Interpretation Of Experiments Research Articles

Effects of experience and directionality on cognitive load in dialogue interpreting

Abstract The aim of the study was to investigate the effects of interpreter experience and interpreting direction on cognitive load of dialogue interpreters. The study was conducted on two groups of interpreters (experienced and inexperienced) during a simulated interpreter-mediated encounter and cognitive load was operationalized with disfluency durations, and disfluency counts. The data suggest that both experience and directionality modulate cognitive load in dialogue interpreters. All participants manifested an increase in cognitive load while interpreting into their weaker language (L2), which suggest that directionality had an effect on cognitive load of dialogue interpreters. The results also suggest that inexperienced interpreters are likely to exhibit higher cognitive load compared to experienced interpreters. The study underscores the significance of experience and directionality in understanding the cognitive demands faced by dialogue interpreters, contributing valuable insights to research on interpreting and cognitive load.

Interpreter Intervention: Balancing Objectivity and Subjectivity in Business Negotiations

In the era of economic globalization, interactions and collaborations between China and the global economy have significantly intensified. This trend has led to increased foreign investment in China and a growing number of Chinese enterprises expanding their operations internationally. Successful business negotiations are crucial for these endeavors, yet they face challenges such as linguistic barriers, cultural differences, and divergent thought processes. In such high-stakes scenarios, an experienced interpreter serves as a vital communication link between Chinese and international parties. This paper aims to examine the role of dialogue interpretation in business negotiations and to investigate how interpreters can effectively leverage their subjective judgment alongside strategic translation techniques. By doing so, interpreters can facilitate smoother negotiations and contribute to mutually beneficial outcomes. The study will explore specific strategies that enable interpreters to bridge cultural and linguistic gaps, thereby promoting successful and harmonious negotiations.

Computer-assisted regional-residual gravity anomaly separation with regularized first- and second-order derivatives

The regional-residual separation of gravity anomalies in crustal and mineral exploration was a graphical-based procedure before the advent of fast digital computers and the need for more efficient algorithms to process large data sets. However, since requiring the supervision of an experienced interpreter, the results, once obtained with graphical procedures, are often accepted as second to none in producing anomalies with geologic significance. Numerical methods based on spectral filtering and robust polynomial fitting have worked in many scenarios but seem not fully effective in replicating in algorithms the kind of results once obtained with interpreter-assisted graphical methods. We develop a computer-assisted regional residual separation (CARRS) procedure implemented by a set of short MATLAB scripts; in many aspects, CARRS simulates the operations of former graphical methods but requires few decisions and minor handwork from the interpreter. CARRS applies robust polynomial fitting to points with a low horizontal gradient and a vertical second-order derivative; thus, selecting fitting points as a real interpreter would do with graphical approaches in outlining the regional field. A regularized procedure is used to calculate the stable first- and second-order derivatives. Companion MATLAB codes allow for the replication of our results and further exploration with different threshold levels to identify flat domain regions. CARRS is illustrated with airborne gravity data CPRM-1123, freely available to download. A data window of the residual field is used to analyze the distribution of cassiterite deposits in greisen zones of the Paleoproterozoic Velho Guilherme granites in the Amazon Craton, as their distribution appears in the observed gravity anomaly and its corresponding residual field.

Bipolar disorder, Deafness and Culturality in Psychiatric Home Hospitalization: A Clinical Case

