{"id":300839,"name":"WATex","description":"A Python-based library primarily designed for Groundwater Exploration.","url":"https://github.com/earthai-tech/watex","last_synced_at":"2026-04-22T12:30:16.661Z","repository":{"id":37086152,"uuid":"368390300","full_name":"earthai-tech/watex","owner":"earthai-tech","description":"machine learning research in water exploration","archived":false,"fork":false,"pushed_at":"2025-02-25T12:58:18.000Z","size":145429,"stargazers_count":16,"open_issues_count":1,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-04-17T10:03:30.958Z","etag":null,"topics":["geophysics","hydrogeophysics","machine-learning","python","water"],"latest_commit_sha":null,"homepage":"https://watex.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/earthai-tech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-05-18T03:31:40.000Z","updated_at":"2025-10-09T05:57:35.000Z","dependencies_parsed_at":"2022-07-14T00:50:33.550Z","dependency_job_id":"0670c4e0-610f-4611-82e8-30af467e70a6","html_url":"https://github.com/earthai-tech/watex","commit_stats":{"total_commits":1039,"total_committers":4,"mean_commits":259.75,"dds":0.260827718960539,"last_synced_commit":"53c8a63f2edd2a16ef12c06112d5156138c7546f"},"previous_names":["earthai-tech/watex","wegeophysics/watex"],"tags_count":25,"template":false,"template_full_name":null,"purl":"pkg:github/earthai-tech/watex","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/earthai-tech","download_url":"https://codeload.github.com/earthai-tech/watex/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32091053,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-21T11:25:29.218Z","status":"ssl_error","status_checked_at":"2026-04-21T11:25:28.499Z","response_time":128,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"owner":{"login":"earthai-tech","name":"lkouadio","uuid":"59920007","kind":"user","description":"Computational Geophysicist |  Lecturer @ AI for Earth Systems","email":"etanoyau@gmail.com","website":"https://lkouadio.com/","location":"Cote d'Ivoire","twitter":null,"company":"@water4all","icon_url":"https://avatars.githubusercontent.com/u/59920007?u=d199134ee6e3d0053d4ee4998543949e74d554fc\u0026v=4","repositories_count":18,"last_synced_at":"2026-04-15T04:27:36.563Z","metadata":{"has_sponsors_listing":false},"html_url":"https://github.com/earthai-tech","funding_links":[],"total_stars":80,"followers":27,"following":10,"created_at":"2022-11-14T09:39:59.769Z","updated_at":"2026-04-15T04:27:36.563Z","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/earthai-tech","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/earthai-tech/repositories"},"packages":[{"id":11729516,"name":"github.com/earthai-tech/watex","ecosystem":"go","description":null,"homepage":null,"licenses":"bsd-3-clause","normalized_licenses":["BSD-3-Clause"],"repository_url":"https://github.com/earthai-tech/watex","keywords_array":[],"namespace":null,"versions_count":21,"first_release_published_at":"2022-06-15T15:37:51.000Z","latest_release_published_at":"2024-03-14T05:08:45.000Z","latest_release_number":"v0.3.3","last_synced_at":"2026-04-19T11:00:42.924Z","created_at":"2025-06-04T10:54:57.022Z","updated_at":"2026-04-19T11:00:42.924Z","registry_url":"https://pkg.go.dev/github.com/earthai-tech/watex","install_command":"go get github.com/earthai-tech/watex","documentation_url":"https://pkg.go.dev/github.com/earthai-tech/watex#section-documentation","metadata":{},"repo_metadata":{"id":37086152,"uuid":"368390300","full_name":"earthai-tech/watex","owner":"earthai-tech","description":"machine learning research in water exploration","archived":false,"fork":false,"pushed_at":"2025-02-25T12:58:18.000Z","size":145429,"stargazers_count":16,"open_issues_count":1,"forks_count":5,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-10T15:32:01.031Z","etag":null,"topics":["geophysics","hydrogeophysics","machine-learning","python","water"],"latest_commit_sha":null,"homepage":"https://watex.