The challenges of signal interpretation of burials in ground‐penetrating radar

AbstractThe identification of unmarked graves and burials is one of most common applications of ground‐penetrating radar (GPR) in archaeology. Despite a high frequency of use and a long history of experimentation, there appears to be considerable variability on what indicates a burial in GPR data—likely a consequence of heterogeneity in geological contexts, age and in burial practices. Although general statements about uncertainty in GPR interpretation may be acceptable in archaeological applications, the interpretative process becomes more complicated when GPR is used to locate unmarked graves in culturally, politically and legally contested locations such as at former Indian Residential Schools (IRSs) in Canada. In this paper, we review international applications of the technique and identify trends and traits between the authors' use of GPR to identify burials. By categorizing the studies based on the GPR reflection signatures identified, our review demonstrates that there is modest consensus across the 77 documents reviewed for what represents a burial. Interrogating these findings, we identify a range of potential contributors to signal heterogeneity and outline potential steps forward to a higher confidence or more statistically robust identification of unmarked graves using GPR.

Using Bayesian Neural Networks for Uncertainty Assessment of Ore Type Boundaries in Complex Geological Models

AbstractBuilding an orebody model is a key step in the design and operation of a mine because it provides the basis for follow-up mine decisions. Recently, it was shown that convolutional neural networks can successfully reproduce the manual geological interpretation of a complex ore deposit. The deep learning approach mitigates the shortcomings of a labor-intensive process that greatly limits the speed at which geological resources can be updated. However, convolutional neural network architectures lack the ability to measure the confidence of their predictions. In this study, we tried to assess the uncertainty of the boundaries of these domains so that the characterization of metal grades within them can account for this uncertainty. We explored and compared Monte Carlo Dropout and Bayesian neural networks to assess the uncertainty of deep convolutional neural network models trained to predict geological domains conditioned to drill-hole data. Monte Carlo Dropout uncertainty maps reflect the uncertainty in geological interpretations. The uncertainty is highest in areas where the interpreter/geologist had more difficulty delineating the boundaries of geological bodies. This is known as geological interpretation uncertainty. In contrast, Bayesian neural network uncertainty is visible depending on ore type frequency, complexity, and heterogeneity. Bayesian neural networks are able to better represent the uncertainty regarding the unknown. The application example here is a real case study of several ore types from a polymetallic sulfide orebody located in the south of Portugal.

Basin Structure for Earthquake Ground Motion Estimates in Urban Los Angeles Mapped with Nodal Receiver Functions

We constrained sedimentary basin structure using a nodal seismic array consisting of ten dense lines that overlie multiple basins in the northern Los Angeles area. The dense array consists of 758 seismic nodes, spaced ~250–300 m apart along linear transects, that recorded ground motions for 30–35 days. We applied the receiver function (RF) technique to 16 teleseismic events to investigate basin structure. Primary basin-converted phases were identified in the RFs. A shear wave velocity model produced in a separate study using the same dataset was incorporated to convert the basin time arrivals to depth. The deepest part of the San Bernardino basin was identified near the Loma Linda fault at a depth of 2.4 km. Basin depths identified at pierce points for separate events reveal lateral changes in basin depth across distances of ~2–3 km near individual stations. A significant change in basin depth was identified within a small distance of ~4 km near the San Jacinto fault. The San Gabriel basin exhibited the largest basin depths of all three basins, with a maximum depth of 4.2 km. The high lateral resolution from the dense array helped to reveal more continuous structures and reduce uncertainties in the RFs interpretation. We discovered a more complex basin structure than previously identified. Our findings show that the basins’ core areas are not the deepest, and significant changes in basin depth were observed near some faults, including the San Jacinto fault, Fontana fault, Red Hill fault and Indian Hill fault.

Mapping faults in 3D seismic data – why the method matters

3D seismic reflection imagery is the most widely used resource for interpreting the geometric structure of faults in the subsurface. Yet these endeavours carry uncertainties, the significance of which are rarely discussed. We explore how the application of different workflows yield different interpretations of a single high-quality 3D seismic image-set. We describe and apply five mapping workflows, based on 2D derivations of imagery to map an array of small-scale faults. Some workflows use vertical profiles, a conventional approach, others use plan views. We also vary the amount of under-sampling. Stacking the fault maps derived from the five workflows into a heat map shows broadly similar trends and distributions of faults irrespective of the workflow deployed. However, juxtaposition mapping reveals differences in fault length and network pattern (linkage and segmentation) arising from the different workflows. We quantify the total fault areas and distribution of fault lengths for each workflow – revealing significant differences in these statistics. Our results show that mapping strategies impact the interpretation of fault geometry, their network patterns and derived statistics. This understanding is critical for assessing and risking fault interpretations – deploying multiple workflows can reveal inherent uncertainty in structural interpretation of 3D seismic imagery.

Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning

The unreliable prediction of the low-frequency components from inverted acoustic impedance causes uncertainty in quantitative seismic interpretation. To address this issue, we first calculate various seismic and geological attributes that contain low-frequency information, such as relative geological age, interval velocity, and integrated instantaneous amplitude. Then, we develop a method to predict the low-frequency content of seismic data using these attributes, their high-frequency components, and recurrent neural networks. Next, we test how to predict the low-frequency components using stacking velocity obtained from velocity analysis. Using all the attributes and seismic data, we propose a supervised deep learning method to predict the low-frequency components of the inverted acoustic impedance. The results obtained in both synthetic and real data cases show that the proposed method can improve the prediction accuracy of the low-frequency components of the inverted acoustic impedance, with the best improvement in a real data example of 57.7% compared with the impedance predicted using well-log interpolation.

Advantages of a Solid Solution over Biphasic Intercalation for Vanadium-Based Polyanion Cathodes in Na-Ion Batteries.

The efficient operation of metal-ion batteries in harsh environments, such as at temperatures below -20 °C or at high charge/discharge rates required for EV applications, calls for a careful selection of electrode materials. In this study, we report advantages associated with the solid solution (de)intercalation over the two-phase (de)intercalation pathway and identify the main sources of performance limitations originating from the two mechanisms. To isolate the (de)intercalation pathway as the main variable, we focused on two cathode materials for Na-ion batteries: a recently developed KTiOPO4-type NaVPO4F and a well-studied Na3V2(PO4)2F3. These materials have the same elemental composition, operate within the same potential range, and demonstrate very close ionic diffusivities, yet follow different (de)intercalation routes. To avoid any interpretation uncertainties, we obtained these materials in the form of particles with merely identical morphology and size. A detailed electrochemical study revealed a much lower capacity and energy density retention for phase-transforming Na3V2(PO4)2F3 compared to NaVPO4F, which exhibits a single-phase behavior over a wide range of Na concentrations. The reasons for the inferior rate capability and temperature tolerance for the phase-separating Na3V2(PO4)2F3 material should be affiliated with slow phase boundary propagation. We hope that the comprehensive information on limiting factors provided for both mechanisms is useful for the further optimization of electrode materials toward a new generation of high-power and low-temperature metal-ion batteries.

Natural fracture mapping and discrete fracture network modeling of Wolfcamp formation in hydraulic fracturing test site phase 1 area, Midland Basin: Fractures from 3D seismic data, image log, and core

Fractures control fluid flow in ultra-low permeability reservoirs due to their much higher permeability. Recognition, mapping, and characterization of both natural and hydraulic fractures at multiple scales are critical to predicting hydrocarbon production in tight rocks, including mudrock. However, this determination is very challenging due to data scarcity, varying data resolutions, and uncertainties in interpretations. In this research, we combined core-identified fractures, image log-identified fractures, and 3D seismic data to analyze natural fracture features of, and construct a discrete fracture network (DFN) model for, the Wolfcamp Formation in the Hydraulic Fracturing Test Site Phase 1 (HFTS-1) project area in the central Midland Basin of the United States. An azimuth-guided, ant-track seismic attribute called VarAntAntPas was developed to recognize natural fractures and extract their seismic discrete planes (SDP) at multiple dip azimuth ranges. The extracted SDPs were then employed to calculate the fracture length, height, and elongation, as well as the dip angle and dip azimuth. This process revealed a limitation of recognizing natural fractures shorter than 600 ft by 3D seismic data due to its resolution. To model fractures shorter than 600 ft, we calculated fracture intensity at different dip azimuths from the VarAntAntPas attributes and combined this variable with fracture dip angle derived from well data and the extracted SDPs. The extracted SDPs for larger fractures (length >600 ft) and the modeled smaller fractures (length <600 ft) were combined to build a DFN model of the Wolfcamp Formation in the HFTS-1 project area. Clearly, the DFN model was built with different ways for two different-size fracture groups. Meanwhile, the discrete fractures in different orientations were built independently and then combined together in the DFN model. Natural fractures predicted by the DFN model were then validated by comparison to fractures identified from core and image logs from two horizonal wells and one slant well in the HFTS-1 project area.