IntroductionMental health in the deaf community is a complex issue. Challenges in diagnosis and treatment arise from a lack of experienced interpreters and difficulties in translating Sign Language to spoken language. Deaf individuals, due to auditory limitations, are more vulnerable to abuse, increasing their risk of mental health disorders, including bipolar affective disorder (BPAD). BPAD is a prevalent, debilitating condition with varied prevalence estimates. Managing it is tough due to its lifelong, unpredictable nature. A new approach called Psychiatric Home Hospitalization Unit aims to provide acute mental health care at home as an alternative to hospitalization.ObjectivesTo show the management of severe bipolar disorder with comorbidity from a Psychiatric Home Hospitalization UnitMethodsA clinical case of bipolar disorder with deafness attended at the Psychiatric Home Hospitalization Unit of our hospital is presented.ResultsA 24-year-old deaf woman borned in Pakistan and later moving to Catalonia, she faced educational challenges but ultimately completed her studies with sign language support. Afterward, she struggled to find suitable employment, and her family had a history of bipolar disorder.She exhibited a sudden change in behavior, characterized by irritability, paranoia, and distrust. Communication was challenging due to her speech difficulties, but assessments using sign language and observation were conducted. Her physical examination was normal, but her speech was disorganized and pressured, suggesting possible auditory hallucinations and thought disturbances. She was hospitalized and diagnosed with bipolar disorder with psychotic features.During her initial hospitalization, she received lithium, olanzapine, clotiapine and benzodiacepines. After discharge, she continued treatment through a home hospitalization service during almost 4 month. During follow-up she presented a course with high affective instability, rapid cycling alternating brief periods of stability with other presenting manic and mixed features with high disorganization.Due to the rapid cycling pattern Valproic acid was considered. Valproic acid was introduced up to 700 mg/d (97.1 mcg/mL). Treatment with lithium carbonate 800 mg/d (0.91 mEq/L) was maintained. Previous antipsychotic regimen was changed to quetiapine 400mg/d, olanzapine 5mg/d. Her condition improved significantly with the adjusted treatment regimen. She was discharged to an outpatient service.ConclusionsDiagnosing and treating bipolar affective disorder (BPAD) in a deaf and mute patient posed unique challenges. The rapid mood cycling pattern and complexity of her case made treatment challenging. Family information and interpreter support were vital. Cultural factors were considered, and home hospitalization was crucial in managing symptoms that lasted over four months.Disclosure of InterestNone Declared

Interpreter and limited-English proficiency patient training helps develop medical and physician assistant students’ cross-cultural communication skills

BackgroundThe increasing linguistic and cultural diversity in the United States underscores the necessity of enhancing healthcare professionals' cross-cultural communication skills. This study focuses on incorporating interpreter and limited-English proficiency (LEP) patient training into the medical and physician assistant student curriculum. This aims to improve equitable care provision, addressing the vulnerability of LEP patients to healthcare disparities, including errors and reduced access. Though training is recognized as crucial, opportunities in medical curricula remain limited.MethodsTo bridge this gap, a novel initiative was introduced in a medical school, involving second-year students in clinical sessions with actual LEP patients and interpreters. These sessions featured interpreter input, patient interactions, and feedback from interpreters and clinical preceptors. A survey assessed the perspectives of students, preceptors, and interpreters.ResultsOutcomes revealed positive reception of interpreter and LEP patient integration. Students gained confidence in working with interpreters and valued interpreter feedback. Preceptors recognized the sessions' value in preparing students for future clinical interactions.ConclusionsThis study underscores the importance of involving experienced interpreters in training students for real-world interactions with LEP patients. Early interpreter training enhances students' communication skills and ability to serve linguistically diverse populations. Further exploration could expand languages and interpretation modes and assess long-term effects on students' clinical performance. By effectively training future healthcare professionals to navigate language barriers and cultural diversity, this research contributes to equitable patient care in diverse communities.

Cross-Regional Seismic Event Discrimination via Convolutional Neural Networks: Exploring Fine-Tuning and Ensemble Averaging

ABSTRACT The gradual densification of seismic networks has facilitated the acquisition of large amounts of data. However, alongside natural tectonic earthquakes, seismic networks also record anthropogenic events such as quarry blasts or other induced events. Identifying and distinguishing these events from natural earthquakes requires experienced interpreters to ensure that seismological studies of natural phenomena are not compromised by anthropogenic events. Advanced artificial intelligence methods have already been deployed to tackle this problem. One of the applications includes Convolutional Neural Networks (CNN) to discriminate different kinds of events, such as natural earthquakes and quarry blasts. In this study, we investigate the effects of ensemble averaging and fine-tuning on seismic event discrimination accuracy to estimate the potential of these methods. We compare discrimination accuracy of two different CNN model architectures across three datasets. This was done with the best models from an ensemble of each model architecture, as well as with ensemble averaging and fine-tuning methods. Soft voting was used for the CNN ensemble predictions. For the transfer learning approach, the models were pretrained with data from two of the datasets (nontarget regions) and fine-tuned with data from the third one (target region). The results show that ensemble averaging and fine-tuning of CNN models leads to better generalization of the model predictions. For the region with the lowest numbers of one event type, the combination of ensemble averaging and fine-tuning led to an increase in discrimination accuracy of up to 4% at station level and up to 10% at event level. We also tested the impact of the amount of training data on the fine-tuning method, showing, that to create a global model, the selection of comprehensive training data is needed.