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/earthai-tech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-05-18T03:31:40.000Z","updated_at":"2025-10-09T05:57:35.000Z","dependencies_parsed_at":"2022-07-14T00:50:33.550Z","dependency_job_id":"da346496-0f2a-467b-b5dd-4f7dbf6bb13f","html_url":"https://github.com/earthai-tech/watex","commit_stats":{"total_commits":1039,"total_committers":4,"mean_commits":259.75,"dds":0.260827718960539,"last_synced_commit":"53c8a63f2edd2a16ef12c06112d5156138c7546f"},"previous_names":["earthai-tech/watex","wegeophysics/watex"],"tags_count":25,"template":false,"template_full_name":null,"purl":"pkg:github/earthai-tech/watex","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/earthai-tech","download_url":"https://codeload.github.com/earthai-tech/watex/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006444,"owners_count":26084107,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"},"tags":[]},"repo_metadata_updated_at":"2025-10-12T04:58:16.754Z","dependent_packages_count":0,"downloads":null,"downloads_period":null,"dependent_repos_count":0,"rankings":{"downloads":null,"dependent_repos_count":5.764254255794306,"dependent_packages_count":5.401293307900869,"stargazers_count":null,"forks_count":null,"docker_downloads_count":null,"average":5.582773781847587},"purl":"pkg:golang/github.com/earthai-tech/watex","advisories":[],"docker_usage_url":"https://docker.ecosyste.ms/usage/go/github.com/earthai-tech/watex","docker_dependents_count":null,"docker_downloads_count":null,"usage_url":"https://repos.ecosyste.ms/usage/go/github.com/earthai-tech/watex","dependent_repositories_url":"https://repos.ecosyste.ms/api/v1/usage/go/github.com/earthai-tech/watex/dependencies","status":null,"funding_links":[],"critical":null,"issue_metadata":{"last_synced_at":"2025-08-31T20:06:02.459Z","issues_count":0,"pull_requests_count":101,"avg_time_to_close_issue":null,"avg_time_to_close_pull_request":369.94,"issues_closed_count":0,"pull_requests_closed_count":100,"pull_request_authors_count":2,"issue_authors_count":0,"avg_comments_per_issue":null,"avg_comments_per_pull_request":0.009900990099009901,"merged_pull_requests_count":100,"bot_issues_count":0,"bot_pull_requests_count":1,"past_year_issues_count":0,"past_year_pull_requests_count":0,"past_year_avg_time_to_close_issue":null,"past_year_avg_time_to_close_pull_request":null,"past_year_issues_closed_count":0,"past_year_pull_requests_closed_count":0,"past_year_pull_request_authors_count":0,"past_year_issue_authors_count":0,"past_year_avg_comments_per_issue":null,"past_year_avg_comments_per_pull_request":null,"past_year_bot_issues_count":0,"past_year_bot_pull_requests_count":0,"past_year_merged_pull_requests_count":0,"issues_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/issues","maintainers":[{"login":"earthai-tech","count":100,"url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/authors/earthai-tech"}],"active_maintainers":[]},"versions_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/packages/github.com%2Fearthai-tech%2Fwatex/versions","version_numbers_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/packages/github.com%2Fearthai-tech%2Fwatex/version_numbers","dependent_packages_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/packages/github.com%2Fearthai-tech%2Fwatex/dependent_packages","related_packages_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/packages/github.com%2Fearthai-tech%2Fwatex/related_packages","codemeta_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/packages/github.com%2Fearthai-tech%2Fwatex/codemeta","maintainers":[],"registry":{"name":"proxy.golang.org","url":"https://proxy.golang.org","ecosystem":"go","default":true,"packages_count":2103565,"maintainers_count":0,"namespaces_count":782439,"keywords_count":112823,"github":"golang","metadata":{"funded_packages_count":53495},"icon_url":"https://github.com/golang.png","created_at":"2022-04-04T15:19:22.939Z","updated_at":"2026-04-19T05:14:45.920Z","packages_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/packages","maintainers_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/maintainers","namespaces_url":"https://packages.ecosyste.ms/api/v1/registries/proxy.golang.org/namespaces"}}],"commits":{"id":1866849,"full_name":"earthai-tech/watex","default_branch":"master","total_commits":1076,"total_committers":4,"total_bot_commits":7,"total_bot_committers":1,"mean_commits":269.0,"dds":0.2620817843866171,"past_year_total_commits":0,"past_year_total_committers":0,"past_year_total_bot_commits":0,"past_year_total_bot_committers":0,"past_year_mean_commits":0.0,"past_year_dds":0.0,"last_synced_at":"2026-04-19T11:01:12.362Z","last_synced_commit":"e862fec4e6b90e2ae8784f4619cab87bded18229","created_at":"2024-09-27T00:12:15.001Z","updated_at":"2026-04-19T11:00:54.534Z","committers":[{"name":"WEgeophysics","email":"etanoyau@gmail.com","login":"earthai-tech","count":794},{"name":"Daniel03","email":"59920007+WEgeophysics","login":"WEgeophysics","count":273},{"name":"dependabot[bot]","email":"49699333+dependabot[bot]","login":"dependabot[bot]","count":7},{"name":"Daniel","email":"daniel@pop-os.localdomain","login":"changdaniel","count":2}],"past_year_committers":[],"commits_url":"https://commits.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/commits","host":{"name":"GitHub","url":"https://github.com","kind":"github","last_synced_at":"2026-04-21T00:00:07.949Z","repositories_count":6215266,"commits_count":899445528,"contributors_count":34906366,"owners_count":1143777,"icon_url":"https://github.com/github.png","host_url":"https://commits.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://commits.ecosyste.ms/api/v1/hosts/GitHub/repositories"}},"issues_stats":{"full_name":"earthai-tech/watex","html_url":"https://github.com/earthai-tech/watex","last_synced_at":"2026-03-25T23:01:00.345Z","status":"error","issues_count":0,"pull_requests_count":101,"avg_time_to_close_issue":null,"avg_time_to_close_pull_request":369.94,"issues_closed_count":0,"pull_requests_closed_count":100,"pull_request_authors_count":2,"issue_authors_count":0,"avg_comments_per_issue":null,"avg_comments_per_pull_request":0.009900990099009901,"merged_pull_requests_count":100,"bot_issues_count":0,"bot_pull_requests_count":1,"past_year_issues_count":0,"past_year_pull_requests_count":0,"past_year_avg_time_to_close_issue":null,"past_year_avg_time_to_close_pull_request":null,"past_year_issues_closed_count":0,"past_year_pull_requests_closed_count":0,"past_year_pull_request_authors_count":0,"past_year_issue_authors_count":0,"past_year_avg_comments_per_issue":null,"past_year_avg_comments_per_pull_request":null,"past_year_bot_issues_count":0,"past_year_bot_pull_requests_count":0,"past_year_merged_pull_requests_count":0,"created_at":"2024-09-27T00:12:15.350Z","updated_at":"2026-03-25T23:01:00.345Z","repository_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex","issues_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/repositories/earthai-tech%2Fwatex/issues","issue_labels_count":{},"pull_request_labels_count":{"dependencies":1,"python":1},"issue_author_associations_count":{},"pull_request_author_associations_count":{"OWNER":100,"CONTRIBUTOR":1},"issue_authors":{},"pull_request_authors":{"earthai-tech":100,"dependabot[bot]":1},"host":{"name":"GitHub","url":"https://github.com","kind":"github","last_synced_at":"2026-04-21T00:00:07.919Z","repositories_count":14347796,"issues_count":34449597,"pull_requests_count":112772519,"authors_count":11241686,"icon_url":"https://github.com/github.png","host_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/repositories","owners_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/owners","authors_url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/authors"},"past_year_issue_labels_count":{},"past_year_pull_request_labels_count":{},"past_year_issue_author_associations_count":{},"past_year_pull_request_author_associations_count":{},"past_year_issue_authors":{},"past_year_pull_request_authors":{},"maintainers":[{"login":"earthai-tech","count":100,"url":"https://issues.ecosyste.ms/api/v1/hosts/GitHub/authors/earthai-tech"}],"active_maintainers":[]},"events":{"total":{"ForkEvent":1,"WatchEvent":1,"PushEvent":2},"last_year":{}},"keywords":["geophysics","hydrogeophysics","machine-learning","python","water"],"dependencies":[],"score":null,"created_at":"2024-09-27T00:12:06.109Z","updated_at":"2026-04-22T12:30:16.676Z","avatar_url":"https://github.com/earthai-tech.png","language":"Python","category":"Hydrosphere","sub_category":"Freshwater and Hydrology","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003cimg src=\"docs/_static/logo_wide_rev.svg\"\u003e\u003cbr\u003e\r\n\r\n-----------------------------------------------------\r\n\r\n# *WATex*: machine learning research in water exploration\r\n\r\n### *Life is much better with potable water*\r\n\r\n [![Documentation Status](https://readthedocs.org/projects/watex/badge/?version=latest)](https://watex.readthedocs.io/en/latest/?badge=latest)\r\n ![GitHub](https://img.shields.io/github/license/WEgeophysics/watex?color=blue\u0026label=Licence\u0026logo=Github\u0026logoColor=blue\u0026style=flat-square)\r\n ![GitHub Workflow Status (with branch)](https://img.shields.io/github/actions/workflow/status/WEgeophysics/watex/ci.yaml?label=CI%20-%20Build%20\u0026logo=github\u0026logoColor=g)\r\n[![Coverage Status](https://coveralls.io/repos/github/WEgeophysics/watex/badge.svg?branch=master)](https://coveralls.io/github/WEgeophysics/watex?branch=master)\r\n ![GitHub release (latest SemVer including pre-releases)](https://img.shields.io/github/v/release/WEgeophysics/watex?color=blue\u0026include_prereleases\u0026logo=python)\r\n [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7744732.svg)](https://doi.org/10.5281/zenodo.7744732)\r\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/watex?logo=pypi)\r\n [![PyPI version](https://badge.fury.io/py/watex.svg)](https://badge.fury.io/py/watex)\r\n[![Conda Version](https://img.shields.io/conda/vn/conda-forge/watex.svg)](https://anaconda.org/conda-forge/watex)\r\n[![Anaconda-Server Badge](https://anaconda.org/conda-forge/watex/badges/platforms.svg)](https://anaconda.org/conda-forge/watex)\r\n\r\n\r\n## Overview\r\n\r\n*WATex* is a Python-based library primarily designed for Groundwater Exploration (GWE). It introduces innovative strategies aimed at minimizing losses encountered during hydro-geophysical exploration projects. Integrating methods from Direct-current (DC) resistivity—including Electrical Profiling (ERP) and Vertical Electrical Sounding (VES)—alongside short-period electromagnetic (EM), geology, and hydrogeology, *WATex* leverages Machine Learning techniques to enhance exploration outcomes. Key features include:\r\n- Automating the identification of optimal drilling locations to reduce the incidence of unsuccessful drillings and unsustainable boreholes.\r\n- Predicting well water content, including groundwater flow rates and water inrush levels.\r\n- Restoring EM signal integrity in areas plagued by significant interference noise.\r\n- And more.\r\n\r\n## Documentation\r\n\r\nFor comprehensive information and additional resources, visit the [WATex library website](https://watex.readthedocs.io/en/latest/). To quickly navigate through the software's API reference, access the [API reference page](https://watex.readthedocs.io/en/latest/api_references.html). Explore the [examples section](https://watex.readthedocs.io/en/latest/glr_examples/index.html) for a preview of potential results. Additionally, a detailed [step-by-step guide](https://watex.readthedocs.io/en/latest/glr_examples/applications/index.html#applications-step-by-step-guide) is provided to tackle real-world engineering challenges, such as computing DC parameters and predicting the k-parameter.\r\n\r\n## License\r\n\r\n*WATex* is distributed under the [BSD-3-Clause License](https://opensource.org/licenses/BSD-3-Clause).\r\n\r\n## Installation\r\n\r\n*WATex* is best supported on Python 3.9 or later.\r\n\r\n### From *pip*\r\n\r\nInstall *WATex* directly from the Python Package Index (PyPI) with the following command:\r\n\r\n```bash\r\npip install watex\r\n```\r\n### From *conda*\r\n\r\nFor users who prefer the conda ecosystem, *WATex* can be installed from the conda-forge distribution channel:\r\n\r\n```bash\r\nconda install -c conda-forge watex\r\n```\r\n\r\n### From Source\r\n\r\nTo access the most current development version of the code, installation from the source is recommended. Use the following commands to clone the repository and install:\r\n```bash\r\ngit clone https://github.com/WEgeophysics/watex.git\r\n```\r\n\r\n### Additional Information\r\n\r\nFor a comprehensive installation guide, including how to manage dependencies effectively, \r\nplease refer to our [Installation Guide](https://watex.readthedocs.io/en/latest/installation.html).