Characterization of an active landslide structure with integrated electrical resistivity tomography and multi‐channel analysis of surface waves methods in Değirmendere district, Sakarya (Türkiye)

AbstractLandslides following the development of slope instability can have serious consequences. Geophysical surveys have potential to aid in the understanding of landslides and their instability. This study described a landslide that occurred under particular geomorphological and meteorological conditions in Erenler (Sakarya, NW Türkiye) using electrical resistivity tomography (ERT) and multi‐channel analysis of surface waves (MASW) surveys and mechanical drillings. Data showed that the landslide is characterized by downslope movement of an altered clay unit over a claystone unit. The traditional landslide model used to interpret geoelectric data involves sliding of a relatively low‐resistivity material over higher resistivity bedrock. Here, the ERT survey showed a relatively high‐resistivity sliding material moving over relatively low‐resistivity bedrock units corresponding to the claystone unit. The relatively lower resistivity of the whole study area was related to the clay‐rich materials detected in the boreholes. This low‐resistivity unit was detected at high velocity in the MASW section, and it was understood that this layer was a compact structure acting like bedrock. The detected slip surfaces, landslide scars and slip surface toe indicate that the movement character of the landslide is retrogressive. The study results showed that the integrated and comparative use of different geophysical methods effectively reduces interpretation uncertainties and potential errors and leads to much better landslide characterization.

Multi-level ultra-deep fault-controlled karst reservoirs characterization methods for the Shunbei field

Ultra-deep carbonate fault-controlled reservoirs are characterized by large burial depths, low dissolution degrees, strong heterogeneity, and limited effective well data. Accurate 3D characterization based on seismic data is essential for efficient development of this type of reservoir. However, traditional seismic prediction methods are insufficient to accurately characterize different reservoir levels in the exploration and development of fault-controlled ultra-deep reservoirs. We propose a set of improved multi-level characterization methods for fault-controlled reservoirs. The improved methods could recover seismic information obscured by strong reflections and reduce the uncertainty of seismic interpretation. This study combined seismic strong reflection suppression and sedimentary strata seismic reflection interference elimination, proposed improved inversion methods, and advanced attribute calculation methods to improve the identification accuracy of reservoirs. In particular, we proposed the karst cave carving method based on improved inversion method and karst cave enhancement algorithm, the dissolved pore zone identification method based on optimization energy envelope algorithm, and the fault-fracture zone characterization method based on optimized atomic decomposition texture contrast. These methods were thoroughly validated by theoretical 3D models and field data. The proposed multi-level characterization methods can effectively improve the identification accuracy of fault-controlled karst reservoirs, provide a benchmark for predicting similar strong heterogeneous carbonate reservoirs, and provide reliable support for further facies modeling.

A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure

AbstractThis article presents a Bayesian reliability modelling approach for wind turbines that incorporates the effect of time‐dependent variables. Namely, the technique is used to explore the effect of annual services on wind turbine failure intensity through time for turbines within a currently operational wind farm. In the operator's experience, turbines seemed to fail more frequently after scheduled maintenance was performed; however, this is an unexplored effect in the literature. Additionally, the effects of seasonality, year of operation and position in the array on failure intensity are explored. These features were included in a Cox‐like model formulation which allows for time‐dependent covariates. Inference was performed via Bayes rule. Results show a spike in failure intensity reaching 1.57 times the baseline in the six days directly proceeding annual servicing, after which failure intensity is reduced compared to baseline. Also observed is a significant year‐on‐year reduction of failure intensity since the introduction of the site's data management system in 2018, a clear preference for modelling time to failure via a Weibull distribution and a dependence on location in the array with respect to the prominent wind direction. Results also show the benefit of employing a Bayesian regime, which provides easily interpretable uncertainty quantification.