REDUCTION INTERPRETATION STRATEGIES ON CHURCH SERMONS: A STUDY OF KISWAHILI TO ENGLISH INTERPRETATION IN EVANGELICAL CHURCHES IN GATANGA SUBCOUNTY

This paper examined reduction interpretation strategies on church sermons. Currently, there is an increase need for interpretation in most of the Kenyan churches due to the increase in a number of evangelistic churches in the country. This study examined sermons preached in English and consecutively interpreted to Kiswahili. The study focused on the effect of reduction interpretation on the intended meaning. The study applied interpretive theory of Interpretation as propounded by Marianne (2003). The study was anchored on a descriptive research design where both interviews and questionnaires were used to collect data. Purposive sampling was used to select five churches that used interpreting, in which the sermons were preached in English and interpreted to Kiswahili. The study used a descriptive research design in collecting data from the respondents. The target population was drawn from each of the eight evangelical churches in Gatanga Sub County which consisted of pastors, interpreters and the audience. The study findings revealed that reduction interpretation strategy affect the interpretation of church sermons in Evangelical Churches. The study concludes that Reduction strategies did not meet the goal of interpreting church sermons instead they had a negative impact on the interpreted message. This study recommends that church interpreters should be brought to light to the works of other experienced interpreters as this will add to their knowledge

НЕГАТИВНОСТЬ В РЕЧИ И ЖЕСТАХ СИНХРОННЫХ ПЕРЕВОДЧИКОВ

The article elaborates on the role of negative language triggers for the highly demanding cognitive activity of simultaneous interpreting. Presumably, maintenance, omission or neutralization of negative information expresses a conscious decision of an interpreter. This process is often accompanied by certain co-speech gestures whose kinetic features we also subjected to analysis in this study. The data were collected during an experiment with 16 interpreters (8 from German and 8 from English). The information was annotated in ELAN program with further tests run in the Jamovi software. The results suggest that negative triggers tend to break monotony of interpretation and make interpreters process information more accurately, which is more typical of experienced interpreters. Both categories of interpreters though get emotionally involved, which can be seen by the gestural kinetic features traditionally associated with negativity (away movement, downward movement, palm down orientation, rubbing movement, usage of both hands and gesture space increase). At the same time the predominant part of negative items in the source text was preserved by the interpreters, who also created a “negativity balance”, adding negativity and neutralizing it to equalize the general negativity rate. Important differences associated with the language of the source text were not observed. Thus, a conclusion can be made that negativity disrupts the mechanical character of interpretation making interpreters more aware of the text contents.

Comparing Student Self-assessment and Teacher Assessment in Korean-English Consecutive Interpreting

Self-assessment in interpreter training has been recognized as an important tool to motivate learners and help them learn to evaluate their own performance. The literature on this topic shows that students’ self-assessment ratings positively correlate with trainers’ assessments, but there are certain differences between the two groups in terms of interpretation quality assessment. The present study wishes to contribute to research on self-assessment as a learning tool by comparing students’ self-assessments with teacher assessments in terms of a set of quality categories and identifying in what areas students need more guidance to draw pedagogical implications. For data collection, 20 first-year graduate students of Korean-English interpretation conducted self-assessment of their sentence-by-sentence consecutive interpretation in both directions. Two broad quality categories were applied, which are fidelity to the source text and target language adequacy, along with the three sub-categories of fidelity which are omissions, misinterpretations, and additions. An experienced interpreter trainer was recruited to perform assessment of the students’ consecutive interpretations based on the same quality categories. The results show that the students tended to focus more on target language quality in both directions while the teacher applied stricter criteria in evaluating fidelity of the interpretations. For instance, the study found several instances where students marked parts of their interpretations as target language errors while the trainer marked them as fidelity errors such as omissions and misinterpretations. The results suggest that the students were not consistent and reliable detectors of errors in their interpretation performance during self-assessment. This may be partly attributable to the fact that the participants were in the second semester of the two-year long graduate program, and in the process of learning how to evaluate the quality of interpretation accurately. Pedagogical implications of the findings are discussed, including the kind of guidance needed for students to learn how to conduct self-assessment more successfully.

Reference Data Accuracy Impacts Burned Area Product Validation: The Role of the Expert Analyst

Accurate reference data to validate burned area (BA) products are crucial to obtaining reliable accuracy metrics for such products. However, the accuracy of reference data can be affected by numerous factors; hence, we can expect some degree of deviation with respect to real ground conditions. Since reference data are usually produced by semi-automatic methods, where human-based image interpretation is an important part of the process, in this study, we analyze the impact of the interpreter on the accuracy of the reference data. Here, we compare the accuracy metrics of the FireCCI51 BA product obtained from reference datasets that were produced by different analysts over 60 sites located in tropical regions of South America. Additionally, fire severity, tree cover percentage, and canopy height were selected as explanatory sources of discrepancies between interpreters’ reference BA classifications. We found significant differences between the FireCCI51 accuracy metrics obtained with the different reference datasets. The highest accuracies (highest Dice coefficient) were obtained with the reference dataset produced by the most experienced interpreter. The results indicated that fire severity is the main source of discrepancy between interpreters. Disagreement between interpreters was more likely to occur in areas with low fire severity. We conclude that the training and experience of the interpreter play a crucial role in guaranteeing the quality of the reference data.

Automated active learning in seismic image interpretation

Three-dimensional seismic interpretation has been significantly accelerated by the recent implementation of various machine learning algorithms, particularly supervised convolutional neural networks (CNNs). CNNs are able to parse seismic data from the perspective of pattern recognition, extract seismic features at multiple scales, and provide acceptable predictions. The performance of a supervised CNN in seismic image interpretation greatly depends on its training labels, which are usually a set of seismic sections with expert annotations. Among the thousands of sections in a typical 3D seismic cube, effectively selecting those that are most representative is a challenging task. A common approach is to have an experienced interpreter visually screen all of the sections and make their selection. To improve the efficiency of training section selection and to avoid introducing bias from manual screening, this work proposes an automated-active-learning (AutoAL) workflow for interactive seismic image interpretation. This enables quantitatively evaluating the machine performance after one iteration and efficiently recommending the sections to be labeled for learning in the next iteration. The added value of the proposed approach is validated through an application of seismic facies classification to the Parihaka data set in the northwest part of offshore Taranaki Basin in New Zealand. Starting from four initial sections, in three iterations the proposed AutoAL automatically recommends 14 from more than 1300 sections as training data. This improves the accuracy and average F1 of the machine prediction over 0.9. Comparisons demonstrate better prediction by the proposed scheme over traditional training section selection schemes, such as manual screening and clustering-based recommendation.

Facies prediction with Bayesian inference: Application of supervised and semisupervised deep learning

Accurate delineation of geologic facies and determination of live fluids from seismic reflection data is crucial for reservoir characterization during petroleum exploration. Facies classification or fluid identification is often done manually by an experienced interpreter, which makes this process subjective, laborious, and time-consuming. Several machine-learning models have been proposed to automate multiclass facies segmentation, but significant practical challenges (e.g., limited scope of labels for training purposes, skewed data distribution, inefficient performance evaluation metrics, etc) still remain. We present supervised and semisupervised Bayesian deep-learning methodologies to improve analysis of seismic facies depending on the scope of the labeled data. The developed networks reliably predict facies distribution using seismic reflection data and estimate the corresponding uncertainty. Therefore, they provide more consistent and meaningful information for seismic interpretation than commonly used deterministic approaches. We apply the proposed deep-learning models to field data from the North Sea to demonstrate the generalized-prediction capabilities of our methodology. In the case of sufficient availability of manually interpreted labels (or facies), the supervised learning model accurately recovers the facies distribution. When the amount of the interpreted labels is limited, we efficiently apply the semisupervised algorithm to avoid overfitting.

Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands

Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and derisking activities. Efforts to automate the detection and extraction of geologic features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine-learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, northeast Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D prestack depth migrated seismic data set. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geologic context to the interpreter and input to dependent workflows more efficiently.

A Simple Gain-Based Evaluation of the Video Head Impulse Test Reliably Detects Normal Vestibulo-Ocular Reflex Indicative of Stroke in Patients With Acute Vestibular Syndrome