\r\n\r\n\r\n## Some Demos\r\n\r\n### 1. Drilling Location Auto-detection\r\n\r\nIn this demonstration, we showcase the process of automatically detecting optimal locations \r\nfor drilling by generating 50 stations of synthetic ERP resistivity data. The data is characterized \r\nby minimum and maximum resistivity values set at `10 ohm.m` and `10,000 ohm.m`, respectively:\r\n\r\n```python\r\nimport watex as wx\r\ndata = wx.make_erp(n_stations=50, max_rho=1e4, min_rho=10., as_frame=True, seed=42)\r\n```\r\n\r\n#### Naive Auto-detection (NAD)\r\n\r\nThe NAD method identifies a suitable drilling location without considering any restrictions or \r\nconstraints that might be present at the survey site during Groundwater Exploration (GWE). A location \r\nis deemed \"suitable\" if it is expected to yield a flow rate of at least 1m³/hr:\r\n\r\n```python\r\nfrom watex.methods import ResistivityProfiling\r\nrobj = ResistivityProfiling(auto=True).fit(data)\r\nrobj.sves_\r\nOut[1]: 'S025'\r\n```\r\n\r\nThe algorithm proposes station `S25` as the optimal drilling location, which is stored \r\nin the `sves_` attribute.\r\n\r\n#### Auto-detection with Constraints (ADC)\r\n\r\nIn contrast, the ADC method accounts for constraints observed in the survey area during \r\nthe Drilling Water Supply Chain (DWSC). These constraints are often encountered in real-world \r\nscenarios. For example, a station near a heritage site may be excluded due to drilling restrictions. \r\nWhen multiple constraints exist, they should be compiled into a dictionary detailing the reasons for \r\neach and passed to the `constraints` parameter. This ensures that these stations are disregarded during \r\nthe automatic detection process:\r\n\r\n```python\r\nrestrictions = {\r\n    'S10': 'Household waste site, avoid contamination',\r\n\r\n    'S27': 'Municipality site, no authorization for drilling',\r\n    'S29': 'Heritage site, drilling prohibited',\r\n    'S42': 'Anthropic polluted place, potential future contamination risk',\r\n    'S46': 'Marsh zone, likely borehole dry-up during dry season'\r\n}\r\nrobj = ResistivityProfiling(constraints=restrictions, auto=True).fit(data)\r\nrobj.sves_\r\n# Output: 'S033'\r\n```\r\nThis method revises the suitable drilling location to station `S33`, taking into account \r\nthe specified constraints. Should a station be near a restricted area, the system raises a warning \r\nto advise against risking drilling operations at that location.\r\n\r\n**Important Reminder:** Prior to initiating drilling operations, ensure a DC-sounding (VES) is conducted at the identified location. *WATex* calculates an additional parameter known as `ohmic-area` (ohmS) to evaluate the presence and effectiveness of fracture zones at that site. For further information, refer to the [WATex documentation](https://watex.readthedocs.io/en/latest/).\r\n\r\n\r\n### 2. EM Tensor Recovery and Analysis\r\n\r\nThis demonstration outlines the process of recovering and analyzing electromagnetic (EM) tensor data. \r\nWe begin by fetching 20 audio-frequency magnetotelluric (AMT) data points stored as EDI objects \r\nfrom the Huayuan area in Hunan Province, China, known for multiple interference noises:\r\n\r\n```python\r\nimport watex as wx\r\ne = wx.fetch_data('huayuan', samples=20, key='noised')  # Returns an EM object\r\nedi_data = e.data  # Retrieve the array of EDI objects\r\n```\r\n\r\nBefore restoring EM data, it's crucial to assess the data quality and evaluate the confidence \r\nintervals to ensure reliability at each station. Typically, this quality control (QC) analysis \r\nfocuses on errors within the resistivity tensor:\r\n\r\n```python\r\nfrom watex.methods import EMAP\r\npo = EMAP().fit(edi_data)  # Creates an EM Array Profiling processing object\r\nr = po.qc(tol=0.2, return_ratio=True)  # Good data deemed from 80% significance level\r\nr\r\nOut[9]: 0.95\r\n```\r\n\r\nTo visualize the confidence intervals at the 20 AMT stations:\r\n\r\n```python\r\nfrom watex.utils import plot_confidence_in\r\nplot_confidence_in(edi_data)\r\n```\r\n\r\nFor a more thorough quality control, we use the `qc` function to filter out invalid data and \r\ninterpolate frequencies. To determine the number of frequencies dropped during this analysis:\r\n\r\n```python\r\nfrom watex.utils import