Uncertainties in and recommendations to small punch tensile and creep tests for ductile materials

The objective of this paper is to clarify the complex nature which involves the interaction of several non-linear deformation mechanisms experienced in small punch tensile and small punch creep tests, and as a consequence, the associated difficulties in data interpretation. The paper starts with a highlight of some specific features experienced during small punch deformation, e.g., large deformation, high local plasticity, contact, the effect of initial plasticity, possible early cracking etc. and the consequences of them in data interpretation. Although several test standards have been available, uncertainties in data interpretation still exist and a universally accepted conversion method has not been available. On this basis, for the first time, critical comments on the uncertainties and applicability of the test methods in determining the tensile and creep properties and in high-temperature structural integrity assessment are addressed. Finally, recommendations on future developments are provided.

Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess

The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (γEM) used in Galactic center analyses affects the interpretation of the GCE's nature.To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of γEMs with increasing parametric freedom. We calculate the fractional contribution (f src) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the γEM. For the simplest γEM, we obtain f src = 0.10 ± 0.07, while the most complex model yields f src = 0.79 ± 0.24.In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed γEM.The quoted results for f src do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated γEM iterations. We quantify the reality gap between our γEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed γEMs have gaps to reality.Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.

FEATURES OF DETERMINING FILTRATION PARAMETERS OF COMPLEX CARBONATE RESERVOIRS AT THEIR OPERATION BY HORIZONTAL WELLS

Link for citation: Salnikova O.L., Chernykh I.A., Martyushev D.A., Ponomareva I.N. Features of determining filtration parameters of complex carbonate reservoirs at their operation by horizontal wells. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 5, рр.137-147 In Rus. The relevance of the research is caused by the fact that in the conditions of continuous deterioration in the structure of hydrocarbon reserves, complex technologies for their development are being implemented, based, among other things, on the use of wells of complex design (horizontal wells). The increase in the share of horizontal wells in the total operating stock determines the relevance of the scientific and methodological justification of research technologies that comprehensively take into account the structural features of the reservoir and the geometry of the fluid flow. The most complete volume of geological and field information can be obtained by combining two main types of research – geophysical and hydrodynamic. The problem of data, in fact, of the indirect research methods, should be considered the need to assess the reliability of their results and the lack of approaches to reduce interpretation uncertainties, which is especially important in the conditions of the impossibility of coring in horizontal wells. This article proposes possible directions for solving these problems. The main aim of the study is the scientific substantiation of approaches to research and interpretation of their results, allowing reliably determining the filtration parameters of productive formations during their operation with horizontal wells. Object: complex Tournaisian-Famenian carbonate reservoirs in Perm region, operated by horizontal wells. Methods: geophysical and hydrodynamic studies of wells, laboratory core studies, multivariate statistical modeling. Results. It has been established that geophysical studies (interpretation was made in accordance with the algorithms for vertical wells) do not allow reliably estimating the length of the horizontal section of the wellbore and the filtration properties of the reservoir. It is recommended to determine the filtration properties when interpreting the data of hydrodynamic studies of wells. Algorithms of modern methods of interpretation of hydrodynamic studies, implemented in common software products, allow you to determine a large amount of geological and field data, including a number of parameters specific to horizontal wells. However, this procedure is accompanied by uncertainties, for example, in terms of the choice of interpretation models. The performed multivariate statistical modeling made it possible to substantiate the methodology for reliably determining the filtration properties of the reservoirs under consideration and the approach to reduce uncertainties when choosing interpretation models.

Quantifying and modeling curved thrust fault-propagation folds at different scales

Classical models of fault-related folds, which invoke planar faults, are inappropriately applied in many instances, as curved faults are commonly observed in nature. The application of classical planar fault models for such folds hinders the assessment of the geometric relationship between these features and the quantification of crustal shortening during faulting and folding. Our study, for the first time, attempts to establish fundamental geometric relationships for curved thrust fault-propagation folds by examining the geometries and kinematics of three contractional structures at different scales. By relating the deformation of backlimb to the underlying curved thrust with an integrated algorithm incorporating forelimb trishear and backlimb shear, we successfully reproduced most of the fold geometry with a smoothly curving backlimb and an abrupt forelimb. We discovered that the deformation of folded backlimb is a combination of limb rotation and kink-band migration. This unique deformation mechanism means that curved faults do not experience greater slips (less than 2%) compared to planar faults of similar dips. Although the introduction of minor curvature in the fault trajectory cannot have a significant impact on shortening, our results emphasize that the choice of fault geometry is crucial for the deformation of folded backlimb and landform surface. We then combine our new work with previous studies and find that the arc angle distributions of curved thrust fault-propagation folds at different scales are in the range of ∼30°–∼60°, which is important for reasonable constraints on the geometry of deep faults in the absence of detailed seismic controls. Finally, our study further suggests that introducing area-depth strain analysis prior to the modeling analysis could minimize the assigned uncertainty in interpretation and test the validity of kinematic models in determining balanced structures.