Objective: The head impulse test (HIT) assesses the vestibulo-ocular reflex (VOR) and is used to differentiate vestibular neuritis (abnormal VOR) from stroke (normal VOR) in patients presenting with an acute vestibular syndrome (AVS). The video-oculography-based HIT (vHIT) quantifies VOR function and provides information imperceptible for the clinician during clinical bedside HIT. However, the vHIT—like an electrocardiogram—requires experienced interpretation, which is especially difficult in the emergency setting. This calls for a simple, reliable and rater-independent way of analysis.Methods: We retrospectively collected 171 vHITs performed in patients presenting with AVS to our emergency department. Three neuro-otological experts comprehensively assessed the vHITs including interpretability (artifacts), VOR gain (eye/head velocity ratio), velocity profile (abrupt decline) and corrective saccades (overt/covert). Their consensus rating (abnormal/peripheral vs. normal/central) was compared to a simple algorithm that automatically classified the vHITs based on a single VOR gain cutoff (0.7).Results: Inter-rater agreement between experts was high (Fleiss' kappa = 0.74). Five (2.9 %) vHITs were “uninterpretable” according to experts' consensus, 80 (46.8 %) were rated “normal” and 86 (50.3 %) “abnormal”. The algorithm had substantial agreement with the experts' consensus (Cohen's kappa = 0.75). Importantly, it correctly classified all of the normal/central vHITs denoted by the experts (100% specificity) and at the same time it had sufficient sensitivity (75.6%) in detecting abnormal/peripheral vHITs.Conclusion: A simple, automated, gain-based evaluation of the vHIT reliably detects normal/central VOR and may be a feasible and effective tool to screen AVS patients for potentially underlying stroke in the emergency setting.

Optical Mapping of Brain Activity Underlying Directionality and Its Modulation by Expertise in Mandarin/English Interpreting.

Recent neuroimaging research has suggested that unequal cognitive efforts exist between interpreting from language 1 (L1) to language 2 (L2) compared with interpreting from L2 to L1. However, the neural substrates that underlie this directionality effect are not yet well understood. Whether directionality is modulated by interpreting expertise also remains unknown. In this study, we recruited two groups of Mandarin (L1)/English (L2) bilingual speakers with varying levels of interpreting expertise and asked them to perform interpreting and reading tasks. Functional near-infrared spectroscopy (fNIRS) was used to collect cortical brain data for participants during each task, using 68 channels that covered the prefrontal cortex and the bilateral perisylvian regions. The interpreting-related neuroimaging data was normalized by using both L1 and L2 reading tasks, to control the function of reading and vocalization respectively. Our findings revealed the directionality effect in both groups, with forward interpreting (from L1 to L2) produced more pronounced brain activity, when normalized for reading. We also found that directionality was modulated by interpreting expertise in both normalizations. For the group with relatively high expertise, the activated brain regions included the right Broca’s area and the left premotor and supplementary motor cortex; whereas for the group with relatively low expertise, the activated brain areas covered the superior temporal gyrus, the dorsolateral prefrontal cortex (DLPFC), the Broca’s area, and visual area 3 in the right hemisphere. These findings indicated that interpreting expertise modulated brain activation, possibly because of more developed cognitive skills associated with executive functions in experienced interpreters.

A review of seismic attribute taxonomies, discussion of their historical use, and presentation of a seismic attribute communication framework using data analysis concepts

Beginning in the 1970s, seismic attributes have grown from a few simple measurements of wavelet amplitude, frequency, and phase to an expanded attribute toolbox that measures not only wavelet properties but also their context within the 3D seismic volume. When using multiple seismic attributes, the interpreter must understand not only each individual attribute but also the relationships between them. Researchers communicate these relationships via seismic attribute taxonomies, which group attributes by their signal property, mathematical formulation, or their interpretive value. The first attempts to organize seismic attributes began in the 1990s, and with new attributes and their increasing breadth of applications, continues to this day. Most scientific papers that use seismic attributes focus on a specific application, new algorithms, or a novel interpretation workflow, rather than how a specific attribute fits within the greater whole, leading to confusion for the less experienced interpreter. We have analyzed more than 2100 citing works, identified the 231 papers that discuss the taxonomies specifically, and found out how the authors use those citations. The result is a list of more than a dozen seismic attribute classification systems, which we reduce to a smaller subset by including only those that apply to general use. An optimal seismic attribute taxonomy should not only be useful to the interpretation community today, but it should also adapt to the ever-changing needs of the profession, including changes appropriate for their use in modern machine-learning algorithms. The adaptability of prior work to modern workflows remains a shortcoming. However, as we develop our work in two parts — the first covering the evolution of seismic attribute taxonomies and their use through time and the second proposing a new seismic attribute communication framework for the larger community — we link attributes together via data analysis principles and provide an extensible model as the profession and research expand.

Selective Attention Measurement of Experienced Simultaneous Interpreters Using EEG Phase-Locked Response.