qc\r\nQCo = qc(edi_data, tol=.2, return_qco=True)  # Returns the quality control object\r\nlen(e.emo.freqs_)  # Original number of frequencies in noisy data\r\nOut[10]: 56\r\nlen(QCo.freqs_)  # Number of frequencies in valid data after QC\r\nOut[11]: 53\r\nQCo.invalid_freqs_  # Frequencies discarded based on the tolerance parameter\r\nOut[12]: array([81920.0, 48.53, 5.625])  # 81920.0, 48.53, and 5.625 Hz\r\n```\r\n\r\nThe `plot_confidence_in` function is crucial for assessing whether tensor values for these \r\nfrequencies are recoverable at each station. It's important to note that data is considered \r\nunrecoverable if the confidence level falls below 50%.\r\n\r\nShould the initial QC rate of 95% not meet our standards, we can proceed to restore the \r\nimpedance tensor `Z`:\r\n\r\n```python\r\nZ = po.zrestore()  # Returns 3D tensors for XX, XY, YX, and YY components\r\n```\r\n\r\nEvaluating the new QC ratio post-restoration confirms the effectiveness of our \r\nrecovery efforts:\r\n\r\n```python\r\nr, = wx.qc(Z)\r\nr\r\nOut[13]: 1.0\r\n```\r\n\r\nAs observed, the tensor restoration achieves a 100% success rate across all stations, \r\nsignificantly improving upon the initial analysis. To visualize this enhancement in \r\nconfidence levels:\r\n\r\n```python\r\nplot_confidence_in(Z)\r\n```\r\n\r\nFor further exploration on EM tensor restoration, phase tensor analysis, strike plotting, data filtering, and more, users are encouraged to visit the following links for detailed examples:\r\n- [EM Tensor Restoring](https://watex.readthedocs.io/en/latest/glr_examples/applications/plot_tensor_restoring.html#sphx-glr-glr-examples-applications-plot-tensor-restoring-py)\r\n- [Skewness Analysis Plots](https://watex.readthedocs.io/en/latest/glr_examples/methods/plot_phase_tensors.html#sphx-glr-glr-examples-methods-plot-phase-tensors-py)\r\n- [Strike Plot](https://watex.readthedocs.io/en/latest/glr_examples/utils/plot_strike.html#sphx-glr-glr-examples-utils-plot-strike-py)\r\n- [Filtering Data](https://watex.readthedocs.io/en/latest/methods.html#filtering-tensors-ama-flma-tma)\r\n\r\n\r\n## Citations\r\n\r\nShould you find the [WATex software](https://doi.org/10.1016/j.softx.2023.101367) beneficial \r\nfor your research or any published work, we kindly ask you to cite the following article:\r\n\r\n\u003e Kouadio, K.L., Liu, J., Liu, R., 2023. watex: machine learning research in water exploration. SoftwareX, 101367(2023). [https://doi.org/10.1016/j.softx.2023.101367](https://doi.org/10.1016/j.softx.2023.101367)\r\n\r\nIn publications that mention *WATex*, acknowledging [scikit-learn](https://scikit-learn.org/stable/about.html#citing-scikit-learn) may also be relevant due to its integral role in the software's development.\r\n\r\nFor additional insights and examples, refer to our compilation of [case history papers](https://watex.readthedocs.io/en/latest/citing.html) that utilized *WATex*.\r\n\r\n## Contributions\r\n\r\nThe development and success of *WATex* have been made possible through contributions from the following \r\ninstitutions:\r\n\r\n1. Department of Geophysics, School of Geosciences \u0026 Info-physics, [Central South University](https://en.csu.edu.cn/), China.\r\n2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hunan, China.\r\n3. Laboratoire de Geologie, Ressources Minerales et Energetiques, UFR des Sciences de la Terre et des Ressources Minières, [Université Félix Houphouët-Boigny](https://www.univ-fhb.edu.ci/index.php/ufr-strm/), Côte d'Ivoire.\r\n\r\nFor inquiries, suggestions, or contributions, please reach out to the developers:\r\n* [1,2,3] [_LKouadio_](https://wegeophysics.github.io/) at \u003cetanoyau@gmail.com\u003e \u0026\r\n* [1,2] [_Liu Rong_](https://faculty.csu.edu.cn/liurong1234/en/lwcg/196821/content/51412.htm) at \u003cliurongkaoyan@csu.edu.cn\u003e\r\n\r\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.7744732","https://doi.org/10.1016/j.softx.2023.101367"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["optimize","archiving","measur","transforms","animals","conversion","generic","observation","projection","compose"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/300839","html_url":"https://ost.ecosyste.ms/projects/300839"}