Uncertainty In Interpretation Research Articles

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

Existing methods for estimating formation boundaries from well-log data only analyze the formation along the wellbore, failing to capture changes in the 3D formation structure around it. This paper presents a method for real-time 3D formation boundary interpretation using readily available well logs and seismic image data. In the proposed workflow, the mean formation boundary is estimated as a curve following the well path. 3D surfaces are then fitted through this boundary curve, aligning with the slopes and features in the seismic image data. The proposed method is tested on both synthetic and field datasets and illustrates the capabilities of accurate boundary estimation near the well path and precise representation of boundary shape changes further away from the well trajectory. With this fully automated geological interpretation workflow, human bias and interpretation uncertainty can be minimized. Subsurface conditions can be continually updated while drilling to optimize drilling decisions and further automate the geosteering process.

Construction of knowledge constraints: a case study of 3D structural modeling

The uncertainty of structural interpretation complicates the practical production and application of data-driven complex geological structure modeling technology. Intelligent structural modeling excavates and extracts structural knowledge from structural interpretation through human–machine collaboration and combines structural interpretation to form a new model of complex structural modeling guided by knowledge. Specifically, we focus on utilizing knowledge rule reasoning technology to extract topological semantic knowledge from interpretive data and employ knowledge inference to derive structural constraint information from complex geological structure models, thus effectively constraining the 3D geological structure modeling process. To achieve this, we develop a rule-based knowledge inference system that derives theoretical models consistent with expert cognition from interpretive data and prior knowledge. Additionally, we represent the extracted knowledge as a topological semantic knowledge graph, which facilitates computer recognition and allows estimation of intersection lines during 3D geological modeling, resulting in the creation of accurate models. The applicability of our proposed method to various complex geological structures is validated through application tests using real-world data. Furthermore, our method effectively supports the realization of intelligent structure modeling in real working area.

Hydroclimatic changes in eastern China during the Holocene based on pollen data and climate modeling

The Asian monsoon system represents one of the world's most dynamic interactions of the cryosphere-continent-ocean-atmosphere system. Understanding and responding to changes in monsoonal precipitation is a major component of environmental management in this region, given its profound influence on socioeconomic activity in monsoonal Asia. In particular, characterizing the spatiotemporal variability of the East Asian summer monsoon (EASM) is critical for comprehensively understanding its dynamics and future impacts. Although substantial progress has been made in reconstructing past changes in the EASM, the spatial pattern of EASM intensity in China on the orbital-scale remains controversial. This is mainly because of uncertainties in the climatic interpretation of EASM-related proxies. Here, we present a quantitative seasonal precipitation reconstruction for monsoonal China during the Holocene, using the Modern Analog Technique (MAT) based on pollen data. The results show that during the Holocene both the annual and summer precipitation in monsoonal China increased gradually and reached a peak during the early mid-Holocene, with respective values that were ∼ 170 mm and 60 mm higher than today, followed by a decrease in the late Holocene. On the regional scale, the temporal pattern of monsoon rainfall was discrepant: a decrease from the early through late Holocene in northern China, and an increase from the early through late Holocene in southern China.To investigate the possible forcing mechanisms of these phenomena, we used the full TraCE-21 k simulation and single-forcing transient simulations to analyze spatial and temporal rainfall patterns. The results suggest that the north-south pattern is the product of orbital forcing. Intensified summer insolation during the early Holocene enhanced the land-sea thermal contrast, which strengthened the EASM along with the northward extension of the west Pacific subtropical high. Both factors caused the increased withdrawal of oceanic water vapor and the northward advance of the EASM rain belt, delivering more rainfall to northern China and less rainfall to southern China, and vice versa. Overall, our results indicate that insolation variations were the dominant factor determining the spatial pattern of EASM precipitation during the Holocene. Our findings are relevant to the calibration of palaeoclimate models and they contribute to an improved understanding of the Asian monsoon sub-systems.

Seawater intrusion assessment in the Bir Guendouz-Boulanoire coastal transboundary aquifers of Morocco and Mauritania

The Bir Guendouz region, situated in Southern Morocco, is affected by saltwater intrusion. The transboundary aquifer is shared by Morocco and Mauritania, and it serves as the primary source of freshwater in this arid region. To differentiate between freshwater and saltwater areas, 750 non-invasive vertical electrical soundings (VES) were utilized along 42 perpendiculars to the coast. Dar-Zarrouk parameters—transverse resistance (Tr), longitudinal conductance (S), and longitudinal resistivity (ρl)— were determined using VES. These techniques remove uncertainties in resistivity interpretation that may arise due to the overlap between freshwater and saline aquifers during the process of suppression and equivalence. The results indicate a significant marine intrusion in the Bir Guendouz-Boulanoire aquifer, with varying degrees of salinization observed in different zones. Freshwater zones are identified by a transverse resistance of over 4450 Ω m2, a longitudinal conductance below 41.7 mho, and a longitudinal resistivity greater than 8.7 Ω m. These characteristics can be used to prevent saltwater intrusion and ensure the sustainable use of groundwater resources. Furthermore, they will aid in improving resource management between the two nations. The study's consequences also involve safeguarding the ecosystem and ensuring the longevity of these transboundary water resources. Therefore, for researchers, professionals, and decision-makers involved in sustainable management of transboundary groundwater, this study serves as a crucial reference.

Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models

Abstract. Many 3D hydrostratigraphic models of the subsurface are interpreted as deterministic models, where an experienced modeler combines relevant geophysical and geological information with background geological knowledge. Depending on the quality of the information from the input data, the interpretation phase will typically be accompanied by an estimated qualitative interpretation uncertainty. Given the qualitative nature of uncertainty, it is difficult to propagate the uncertainty to groundwater models. In this study, a stochastic-simulation-based methodology to characterize interpretation uncertainty within a manual-interpretation-based layer model is applied in a groundwater modeling setting. Three scenarios with different levels of interpretation uncertainty are generated, and three locations representing different geological structures are analyzed in the models. The impact of interpretation uncertainty on predictions of capture zone area and median travel time is compared to the impact of parameter uncertainty in the groundwater model. The main result is that in areas with thick and large aquifers and low geological uncertainty, the impact of interpretation uncertainty is negligible compared to the hydrogeological parameterization, while it may introduce a significant contribution in areas with thinner and smaller aquifers with high geologic uncertainty. The influence of the interpretation uncertainties is thus dependent on the geological setting as well as the confidence of the interpreter. In areas with thick aquifers, this study confirms existing evidence that if the conceptual model is well defined, interpretation uncertainties within the conceptual model have limited impact on groundwater model predictions.

Separation of the Asian summer monsoon and local precipitation from stalagmite oxygen isotope records in Eastern China during the last millennium

The Asian summer monsoon (ASM) regulates water vapor and heat transport across the East Asian continent and has a significant impact on patterns of regional precipitation. However, assessment of the long-term association between the monsoon and precipitation in different regions remains difficult due to spatial chronological differences and uncertainties in interpretation of climate proxies. This study provides a novel insight into the interpretation of stalagmite oxygen isotopes (δ18O) records and obtained synchronous ASM index series and precipitation variations in Northern China (NC), Central China (CC), and Southwestern China (SWC) over the last 1134 years (850–1983CE). The results show that the ASM index sequence is consistent with the trend evident in the monsoon intensity records from the region upstream of the water vapor source, which represents the response to the inter-hemispheric temperature dominated by effective solar radiation (ESR). Reconstruction precipitation data were in general agreement with the existing paleo-hydrological records of the regions, confirming the reliability and chronological accuracy of the reconstruction. Correlation analysis highlighted the significant regional heterogeneity in the destabilizing relationship between precipitation and the monsoon over the past millennium in Eastern China at sub-centennial scales. Specifically, the precipitation-monsoon relationships in three regions, NC, CC, and SWC, might be related to the North Atlantic Oscillation (NAO), the position of the intertropical convergence zone (ITCZ), and solar activity, respectively. Moreover, at the millennial scale, a certain positive correlation between precipitation and ASM in NC and SWC was identified, whereas there was a weaker correlation between precipitation and ASM in CC. This study represents an attempt to further interpret the stalagmite δ18O record, which can be generalized to larger and longer time scales.

Can an ecological scarcity method for Germany support robust decisions? – analysing the effect of uncertain target values on the impact assessment of energy generation technologies

PurposePotentially contradictory indicators in Life Cycle Assessment cause ambiguity and thus uncertainty regarding the interpretation of results. The weighting-based ecological scarcity method (ESM) aims at reducing interpretation uncertainty by applying policy-based normative target values. However, the definition of these target values is uncertain due to different reasons such as questionable temporal representativeness. By means of an uncertainty analysis, this paper examines if ESMs are an appropriate approach to support robust decisions on multidimensional environmental impacts.MethodsTo assess the effect of uncertain target values (inputs) on environmental indicators (output), the ESM based Life Cycle Impact Assessment (LCIA) is combined with a Monte Carlo Analysis. The comprehensive uncertainty analysis includes the following steps: (1) sample generation, (2) output calculation and (3) results analysis and visualisation. (1) To generate a sample, moderate and strict limits for target values are derived from laws, directives or strategies. Random input parameters are drawn from a uniform distribution within those limits. (2) The sample is used to conduct several LCIAs leading to a distribution of total impact scores. (3) The results’ robustness is evaluated by means of the rank acceptability index to identify stable ranks for energy generation systems taken from ecoinvent v. 3.7.1.Results and discussionApplying moderate and strict target values in the ESM, results in substantial differences in the weighting sets. Even though the application of stricter target values changes the contribution of an environmental indicator to the total impact score the ranking of the energy generation systems varies only slightly. Moreover, the Monte Carlo Analysis reveals that displacement effects in ranks are not arbitrary: systems switch at most between ranks next to each other and most of the analysed systems dominate at least a single rank. Technologies with high shares of land use, global warming and air pollutants and particulate matter show a higher rank variance.ConclusionsThe weighting schemes, deduced from target values, provide a meaningful ranking of alternatives. At the same time, the results are not excessively sensitive to the uncertainties of the target values, i.e. the inherent uncertainty of the target values does not result in arbitrary outcomes, which is necessary to support robust decisions. The ESM is able to effectively facilitate decision making by making different environmental issues comparable.

Purpose definition as a crucial step for determining the legal basis under the GDPR: implications for scientific research.

The General Data Protection Regulation (GDPR) of the European Union, which became applicable in 2018, contains a new accountability principle. Under this principle, controllers (ie parties determining the purposes and the means of the processing of personal data) are responsible for ensuring and demonstrating the overall compliance with the GDPR. However, interpretive uncertainties of the GDPR mean that controllers must exercise considerable judgement in designing and implementing an appropriate compliance strategy, making GDPR compliance both complex and resource-intensive. In this article, we provide conceptual clarity around GDPR compliance with respect to one core aspect of the law: the determination and relevance of the purpose of personal data processing. We derive from the GDPR's text concrete requirements for purpose specification, which we subsequently apply to the area of secondary use of personal data for scientific research. We offer guidance for correctly specifying purposes of data processing under different research scenarios. To illustrate the practical necessity of purpose specification for GDPR compliance, we then show how our proposed approach can enable controllers to meet their compliance obligations, using the example of the overarching GDPR principle of lawfulness to highlight the relevance of purpose specification for the identification of a suitable legal basis.

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.