We quantified the electroencephalogram signals associated with the selective attention processing of experienced simultaneous interpreters and calculated the phase-locked responses evoked by a 40-Hz auditory steady-state response (40-Hz ASSR) and the values of robust inter-trial coherence (ITC) for environmental changes. Since we assumed that an interpreter's attention ability improves with an increase in the number of years of experience of simultaneous interpretation, we divided the participants into two groups based on their simultaneous interpretation experience: experts with more than 15 years of experience (E group; n = 7) and beginners with <1 year (B group; n = 15). We also compared two conditions: simultaneous interpretation (SI) and shadowing (SH). We found a significant interaction in the ITC between years of SI experience (E and B groups) and tasks (SI and SH). This result demonstrates that the number of years of SI experience influences selective attention during interpretation.

Imposing interpretational constraints on a seismic interpretation convolutional neural network

With the expanding size of 3D seismic data, manual seismic interpretation becomes time-consuming and labor-intensive. For automating this process, recent progress in machine learning, in particular the convolutional neural network (CNN), has been introduced into the seismic community and successfully implemented for interpreting seismic structural and stratigraphic features. In principle, such automation aims at mimicking the intelligence of experienced seismic interpreters to annotate subsurface geology accurately and efficiently. However, most of the implementations and applications are relatively simple in their CNN architectures, which primary rely on the seismic amplitude but undesirably fail to fully use the preknown geologic knowledge and/or solid interpretational rules of an experienced interpreter who works on the same task. We have developed a generally applicable framework for integrating a seismic interpretation CNN with such commonly used knowledge and rules as constraints. Three example use cases, including relative geologic time-guided facies analysis, layer-customized fault detection, and fault-oriented stratigraphy mapping, are provided for illustrating how one or more constraints can be technically imposed and demonstrating what added values such a constrained CNN can bring. It is concluded that the imposition of interpretational constraints is capable of improving CNN-assisted seismic interpretation and better assisting the tasks of subsurface mapping and modeling.

Numerical simulation of ground‐penetrating radar data for studying the geometry of fault zone

ABSTRACTPalaeoseismology studies the footprints of ancient earthquakes to improve the knowledge about the modern seismicity of the territory. A ground‐penetrating radar (GPR), among other geophysical methods, is used for quick determination of shallow stratigraphy – displaced, oblique layers within the fault zone. GPR data interpretation from diverse and complex reflection patterns of the fault zone heavily depends on the interpreter's experience. The range of different fault zone parameters in which this method can be successfully applied has not yet been investigated. We used a numerical simulation of GPR data to determine how GPR images the elements of faults (fault plane, hanging wall, footwall) in comparison with other reflections. Furthermore, we studied which parameters have the most significant impact on GPR wave patterns. We performed a series of numerical models of a fault, changing its geometry with increasing complexity from elementary models to realistic ones. The resulting synthetic profiles allowed finding specific GPR signatures from the fault plane, the hanging wall and the footwall. We collected field GPR data from two different fault zones and examined them for verification.

Implementasi Algoritme Support Vector Machines untuk Klasifikasi Area Terbakar di Lahan Gambut

Kebakaran hutan telah menjadi bencana tahunan di Indonesia, yang berdampak pada degradasi lahan. Kebakaran hutan banyak terjadi di lahan gambut. Pada bulan Agustus 2019 terdeteksi sebanyak 810 hotspot di Provinsi Jambi. Informasi luas area kebakaran diperlukan untuk menentukan kebijakan dalam pengelolaan hutan dan lahan. Informasi mengenai luas area kebakaran sulit didapatkan dari pengukuran lapangan karena area yang luas dan tidak mudah diakses. Data Landsat merupakan salah satu jenis citra dari teknologi remote sensing yang dapat digunakan untuk memetakan luas area kebakaran. Salah satu metode yang sering digunakan untuk mengestimasi luas area kebakaran adalah dengan teknik interpretasi visual on screen. Namun, teknik ini memerlukan tenaga interpreter yang berpengalaman. Untuk itu, pada makalah ini digunakan teknik interpretasi digital menggunakan algoritme Support Vector Machine (SVM) untuk mengklasifikasikan area kebakaran di Kabupaten Muaro Jambi pada ekosistem gambut indikatif fungsi lindung, kemudian menghitung estimasi luasan lahan vegetasi, lahan terbuka, dan area terbakar. Makalah ini menghasilkan klasifikasi dengan akurasi sebesar 99,8%. Estimasi luas gambut berdasarkan klasifikasi SVM pada tanggal 15 Agustus 2019 adalah 1.396,89 ha teridentifikasi sebagai area terbakar, 7.069,5 ha adalah area vegetasi, dan 1.089,54 ha merupakan area lahan terbuka.