Stochastic velocity modeling for assessment of imaging uncertainty during seismic migration: Application to salt bodies

Variations in the migration velocity model directly affect the position of the imaged reflectors in the subsurface, leading to structural imaging uncertainties. These uncertainties are not explicitly addressed when trying to deterministically build an adequate velocity model. This paper presents a new stochastic geology-controlled velocity modeling method that handles the possible presence of a salt weld. This permits the generation of a large set of geologic scenarios and associated velocity models. Each model is used to remigrate the seismic data. Then, a statistical analysis of the resulting seismic images is performed to quantify the local variability of the seismic responses. The approach is applied to the imaging of salt diapirs in an iterative scheme (migrate, pick, and update). The results indicate that similar to stacking common-midpoint gathers, the statistical analysis preferentially preserves recurrent features from one image to another. In particular, this analysis permits the distinction between the connected and detached diapirs without prior knowledge about their connectivity, highlighting the potential of the method to resolve important aspects of the basin and reservoir architecture. More generally, it provides quantitative information on the parts of the seismic image most sensitive to migration velocity variations, which opens an interesting perspective to quantitative interpretation uncertainty assessment. Finally, the presented application also suggests that it is possible to significantly improve the quality of the generated seismic images by sampling many possible geologic scenarios.

Features of Self Assessments of the Quality of one's Own Life During the Pandemic and the Beginning of Missile Attacks on the Civilian Infrastructure of Ukraine

Even before the beginning of the military attack of the Russian Federation on Ukraine, the term VUCA appeared in the national information field, which concentrates the basic features of the modern life-being of the society of the 21st century: volatility (Volatility) - due to the urgent need to take into account numerous changing factors; uncertainty (Uncertainty) the real impossibility of predicting the main perspectives; Complexity lack of real understanding of the essence of many events; ambiguity (Ambignity) clear uncertainty of interpretation of a considerable number of life situations. All these separate components of VUCA became even more "sharpened" after 24.02.2022 and could not help but affect the self-assessment of the quality of life (QoL) of somatically healthy young doctors under the simultaneous combined informational pressure of both the COVID-19 pandemic and the beginning of a full-scale war. 44.

Quaternary Crustal Shortening of the Houyanshan Structure in the Eastern Chinese Tian Shan: Constrained from Geological and Geomorphological Analyses

The Tian Shan is one of the most active intracontinental orogenic belts in the world. It has undergone complex deformation that has resulted in the formation of several fold-and-thrust belts (FTBs) in the piedmonts and intermontane basins. Investigating the deformation histories of these FTBs is important for understanding the tectonic propagation processes of the Tian Shan. Here, we gain insight into these crustal shortening processes by deciphering the Houyanshan structure, a typical fold-thrust belt in the eastern Chinese Tian Shan. We first describe a curved thrust ramp and related fold pairs of the structure using high-resolution remote sensing photography, deformation of fluvial terraces, and field-based geological cross-section. Combined with deformed terrace records and optically stimulated luminescence (OSL) dating results, the kinematic style allows us to yield a geologic shortening rate of 1.6 ± 0.2 mm/a since ~52 ka. Second, to reduce uncertainty in the seismic interpretation and quantify the amount and time of crustal shortening, we interpret three seismic reflection profiles by using the theory of quantitative fault-related fold, area-depth-strain (ADS), and reverse modeling analyses. These profiles provide direct evidence that this structure connects by means of a listric thrust ramp to a shallow detachment level. ADS analysis reveals that the maximum shortening of the Huoyanshan structure is ~4.5 km, which is consistent with the result of quantitative inverse modeling. Each of the structural analysis methods gives similar parameters, and the high consistency of results greatly improves the soundness of a given geologic interpretation. Finally, the shortening rate and total shortening amount suggest that the structure may have formed at 1.8–3.7 Ma, which is nearly synchronous around the Tibetan Plateau. Together, these results indicate that this combined geological and geomorphological analysis provides greater insight into deformation information than can be achieved by any individual technique in studying fold-and-thrust belts worldwide.